Method for predicting corrosion amount of steel material, system for predicting corrosion amount of steel material, program for predicting corrosion amount of steel material, and method for proposing steel material

Abstract

This invention relates to a method for predicting the corrosion rate of steel, a system for predicting the corrosion rate of steel, a procedure for predicting the corrosion rate of steel, and a method for predicting the corrosion rate of steel. The method for predicting the corrosion rate of steel includes the following corrosion rate estimation steps: based on meteorological observations W at a predetermined location M where the steel is to be used. M According to each past observation time t Mi Calculate the corrosion index Q(t) of the steel. Mi W M ), for the observation time t M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis is performed on time series data to predict future evaluation periods t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi ), calculate in advance the measured corrosion amount obtained from the corrosion test at test site Z, and the meteorological observation value W based on test site Z. Z According to each observation time t during the corrosion test... Zi The obtained corrosion index Q(t) Zi W Z The relationship between the maximum values ​​of ) is expressed, and the previously predicted maximum value Q of the corrosion index is imported into this relationship. max (t Fi This allows us to obtain an estimated value for the amount of corrosion of the steel during the evaluation period.

Description

Technical Field

[0001] This invention relates to a method for predicting the corrosion amount of steel, a system for predicting the corrosion amount of steel, a procedure for predicting the corrosion amount of steel, and a method for proposing corrosion of steel.

[0002] This application is based on and claims priority to Japan Patent Application No. 2021-59457 filed on March 31, 2021, the contents of which are incorporated herein by reference. Background Technology

[0003] Construction steel is used as a raw material for building exterior walls, columns, beams, etc. Those who research or decide to purchase construction steel often want to predict the future corrosion levels of the steel under long-term outdoor use.

[0004] Patent Document 1 describes a corrosion prediction method for weather-resistant steel, which includes a step of using a computer to calculate the predicted corrosion amount of weather-resistant steel by using extrinsic corrosion information, including annual humidity time, annual average wind speed, annual average temperature, air salinity, and sulfur oxide content, as well as intrinsic corrosion information related to the composition of the weather-resistant steel at a predetermined location where the weather-resistant steel will be used.

[0005] However, steel corrosion can sometimes be significantly affected by sudden climate changes. For example, in locations near the coast, when typhoons bring wind speeds significantly higher than usual, or when atmospheric pressure configurations cause wind speeds to increase dramatically in a short period, large amounts of salt can adhere to the steel used in construction within a relatively short time. Consequently, the amount of corrosion can sometimes increase significantly.

[0006] In Patent Document 1, the predicted corrosion amount is calculated based on annual average values ​​such as annual average wind speed and annual average temperature. However, due to data construction limitations, this prediction based on annual average values ​​does not take into account sudden short-term climate changes that could significantly impact steel corrosion. Therefore, there is room for improvement in the accuracy of corrosion prediction compared to previous methods.

[0007] Existing technical documents

[0008] Patent documents

[0009] Patent Document 1: International Publication No. 2003 / 006957 Summary of the Invention

[0010] The problem that the invention aims to solve

[0011] The objective of this invention is to provide a method, system, and program for predicting the corrosion amount of steel that can improve the accuracy of steel corrosion prediction. Furthermore, the objective of this invention is to provide a proposed method for predicting the corrosion amount of steel using the steel corrosion amount prediction method.

[0012] Methods for solving problems

[0013] To address the aforementioned issues, the present invention employs the following configuration.

[0014] [1] A method for predicting the corrosion amount of steel is a method for predicting the corrosion amount of steel, which is used as an index to evaluate the corrosion amount of steel when it is exposed to the outdoors. The method uses the corrosion amount of steel at each observation time t at the observation location X. Xi Meteorological observations W of wind speed, wind direction, and rainfall (where i is an integer from 1 to n). X The corrosion index Q(t) of the steel was calculated. Xi W X The feature is that it has the following characteristics:

[0015] The extreme value prediction step is based on the meteorological observation value W of the predetermined location M for steel at the observation location X. M According to each past observation time t Mi Calculate the corrosion index Q(t) of the steel. Mi W M ), for the observation time t M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis is performed on the time series data to predict future evaluation periods t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi To speculate; and

[0016] The corrosion estimation step involves pre-calculating the measured corrosion amount obtained from the corrosion test of the steel at test site Z, and comparing it with the meteorological observation value W based on the observation site X being the test site Z. Z And according to each observation time t during the corrosion test. Zi The obtained corrosion index Q(t) of the steel Zi W Z The relationship between the maximum values ​​of ) is expressed, and the maximum value Q of the corrosivity index predicted in the extreme value prediction step is input into this relationship. max (t Fi This allows us to obtain an estimated value of the amount of corrosion of the steel during the evaluation period.

[0017] Alternatively, the relationship can also be the measured corrosion amount obtained from the corrosion test of the steel and the corrosion index Q(t) of the steel. Zi W Z The relationship between the logarithms of the maximum values ​​of ).

[0018] [2] The method for predicting the corrosion amount of steel as described in [1], wherein,

[0019] The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for )

[0020] The calculation steps include:

[0021] The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall;

[0022] The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X );

[0023] The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as

[0024] The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3A). Xi W X ),

[0025] Q1(t Xi W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0026] Q2(t Xi W X )={Q2(t Xi-1 W X )+Q1(t Xi W X )·s(tXi )·(t Xi -t Xi-1 )}·c(t Xi …(2)

[0027] Q(t Xi W X )=Q2(t Xi W X )·p(t Xi …(3A)

[0028] Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°.

[0029] In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1,

[0030] In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1,

[0031] In equation (3A), p(t) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t Xi Let p(t) be the interval during the daytime (from sunrise to sunset).Xi ) = 1.

[0032] [3] The method for predicting the corrosion amount of steel as described in technical solution [1], wherein,

[0033] The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for )

[0034] The calculation steps include:

[0035] The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall;

[0036] The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X );

[0037] The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as

[0038] The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3B). Xi W X ),

[0039] Q1(t Xi W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0040] Q2(t Xi W X )={Q2(t Xi-1 W X )+Q1(t Xi W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi …(2)

[0041] Q(t Xi W X ) = 10 0.04Tb ·Q2(t Xi W X )·p(t Xi …(3B)

[0042] Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°.

[0043] In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1,

[0044] In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1,

[0045] In equation (3B), Tb is any temperature within the range of Temp (°C) to (Temp+2)°C, where the sunrise temperature is set to Temp (°C).

[0046] p(t) in equation (3B) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t XiLet p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

[0047] [4] The method for predicting the corrosion amount of steel as described in technical solution [1], wherein,

[0048] The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for )

[0049] The calculation steps include:

[0050] The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall;

[0051] The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X );

[0052] The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as

[0053] The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3C). Xi W X ),

[0054] Q1(t Xi W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0055] Q2(t Xi W X )={Q2(t Xi-1 W X )+Q1(t Xi W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi…(2)

[0056] Q(t Xi W X ) = 10 0.04Tc ·Q2(t Xi W X )·p(t Xi …(3C)

[0057] Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°.

[0058] In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1,

[0059] In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi The amount of moisture adhering to the surface of the steel, ws(t) Xi If ) is 0, then set it as s(t) Xi ) = 0, in the ws(t) Xi If the value exceeds 0, set it to s(t). Xi ) = 1, the ws(t) Xi ) is from the observation time t Xi The estimated value is obtained by subtracting the evaporation rate from the amount of moisture adhering to the surface due to rainfall and condensation. It is calculated based on the steel's surface temperature, air temperature (°C), air pressure (hPa), wind speed at the steel surface (m / s), and relative humidity (%).

[0060] In equation (3C), Tc is the time relative to the observation time t. Xi The earliest time and the amount of moisture adhering to the steel surface ws(t) Xi The temperature (°C) at the moment when it becomes 0.

[0061] p(t) in equation (3C)Xi ) is the corrosion detection coefficient, in the ws(t) Xi If the value exceeds 0, set it to p(t). Xi ) = 0, in the ws(t) Xi If the value is less than or equal to 0, then p(t) is set to p(t). Xi ) = 1.

[0062] [5] The method for predicting the corrosion amount of steel as described in any one of the technical solutions [1] to [4] is characterized in that the steel is made of stainless steel.

[0063] [6] A corrosion prediction system for steel is a computer-based system for predicting the corrosion of steel. It serves as an indicator for evaluating the corrosion of steel exposed to outdoor conditions, using data from each observation time t at observation location X. Xi Meteorological observations W of wind speed, wind direction, and rainfall (where i is an integer from 1 to n). X The corrosion index Q(t) of the steel was calculated. Xi W X The feature is that it has the following characteristics:

[0064] The extreme value prediction department is based on the meteorological observation value W of the predetermined location M for the utilization of steel at the observation location X. M According to each past observation time t Mi Calculate the corrosion index Q(t) of the steel. Mi W M ), for the observation time t M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis is performed on the time series data to predict future evaluation periods t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi To speculate; and

[0065] The corrosion estimation unit pre-calculates the measured corrosion amount obtained from the corrosion test of the steel at test site Z, and the meteorological observation value W based on the observation site X being the test site Z. Z And according to each observation time t during the corrosion test. Zi The obtained corrosion index Q(t) of the steel Zi W Z The relationship between the maximum values ​​of ) is expressed, and the maximum value Q of the corrosivity index predicted in the extreme value prediction section is input into this relationship. max (t FiThis allows us to obtain an estimated value of the amount of corrosion of the steel during the evaluation period.

[0066] Alternatively, the relationship can also be the measured corrosion amount obtained from the corrosion test of the steel, and the corrosion index Q(t) of the steel. Zi W Z The relationship between the logarithms of the maximum values ​​of ).

[0067] [7] The corrosion prediction system for steel as described in technical solution [6], wherein,

[0068] The extreme value estimation unit and the corrosion amount estimation unit respectively include the ability to calculate the corrosion index Q(t) Xi W X The computing department of )

[0069] The computing unit includes:

[0070] The first processing unit acquires at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall;

[0071] The second processing unit processes data according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X );

[0072] The third processing unit, according to each observation time t Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as

[0073] The fourth processing unit, according to each observation time t Xi The corrosion index Q(t) is calculated using the following formula (3A). Xi W X ),

[0074] Q1(t Xi W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0075] Q2(t Xi W X )={Q2(t Xi-1 W X )+Q1(tXi W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi …(2)

[0076] Q(t Xi W X )=Q2(t Xi W X )·p(t Xi …(3A)

[0077] Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°.

[0078] In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1,

[0079] In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1,

[0080] In equation (3A), p(t) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t XiLet p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

[0081] [8] The corrosion prediction system for steel as described in technical solution [6], wherein,

[0082] The extreme value estimation unit and the corrosion amount estimation unit respectively include the ability to calculate the corrosion index Q(t) Xi W X The computing department of )

[0083] The computing unit includes:

[0084] The first processing unit acquires at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall;

[0085] The second processing unit processes data according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X );

[0086] The third processing unit, according to each observation time t Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as

[0087] The fourth processing unit, according to each observation time t Xi The corrosion index Q(t) is calculated using the following formula (3B). Xi W X ),

[0088] Q1(t Xi W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0089] Q2(t Xi W X )={Q2(t Xi-1 W X )+Q1(t Xi W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi…(2)

[0090] Q(t Xi W X ) = 10 0.04Tb ·Q2(t Xi W X )·p(t Xi …(3B)

[0091] Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°.

[0092] In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1,

[0093] In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1,

[0094] In equation (3B), Tb is any temperature within the range of Temp (°C) to (Temp+2)°C, where the sunrise temperature is set to Temp (°C).

[0095] p(t) in equation (3B) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~tXi Let p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

[0096] [9] The corrosion prediction system for steel as described in technical solution [6], wherein,

[0097] The extreme value estimation unit and the corrosion amount estimation unit respectively include the ability to calculate the corrosion index Q(t) Xi W X The computing department of )

[0098] The computing unit includes:

[0099] The first processing unit acquires at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall;

[0100] The second processing unit processes data according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X );

[0101] The third processing unit, according to each observation time t Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as

[0102] The fourth processing unit, according to each observation time t Xi The corrosion index Q(t) is calculated using the following formula (3C). Xi W X ),

[0103] Q1(t Xi W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0104] Q2(t Xi W X )={Q2(t Xi-1 W X )+Q1(t Xi W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(tXi …(2)

[0105] Q(t Xi W X ) = 10 0.04Tc ·Q2(t Xi W X )·p(t Xi …(3C)

[0106] Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°.

[0107] In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1,

[0108] In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi The amount of moisture adhering to the surface of the steel, ws(t) Xi If ) is 0, then set it as s(t) Xi ) = 0, in the ws(t) Xi If the value exceeds 0, set it to s(t). Xi ) = 1, the ws(t) Xi ) is from the observation time t Xi The estimated value is obtained by subtracting the evaporation rate from the amount of moisture adhering to the surface due to rainfall and condensation. It is calculated based on the steel's surface temperature, air temperature (°C), air pressure (hPa), wind speed at the steel surface (m / s), and relative humidity (%).

[0109] In equation (3C), Tc is the time relative to the observation time t. Xi The earliest time and the amount of moisture adhering to the steel surface ws(t) Xi The temperature (°C) at the moment when it becomes 0.

[0110] p(t) in equation (3C) Xi ) is the corrosion detection coefficient, in the ws(t) Xi If the value exceeds 0, set it to p(t). Xi ) = 0, in the ws(t) Xi If the value is less than or equal to 0, then p(t) is set to p(t). Xi ) = 1.

[0111]

[10] The corrosion prediction system for steel as described in any one of technical solutions [6] to [9] is characterized in that the steel is made of stainless steel.

[0112]

[11] A corrosion prediction program for steel is a steel corrosion prediction program used by an electronic computer, which serves as an indicator for evaluating the corrosion of steel exposed to the outdoors, using data based on each observation time t at observation location X. Xi Meteorological observations W of wind speed, wind direction, and rainfall (where i is an integer from 1 to n). X The corrosion index Q(t) of the steel was calculated. Xi W X The feature is that it has the following characteristics:

[0113] The extreme value prediction step is based on the meteorological observation value W of the predetermined location M for steel at the observation location X. M According to each past observation time t Mi Calculate the corrosion index Q(t) of the steel. Mi W M ), for the observation time t M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis is performed on the time series data to predict future evaluation periods t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi To speculate; and

[0114] The corrosion estimation step involves pre-calculating the measured corrosion amount obtained from the corrosion test of the steel at test site Z, and comparing it with the meteorological observation value W based on the observation site X being the test site Z. Z And according to each observation time t during the corrosion test. Zi The obtained corrosion index Q(t) of the steel Zi W Z The relationship between the maximum values ​​of ) is expressed, and the maximum value Q of the corrosivity index predicted in the extreme value prediction step is input into this relationship. max (t FiThis allows us to obtain an estimated value of the amount of corrosion of the steel during the evaluation period.

[0115] Alternatively, the relationship can also be the measured corrosion amount obtained from the corrosion test of the steel, and the corrosion index Q(t) of the steel. Zi W Z The relationship between the logarithms of the maximum values ​​of ).

[0116]

[12] The corrosion prediction procedure for steel as described in technical solution

[11] , wherein,

[0117] The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for )

[0118] The calculation steps include:

[0119] The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall;

[0120] The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X );

[0121] The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as

[0122] The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3A). Xi W X ),

[0123] Q1(t Xi W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0124] Q2(t Xi W X )={Q2(t Xi-1 W X )+Q1(tXi W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi …(2)

[0125] Q(t Xi W X )=Q2(t Xi W X )·p(t Xi …(3A)

[0126] Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°.

[0127] In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1,

[0128] In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1,

[0129] In equation (3A), p(t) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t XiLet p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

[0130]

[13] The corrosion prediction program for steel as described in technical solution

[11] , wherein,

[0131] The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for )

[0132] The calculation steps include:

[0133] The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall;

[0134] The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X );

[0135] The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as

[0136] The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3B). Xi W X ),

[0137] Q1(t Xi W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0138] Q2(t Xi W X )={Q2(t Xi-1 W X )+Q1(t Xi W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi…(2)

[0139] Q(t Xi W X ) = 10 0.04Tb ·Q2(t Xi W X )·p(t Xi …(3B)

[0140] Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°.

[0141] In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1,

[0142] In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1,

[0143] In equation (3B), Tb is any temperature within the range of Temp (°C) to (Temp+2)°C, where the sunrise temperature is set to Temp (°C).

[0144] p(t) in equation (3B) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~tXi Let p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

[0145]

[14] The corrosion prediction procedure for steel as described in technical solution

[11] , wherein,

[0146] The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for )

[0147] The calculation steps include:

[0148] The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall;

[0149] The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X );

[0150] The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as

[0151] The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3C). Xi W X ),

[0152] Q1(t Xi W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0153] Q2(t Xi W X )={Q2(t Xi-1 W X )+Q1(t Xi W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(tXi …(2)

[0154] Q(t Xi W X ) = 10 0.04Tc ·Q2(t Xi W X )·p(t Xi …(3C)

[0155] Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°.

[0156] In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1,

[0157] In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi The amount of moisture adhering to the surface of the steel, ws(t) Xi If ) is 0, then set it as s(t) Xi ) = 0, in the ws(t) Xi If the value exceeds 0, set it to s(t). Xi ) = 1, the ws(t) Xi ) is from the observation time t Xi The estimated value is obtained by subtracting the evaporation rate from the amount of moisture adhering to the surface due to rainfall and condensation. It is calculated based on the steel's surface temperature, air temperature (°C), air pressure (hPa), wind speed at the steel surface (m / s), and relative humidity (%).

[0158] In equation (3C), Tc is the time relative to the observation time t. Xi The earliest time and the amount of moisture adhering to the steel surface ws(t) Xi The temperature (°C) at the moment when it becomes 0.

[0159] p(t) in equation (3C) Xi ) is the corrosion detection coefficient, in the ws(t) Xi If the value exceeds 0, set it to p(t). Xi ) = 0, in the ws(t) Xi If the value is less than or equal to 0, then p(t) is set to p(t). Xi ) = 1.

[0160]

[15] The corrosion prediction program for steel as described in any one of technical solutions

[11] to

[14] is characterized in that the steel is made of stainless steel.

[0161]

[16] A proposed method for producing steel, comprising:

[0162] The prediction process, using the method for predicting the corrosion amount of steel as described in any one of technical solutions [1] to [5], predicts the corrosion amount of steel at a predetermined location M in the future; and

[0163] The process by which the sales manager presents the customer with the predicted value of corrosion obtained through the prediction process, or an image of the steel surface corresponding to the predicted value of corrosion.

[0164]

[17] A proposed method for producing steel, comprising:

[0165] The prediction process, using the method for predicting the corrosion amount of steel as described in any one of technical solutions [1] to [5], predicts the corrosion amount of various types of steel at a predetermined location M in the future; and

[0166] The sales manager presents the customer with a predicted value of the corrosion amount of each type of steel obtained through the prediction process, or an image of the steel surface corresponding to the predicted corrosion amount.

[0167]

[18] The proposed method for steel as described in technical solution

[16] or

[17] , wherein the predicted value of the corrosion amount is at least a rating to the first decimal place.

[0168]

[19] The proposed method for steel as described in technical solution

[16] or

[17] , wherein the image of the steel surface corresponding to the predicted value of the corrosion amount is at least an image corresponding to the rating to the first decimal place.

[0169] The effects of the invention

[0170] In the steel corrosion prediction method, steel corrosion prediction system, and steel corrosion prediction program of the present invention, the corrosion amount of steel is predicted at each past observation time t at the predetermined utilization location M of the steel. Mi Corrosion index Q M (t Mi WM Extreme value statistical analysis was performed on the time series data to predict the future evaluation period t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi Next, the measured corrosion amount of the steel, pre-calculated based on the corrosion test results, is compared with the steel's corrosion index Q(t). Zi W Z In the relationship between ), the maximum value Q of the corrosivity index is introduced. max (t Fi This invention predicts the future corrosion rate of steel in such a way that it improves the accuracy of corrosion prediction compared to methods that predict corrosion based on historical annual averages.

[0171] Furthermore, the steel corrosion prediction method, system, and program of the present invention predict the future corrosion amount of steel at a predetermined location M based on the corrosion test results of the steel at test location Z. In the present invention, as long as the measured corrosion amount and corrosion index Q(t) of the steel at test location Z can be obtained... Zi W Z The correspondence between them and the meteorological observation value W based on the predetermined location M M The obtained corrosion index Q(t) Mi W M The time series data is sufficient. Therefore, the present invention has excellent versatility.

[0172] Furthermore, according to the steel corrosion prediction method, steel corrosion prediction system, and steel corrosion prediction program of the present invention, the primary corrosion index Q1(t) is sequentially calculated. Xi W X ) and secondary corrosion index Q2(t Xi W X Furthermore, according to the secondary corrosion index Q2(t) Xi W X ) Calculate the corrosion index Q(t) Xi W X Here, the primary corrosion index Q1(t) Xi W X () is an indicator of the amount of salt adhering to steel, based on wind speed u(t) Xi ) and wind direction θs, θw(t Xi The calculation is based on the secondary corrosion index Q2(t). Xi W X The calculation is as follows: The observation time t... Xi The immediate observation time is the observation time t. Xi-1The secondary corrosion index Q2(t) Xi-1 W X Add the observation time t Xi The primary corrosion index Q1(t) Xi W X Furthermore, the extent to which salt, the primary cause of corrosion, is washed away by rainfall is used as the corrosion disappearance coefficient c(t). Xi The data is imported, and the increased adhesion of salt at night, where condensation easily forms on the steel surface, is used as the corrosion accumulation coefficient s(t). Xi Import. Furthermore, the corrosivity index Q(t) Xi W X The following calculation is performed: The phenomenon of salty condensation adhering to the surface at night drying due to daytime sunlight, leading to corrosion, is analyzed using the corrosion coefficient p(t). Xi The corrosion detection coefficient p(t) is then quantified and converted into numerical values. Xi ) and secondary corrosion index Q2(t Xi W X Multiply by t. From this, we can obtain the corrosion index Q(t), which reflects the influence of meteorological conditions on the amount of salt adhering to the steel. Xi W X Its use can improve the accuracy of corrosion prediction.

[0173] The proposed method for the steel of this invention involves predicting the future corrosion amount of the steel at a predetermined location M using the steel corrosion prediction method of this invention, and the sales manager then provides the customer with the predicted corrosion amount. Thus, this invention addresses the desire of customers who wish to know the predicted future corrosion amount of steel used in a long-term outdoor environment. Furthermore, this invention provides highly accurate corrosion prediction values, thereby enabling the recommendation of suitable steel to customers.

[0174] Furthermore, according to the proposed method for steel according to the present invention, the corrosion amount of steel at a predetermined location M is predicted for various types of steel using the steel corrosion prediction method of the present invention. The sales manager then provides the customer with the predicted corrosion amount for each type of steel. Thus, the present invention can respond to the expectations of customers who want to know the predicted corrosion amount of steel under long-term outdoor use. In addition, the present invention can provide highly accurate corrosion amount predictions for each type of steel, thus allowing for the selection and recommendation of appropriate steel to the customer. Attached Figure Description

[0175] Figure 1 This is a schematic diagram illustrating the steel corrosion prediction system according to an embodiment of the present invention.

[0176] Figure 2 This is a schematic diagram illustrating the corrosion prediction procedure for steel according to an embodiment of the present invention.

[0177] Figure 3 This represents the corrosivity index Q(t) Mi W M A chart illustrating an example of the changes over time.

[0178] Figure 4A It represents the observation time t. M1 ~t Mn Corrosion index Q(t) Mi W M A chart of an example of time series data.

[0179] Figure 4B It indicates the future evaluation period t F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi (Charts)

[0180] Figure 5 This represents the maximum value Q for SUS304. Rmax A graph showing the relationship between the logarithm of the chromatic atom and the measured corrosion amount (RN).

[0181] Figure 6 This is a graph representing an example of the output of the corrosion estimation method of this embodiment.

[0182] Figure 7 This represents the maximum value Q for SUS304. Rmax A graph showing the relationship between the logarithm of the chromatic atom and the measured corrosion amount (RN).

[0183] Figure 8 This represents the maximum value Q after temperature correction for SUS304. Rmax A graph showing the relationship between the logarithm of the chromatic atom and the measured corrosion amount (RN). Detailed Implementation

[0184] Steel placed outdoors is susceptible to corrosion due to factors such as wind, which cause corrosion-promoting substances like salt to adhere to its surface, leading to gradual corrosion. Buildings, bridges, towers, and other civil structures require long-term weather resistance in outdoor environments. Therefore, the steel used in these buildings and structures should ideally have its future corrosion rate predictable. Existing technology includes methods for predicting corrosion rates based on annual humidity duration, average annual wind speed, and average annual temperature.

[0185] However, steel corrosion can sometimes develop rapidly due to the adhesion of salt, and at other times it can be significantly affected by sudden climate changes. Therefore, previous forecasts based on annual averages did not account for short-term, sudden climate changes that could have a significant impact on steel corrosion due to data construction limitations. Consequently, there is room for improvement in the accuracy of corrosion forecasts compared to previous methods.

[0186] In other words, the corrosion rate of steel placed outdoors is affected by the amount of salt adhering to the steel. Salt is primarily transported to the steel by winds blowing from the coast, so it was previously believed that the corrosion rate could be predicted to some extent based on monthly and annual average wind speeds and wind direction shifts. However, when steel is actually placed outdoors and corrosion tests are conducted, sometimes considering only monthly and annual average wind speeds and wind direction is insufficient to determine the corrosion rate.

[0187] Furthermore, stainless steel, compared to ordinary steel, sometimes corrodes more rapidly due to salt adhesion. Moreover, stainless steel's superior design flexibility means it is often used unpainted for building exteriors, making it more susceptible to salt damage compared to ordinary steel.

[0188] Therefore, it is desirable to develop a method that can predict the amount of corrosion in steel with high accuracy.

[0189] Therefore, the inventors conducted in-depth research and discovered meteorological conditions under which corrosion of steel placed outdoors is prone to develop, specifically those without rainfall from night to day. Specifically, they discovered that when condensation forms on the steel surface as nighttime temperatures decrease, salt transported by sea breezes blowing from the coast dissolves in the condensation. This salt-containing condensation then evaporates due to daytime sunlight, condensing the salt on the steel surface and thus making it more susceptible to corrosion. In particular, they determined that strong winds blowing from the direction closest to the coast, influenced by the approach of typhoons, hurricanes, cyclones, etc., and their pressure configuration, easily cause a greater amount of salt to adhere to the steel surface. Furthermore, they determined that as long as there is no rainfall, salt continues to accumulate on the steel surface at night, making corrosion more likely. On the other hand, they determined that in the event of rainfall, rainwater washes away the salt from the steel surface, thus hindering corrosion development. Moreover, they discovered a method that, by considering these meteorological conditions and using data based on each observation time t... Xi Meteorological observations of wind speed, wind direction, and rainfall W X The calculated corrosion index Q(t) Xi W X It can accurately predict the future corrosion rate of steel.

[0190] Furthermore, regarding the presence or absence of sunlight, in this invention, it is determined according to the observation time t. Xi The distinction is made between daytime and nighttime. To determine whether there is sunshine, the duration of sunshine can also be included in the meteorological observation value W. X Additionally, to determine whether there is sunshine, the sunrise and sunset times can be calculated based on the latitude, longitude, and elevation of the observation location X, and the observation time t can be determined. Xi Which includes daytime or nighttime? Details will follow.

[0191] Furthermore, to accurately predict the corrosion amount of steel, it is necessary to refer to the results of corrosion tests on the steel. Conventional prediction methods are limited to predicting the corrosion amount at the location where the corrosion test was conducted. In the corrosion amount prediction method of this invention, even if the test location Z of the corrosion test is different from the predetermined utilization location M of the steel, the corrosion amount can be predicted using the corrosion index Q(t) discovered by the inventors. Xi W X This allows for the accurate prediction of future corrosion at a predetermined location M based on the results of corrosion tests at test site Z.

[0192] The embodiments of the present invention will be described below.

[0193] In this embodiment, the steel used for predicting corrosion is not particularly limited and can be made of ordinary steel (carbon steel), alloy steel, special steel, etc. Alternatively, it can be electroplated steel that has undergone electroplating. The type of electroplating is not particularly limited, as long as it is a common electroplating process for steel. Furthermore, the steel can also be made of stainless steel.

[0194] Furthermore, the steel used in this embodiment is not limited to building materials and can be well used for predicting the corrosion of steel used outdoors.

[0195] (Steel corrosion prediction system and steel corrosion prediction program)

[0196] First, the corrosion prediction system and the corrosion prediction program for steel in this embodiment will be explained.

[0197] like Figure 1 As shown, the corrosion prediction system of this embodiment includes an extreme value prediction unit and a corrosion prediction unit. These extreme value prediction units and corrosion prediction units are implemented, for example, as functions of the central processing unit of an electronic computer.

[0198] The extreme value prediction department has the following functions: based on the meteorological observation value W at the predetermined location M for steel utilization, where the observation location X is the steel location. M According to each past observation time t MiCalculate the corrosion index Q(t) of the steel. Mi W M ), for the observation time t M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis was performed on the time series data to predict the future evaluation period t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi ).

[0199] In addition, the corrosion estimation unit has the following functions: to compare the measured corrosion amount obtained from the corrosion test of the steel at test site Z with the meteorological observation value W based on the observation site X, which is test site Z. Z According to each observation time t during the corrosion test... Zi The obtained corrosion index Q(t) of the steel Zi W Z The relationship between the maximum values ​​of ) is used to import the maximum value Q of the corrosivity index predicted in the extreme value prediction section. max (t Fi This allows us to obtain an estimated value for the amount of steel corrosion during the estimation period. The pre-determined formula is then imported into the corrosion estimation section.

[0200] Alternatively, the relationship can also be based on the measured corrosion amount obtained from the corrosion test of the steel and the corrosion index Q(t) of the steel. Zi W Z The relationship between the logarithms of the maximum values ​​of ).

[0201] In addition, the extreme value prediction section and the corrosion rate prediction section can also be equipped with functions for calculating the corrosion index Q(t). Mi W M ), Q(t) Zi W Z The computing department of ).

[0202] The computational unit can also be equipped with: at least the ability to obtain each observation time t at observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi ) and the first processing unit for rainfall data, according to each observation time t Xi Calculate the corrosion index Q1(t) Xi W X The second processing unit, according to each observation time t Xi Calculate the secondary corrosion index Q2(t) Xi W XThe third processing unit and according to each observation time t Xi Calculate the corrosion index Q(t) Xi W X The fourth processing department.

[0203] In addition, the corrosion index Q(t) is calculated in the extreme value prediction section. Mi W M In the calculation unit under the following circumstances, location X is set as the predetermined location for the utilization of steel, M, W. X Let W be the name of the project. M t Xi Let t be the value of t. Mi u(t) Xi Let u(t) be the value of u(t). Mi ), θw(t Xi Let ) be θw(t Mi Additionally, c(t) Xi Let c(t) be the value of c(t). Mi ), s(t) Xi Let ) be s(t) Mi ), p(t) Xi Let p(t) be the base value of p(t). Mi ).

[0204] In addition, the corrosion index Q(t) is calculated in the corrosion estimation section. Zi W Z In the calculation unit under the condition that location X is set as the test location Z and W for the steel, location X is set as the test location Z and W. X Let W be the name of the project. Z t Xi Let t be the value of t. Zi u(t) Xi Let u(t) be the value of u(t). Zi ), θw(t Xi Let ) be θw(t Zi Additionally, c(t) Xi Let c(t) be the value of c(t). Zi ), s(t) Xi Let ) be s(t) Zi ), p(t) Xi Let p(t) be the base value of p(t). Zi ).

[0205] The corrosion prediction system of this embodiment generates and outputs a predicted value of the corrosion amount of steel through an extreme value prediction unit and a corrosion amount prediction unit.

[0206] Next, as Figure 2As shown, the corrosion prediction program of this embodiment includes an extreme value prediction step and a corrosion prediction step. The corrosion prediction program of this embodiment is executed in a computer. The computer equipped with the corrosion prediction program sequentially executes the extreme value prediction step and the corrosion prediction step, thereby outputting a predicted value of the corrosion amount of the steel. Furthermore, the corrosion prediction program can also utilize a program for the corrosion prediction system described above.

[0207] The extreme value prediction step of the corrosion prediction program is as follows: based on the meteorological observation value W at the predetermined location M for steel utilization, the observation location X is the observation location X. M According to each past observation time t Mi Calculate the corrosion index Q(t) of the steel. Mi W M Next, regarding the observation time t... M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis was performed on the time series data to predict the future evaluation period t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi The extreme value prediction step can also be performed in... Figure 1 The extreme value prediction section is shown.

[0208] In addition, the corrosion prediction step of the corrosion prediction program is to compare the measured corrosion amount obtained from the corrosion test of the steel at test site Z with the meteorological observation value W based on the observation site X as test site Z. Z According to each observation time t during the corrosion test... Zi The obtained corrosion index Q(t) of the steel Zi W Z The relationship between the maximum values ​​of ) is used to import the maximum value Q of the corrosivity index predicted in the extreme value prediction step. max (t Fi This allows us to obtain an estimated value for the corrosion amount of the steel during the estimation period. The relationship can be calculated in advance. Alternatively, the relationship can be based on the measured corrosion amount obtained from the steel's corrosion test and the steel's corrosion index Q(t). Zi W Z The relationship between the maximum value and the logarithm of ) is expressed as follows. The corrosion estimation step can also be performed in Figure 1 The corrosion amount is estimated in the section shown.

[0209] In addition, the extreme value prediction step and the corrosion rate prediction step can also respectively have functions for calculating the corrosion index Q(t). Mi W M ), Q(t)Zi W Z The calculation steps for ).

[0210] The calculation process can also include a first step, a second step, a third step, and a fourth step. The first step involves obtaining at least one observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi And rainfall. The second step is to calculate the rainfall at each observation time t. Xi Calculate the corrosion index Q1(t) Xi W X The third step is to proceed according to each observation time t. Xi Calculate the secondary corrosion index Q2(t) Xi W X The fourth step is to proceed according to each observation time t. Xi Calculate the corrosion index Q(t) Xi W X ).

[0211] In addition, the corrosion index Q(t) is calculated in the extreme value prediction step. Mi W M In the calculation steps under the condition that location X is set as the predetermined location M and W for the utilization of steel, location X is set as the predetermined location M and W for the utilization of steel. X Let W be the name of the project. M t Xi Let t be the value of t. Mi u(t) Xi Let u(t) be the value of u(t). Mi ), θw(t Xi Let ) be θw(t Mi Additionally, c(t) Xi Let c(t) be the value of c(t). Mi ), s(t) Xi Let ) be s(t) Mi ), p(t) Xi Let p(t) be the base value of p(t). Mi ).

[0212] In addition, the corrosion index Q(t) is calculated in the corrosion estimation step. Zi W Z In the calculation steps under the condition that location X is set as the test location Z and W of the steel, location X is set as the test location Z and W. X Let W be the name of the project. Z t Xi Let t be the value of t. Zi u(t) Xi Let u(t) be the value of u(t). Zi ), θw(t Xi Let ) be θw(t Zi Additionally, c(t) Xi Let c(t) be the value of c(t).Zi ), s(t) Xi Let ) be s(t) Zi ), p(t) Xi Let p(t) be the base value of p(t). Zi ).

[0213] The detailed description of the operation of the extreme value prediction unit, corrosion prediction unit, and calculation unit of the corrosion prediction system of this embodiment, as well as the operation of the extreme value prediction step, corrosion prediction step, and calculation step of the corrosion prediction program, will be described in the description of the corrosion prediction method for steel.

[0214] In addition, the input values ​​of the corrosion prediction system and corrosion prediction program in this embodiment include at least the meteorological observation value W of the test site Z of the corrosion test. Z Meteorological observation value W at the predetermined location M for steel utilization M The measured corrosion amount is obtained through corrosion testing. Additionally, the output value is a predicted value of the future corrosion amount when the steel is exposed to the outdoors at a predetermined location M. Corrosion amounts can be measured using corrosion reduction, corrosion depth, and corrosion appearance rating, i.e., the RN value (RN: rating). For stainless steel, methods for evaluating the RN value include JIS G 0595:2004 (Method for evaluating the degree of surface corrosion in stainless steel).

[0215] (Methods for predicting the corrosion rate of steel)

[0216] Next, the method for predicting the corrosion amount of steel in this embodiment will be explained.

[0217] The method for predicting the corrosion amount of steel in this embodiment is as follows: As an indicator for evaluating the corrosion amount of steel exposed outdoors, the method uses data from each observation time t at observation location X. Xi Meteorological observations W of wind speed, wind direction, and rainfall (where i is an integer from 1 to n). X The corrosion index Q(t) of the steel was calculated. Xi W X ).

[0218] The method for predicting the corrosion of steel in this embodiment includes: estimating the maximum value Q of the corrosion index. max (t Fi The extreme value estimation step; and the corrosion estimation step for estimating the corrosion amount of steel after the estimation period at a predetermined location M.

[0219] The extreme value prediction step of the prediction method is as follows: based on the observation location X being the steel, the meteorological observation value W from the predetermined location M is used. M According to each past observation time t MiCalculate the corrosion index Q(t) of the steel. Mi W M ), for the observation time t M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis was performed on the time series data to predict the future evaluation period t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi ).

[0220] The corrosion estimation step of the prediction method is as follows: [The steps involve] comparing the measured corrosion amount of the steel with the steel's corrosion index Q(t). Zi W Z The relationship between the maximum values ​​of ) is used to import the maximum value Q of the corrosivity index predicted in the extreme value prediction step. max (t Fi This allows us to infer the amount of corrosion in the steel after the period of corrosion.

[0221] It is preferable to pre-calculate the measured corrosion amount of the steel and its corrosion index Q(t). Zi W Z The relationship between the maximum values ​​of ) is based on the measured corrosion amount obtained from the corrosion test of steel at test site Z, and the meteorological observation value W based on observation site X as test site Z. Z According to each observation time t during the corrosion test... Zi The obtained corrosion index Q(t) of the steel Zi W Z The relationship between the maximum value of ) can also be expressed as the relationship between the measured corrosion amount obtained from the corrosion test of the steel and the corrosion index Q(t) of the steel. Zi W Z The relationship between the logarithms of the maximum values ​​of ).

[0222] In addition, the extreme value prediction step and the corrosion rate prediction step can also respectively have functions for calculating the corrosion index Q(t). Mi W M ), Q(t) Zi W Z The calculation steps for ).

[0223] First, in order to explain the evaluation method for the corrosion of steel in this embodiment, the corrosion index Q(t) is... Xi W X (This will be explained.)

[0224] In the method for predicting the corrosion amount of steel in this embodiment, the corrosion amount of steel exposed to the outdoors is evaluated using the data from each observation time t at observation location X as an indicator. Xi Meteorological data W of wind speed, wind direction, and rainfall (where i is an integer from 1 to n). X The corrosion index Q(t) of the steel was calculated. Xi W X ).

[0225] As described above, regarding meteorological conditions that easily lead to corrosion of steel, the inventors discovered meteorological conditions where wind is observed from night to day and no rainfall occurs. Under these meteorological conditions, condensation forms on the steel surface during the night (from sunset to sunrise), and salts carried by the wind dissolve in the condensation. As the condensation evaporates under the subsequent daytime sunlight, the salts are concentrated, resulting in corrosion. Salts accumulate due to the increased number of days without rainfall. On the other hand, when rainfall occurs, the salts disappear from the steel surface. The corrosion index Q(t) of this embodiment... Xi W X () is a value of an index called the salt content on the surface of steel that takes into account this climate change.

[0226] In addition, regarding the corrosion index Q(t) Xi W X The corrosion accumulation factor s(t) used in the calculation is... i ) and the corrosion detection coefficient p(t) Xi ) will be explained. s(t) i ) and p(t Xi This value is related to the wettability of the steel surface and is either 0 or 1. Whether the value is 0 or 1 depends on the observation time t. Xi It depends on whether it's during the day or night.

[0227] That is, at night, the steel surface becomes wet due to condensation. On the other hand, during the day, the steel surface detaches from the wet state as the condensation evaporates. Therefore, in this embodiment, whether the steel surface is wet is indirectly determined based on whether it is daytime or nighttime, and s(t) is determined based on this determination result. i ) and p(t Xi The value of ).

[0228] In addition, in this embodiment, the period from sunset to sunrise is defined as nighttime, and the period from sunrise to sunset is defined as daytime.

[0229] Based on meteorological observation values ​​W X In order to determine whether it is daytime or nighttime, the meteorological observation value W is used. XAnd obtain the observation time t Xi-1 ~t Xi The duration of sunshine between [times]. At observation time t Xi For observations taken at night when there is no daylight, the observation time t can be distinguished based on whether or not an observation is taken. Xi Is it daytime or nighttime? Therefore, it can be determined based on meteorological observations W. X Determines the cumulative corrosion coefficient s(t) i ) and the corrosion detection coefficient p(t) Xi ).

[0230] Furthermore, sunrise and sunset times can be calculated based on the date (year, month, day), the longitude, latitude, and elevation of location X. Therefore, it is also possible to determine whether it is daytime or nighttime based on the calculated sunrise and sunset times, thereby determining the corrosion accumulation coefficient s(t). i ) and the corrosion detection coefficient p(t) Xi ).

[0231] The following section discusses the corrosion index Q(t). Xi W X The calculation method for the primary corrosion index Q1(t) will be explained. In this embodiment, the primary corrosion index Q1(t) is calculated sequentially through the following calculation steps consisting of the first to fourth steps. Xi W X ) and secondary corrosion index Q2(t Xi W X Furthermore, according to the secondary corrosion index Q2(t) Xi W X ) Calculate the corrosivity index Q(t) of this embodiment. Xi W X ).

[0232] Furthermore, in the description of this embodiment, the subscript X in the various parameter symbols refers to the observation location X, and is intended to be replaced by M as the usable location or Z as the test location. Additionally, the observation time t... Xi It is the observation time t X1 t X2 t X3 …t Xn In the generalized representation, any observation time is sometimes recorded as observation time t. Xi Additionally, sometimes the observation time t will be used. Xi ~t Xn The set of elements is recorded as the observation period t. Xi ~t Xn .

[0233] First, in the first step, at least each observation time t at observation location X is obtained.Xi wind speed u(t) Xi ), wind direction θw(t) Xi And rainfall. Alternatively, it can be calculated according to each observation time t. Xi Obtain sunshine duration. Sometimes the wind speed u(t) at observation location X is used. Xi ), wind direction θw(t) Xi Rainfall and sunshine duration are collectively referred to as meteorological observation values ​​W. X .

[0234] The observation location X and meteorological observation value W in the first step X The appropriate stage should be selected based on the forecasting method. That is, in the extreme value prediction step, the observation location X is set as the predetermined utilization location M for the steel, and the meteorological observation value W... X Let's assume that the meteorological observation value W at the predetermined location M is used. M In addition, in the corrosion estimation step, the observation location X is set as the test location Z, and the meteorological observation value W is used. X Let W be the meteorological observation value at test site Z. Z .

[0235] Meteorological observation value W X For example, data measured and published at meteorological stations managed by public meteorological observation agencies can be used. In Japan, for instance, data measured and published at meteorological stations managed by the Japan Meteorological Agency can be used. That is, the meteorological observation value W. X This can be assumed to be data measured at a meteorological station near the observation location X. Additionally, the meteorological observation value W... X It is not limited to the measurement data measured at meteorological observation stations managed by public meteorological observation agencies, but may also use the measurement data measured at temporary meteorological observation facilities set up near the predetermined utilization location M and test location Z of the steel.

[0236] The observation period t of the weather at observation location X Xi ~t Xn The length is not particularly limited, but the period length described below is preferred.

[0237] Using past observation periods t at the predetermined location M Mi ~t Mn The length can be set to any period between 1 year and 50 years, preferably between 5 years and 50 years, and more preferably between 10 years and 50 years. The observation period t Mi ~t Mn The longer the time series, the more data it contains in the extreme value statistical analysis, and the higher the reliability of the analysis. Therefore, the observation period t Mi ~t MnPreferably, the observation period should be as long as possible. In the prediction method of this embodiment, the future corrosion amount is predicted through extreme value statistical analysis based on the salt dispersion pattern. However, the amount of salt dispersion varies on an annual basis; for example, there are years with more sea salt dispersion and years with less dispersion. Furthermore, when the observation period t... Mi ~t Mn Shorter years, especially those containing significant sea salt dispersal, may result in higher predicted corrosion rates in the future. To prevent such prediction bias, the observation period t... Mi ~t Mn The longer one is preferred.

[0238] Additionally, the future evaluation period t at location M will be utilized. F1 ~t Fn The length can be set to any period between 1 month and 100 years, preferably any period between 2 years and 100 years.

[0239] Furthermore, in the corrosion estimation step, the observation period t at test site Z... Zi ~t Zn The length can be set as any period between 1 day and 30 years, preferably any period between 1 month and 10 years, or it can be between 1 month and 5 years.

[0240] Each observation time t X1 …t Xn The interval can be set to an observation time every 10 minutes, every 15 minutes, or every 30 minutes. The observation period t at test site Z... Zi ~t Zn Therefore, in the corrosion estimation step, the corrosion index Q(t) is calculated. Zi W Z When the relationship between the maximum value of Q(t) and the measured corrosion amount is expressed, Q(t) Zi W Z The maximum value of Q(t) can be within a certain range. Zi W Z The maximum value of ) can also be converted into a logarithm.

[0241] Wind speed u(t) Xi ) can be the observation time t Xi The instantaneous wind speed (m / s) can also be the observation time t. Xi-1 ~t Xi The average wind speed (m / s) between the two points. Wind direction θw(t) Xi The observation time t is the time when the azimuth of north is set to 0°. Xi The wind direction and orientation. Observation time t Xi The rainfall and sunshine duration are respectively set as the data from the observation time t.Xi-1 By observation time t Xi The cumulative rainfall and cumulative sunshine duration between observation times. For example, at observation time t... Xi Let the observation time be t, where every 10 minutes. Xi-1 ~t Xi The cumulative rainfall and cumulative sunshine duration during the 10-minute period.

[0242] Next, in the second step, according to each observation time t Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X ).

[0243] Q1(t Xi W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0244] In equation (1) u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi ) is the observation time t Xi θs is the wind direction (°) with north set to 0°, and θs is the azimuth (°) of observation point X relative to the nearest coastline to observation point X with north set to 0°.

[0245] In addition, d in equation (1) is the distance (m) between the coastline closest to the observation point X and the observation point X.

[0246] Primary corrosion index Q1(t) Xi W X () is an indicator of the amount of salt adhering to steel, based on wind speed u(t) Xi ) and θs, θw(t Xi To calculate. In equation (1), “cos|θs-θw(t)” Xi The term "|" is used to calculate the wind speed u(t) at observation location X. Xi The coefficient of the wind speed component from the coastal direction in the equation. Additionally, the amount of salt adhering to the steel has a relationship with the wind speed u(t). Xi The rule of thumb is that (d+1) is proportional to the square of the law of magnitude 2. Furthermore, (d+1) -0.6 This is a decay coefficient that takes into account the attenuation of sea salt due to the distance d from the coast. Therefore, the index of the amount of salt adhering to the steel, i.e., the primary corrosion index Q1(t), is used. Xi WX Let it be as shown in equation (1).

[0247] Next, in the third step, according to each observation time t Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X ).

[0248] Q2(t Xi W X )={Q2(t Xi-1 W X )+Q1(t Xi W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi …(2)

[0249] However, in equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi = 1. Regarding the threshold, it is possible to conduct experiments beforehand and experimentally determine the amount of rainfall required for the salt adhering to the steel surface to disappear. The threshold can be determined, for example, within the range of 0–5 mm / hour.

[0250] In addition, s(t) in equation (2) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1.

[0251] Corrosion cumulative coefficient s(t) Xi The method for determining (W), as mentioned above, can be based on meteorological data. X The decision can be made by taking into account the latitude, longitude, and elevation of location X, or by calculating the sunrise and sunset times.

[0252] In equation (2), for the observation time t Xi The immediate observation time is the observation time t.Xi-1 The secondary corrosion index Q2(t) Xi-1 W X Add the observation time t i The primary corrosion index Q1(t) Xi W X In addition, the primary corrosion index Q1(t) Xi W X Multiply by the cumulative corrosion coefficient s(t) i ) and (t Xi -t Xi-1 Therefore, Q1(t) becomes part of equation (2). Xi W X )·s(t Xi )·(t Xi -t Xi-1 The polynomial of ) at observation time t Xi-1 ~t Xi When the time interval is during the daytime (from sunrise to sunset), it becomes 0, and the observation time t Xi-1 ~t Xi The value becomes greater than 0 when the time is nighttime (the period from sunset to sunrise).

[0253] Additionally, in equation (2), for {Q2(t) Xi-1 W X )+Q1(t Xi W X )·s(t Xi )·(t Xi -t Xi-1 The polynomial multiplied by c(t) Xi Therefore, Q2(t) Xi W X At observation time t Xi-1 ~t Xi The rainfall amount becomes 0 if it exceeds the threshold at observation time t. Xi-1 ~t Xi The value becomes greater than 0 when the rainfall is below the threshold.

[0254] Therefore, the secondary corrosion index Q2(t) Xi W X The salt content is an indicator of the amount of salt that accumulates on the surface of steel during the night, from sunset to sunrise, when condensation forms and salts carried by the wind dissolve into the condensation.

[0255] Next, in the fourth step, according to each observation time t Xi The corrosion index Q(t) is calculated using the following formula (3A). Xi W X ).

[0256] Q(t Xi W X )=Q2(t Xi W X )·p(t Xi …(3A)

[0257] In equation (3A), p(t) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t Xi Let p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1. Corrosion detection coefficient p(t) Xi The method for determining (W), as mentioned above, can be based on meteorological data. X The decision can be made by taking into account the latitude, longitude, and elevation of location X, or by calculating the sunrise and sunset times.

[0258] In equation (3A), for Q2(t) Xi W X ) multiplied by p(t) Xi Therefore, Q(t) Xi W X At observation time t Xi-1 ~t Xi When the time interval is night (the period from sunset to sunrise), it becomes 0, and at the observation time t Xi-1 ~t Xi The value becomes greater than 0 when the time is during the daytime (the period from sunrise to sunset).

[0259] Therefore, the corrosion index Q(t) Xi W X The salt content is an indicator of the amount of salt that is generated on the surface of steel during the night from sunset to sunrise, where salt is carried by the wind and dissolved into the condensation, and then evaporates due to the sunlight during the following day, thus concentrating the salt.

[0260] Generally speaking, the greater the amount of salt adhering to the steel, the easier it is for the steel to corrode. Therefore, the corrosion index Q(t) can be considered a reliable indicator of corrosion resistance. Xi W X The larger the value, the greater the corrosion rate of the steel.

[0261] By performing the aforementioned calculation steps, it is possible to calculate according to each time t. Xi The corrosion index Q(t) was obtained. Xi W X ).

[0262] In the extreme value prediction step, the location X is set as the predetermined location M and W for the utilization of steel. X Let W be the name of the project. M t Xi Let t be the value of t. Mi u(t) Xi Let u(t) be the value of u(t). Mi ), θw(t Xi Let ) be θw(t Mi ), c(t) Xi Let c(t) be the value of c(t). Mi ), s(t) Xi Let ) be s(t) Mi ), p(t) Xi Let p(t) be the base value of p(t). Mi From this, the corrosion index Q(t) can be obtained. Mi W M ).

[0263] In the calculation step of the corrosion estimation process, location X is set as the test location Z and W of the steel. X Let W be the name of the project. Z t Xi Let t be the value of t. Zi u(t) Xi Let u(t) be the value of u(t). Zi ), θw(t Xi Let ) be θw(t Zi ), c(t) Xi Let c(t) be the value of c(t). Zi ), s(t) Xi Let ) be s(t) Zi ), p(t) Xi Let p(t) be the base value of p(t). Zi From this, the corrosion index Q(t) can be obtained. Zi W Z ).

[0264] Alternatively, the calculation steps can also be performed in the extreme value prediction unit or the corrosion amount prediction unit of the corrosion amount prediction system. Furthermore, the first to fourth steps can also be performed in the first to fourth calculation units of the extreme value prediction unit or the corrosion amount prediction unit, respectively.

[0265] Next, the extreme value estimation step and the corrosion amount estimation step of the prediction method of this embodiment will be described. Alternatively, the extreme value estimation step and the corrosion amount estimation step can also be performed in the extreme value estimation unit and the corrosion amount estimation unit of the corrosion amount prediction system.

[0266] (Extremum prediction steps)

[0267] In the extreme value prediction step, firstly, based on the meteorological observation value W at the predetermined location M for steel utilization... M According to each past observation time t Mi Calculate the corrosion index Q(t) of the steel. Mi W M Corrosion index Q(t) Mi W M The calculation of ) is performed through the calculation steps described above.

[0268] Therefore, according to the past period t at the predetermined location M for the utilization of steel. M1 ~t Mn Each observation time t included M1 t M2 …t Mn Calculate the corrosion index Q(t) M1 W M ), Q(t) M2 W M ), …Q(t) Mn W M Thus, the observation time t can be obtained. M1 ~t Mn Corrosion index Q(t) Mi W M (Time series data)

[0269] Figure 3 The image shows the observation time t. M1 ~t Mn Corrosion index Q(t) Mi W M This is an example of charting time series data. Figure 3 Therefore, the observation time t Mi The horizontal axis represents the corrosivity index Q(t). Mi W M The chart uses ) as the vertical axis. Additionally, Figure 3 The corrosion index Q(t) is determined by setting a location approximately 700m from the coast in the Seto Inland Sea region of Japan as a predetermined location M. Mi W M This is an example of time series data.

[0270] Next, in the extreme value prediction step, by using the previously obtained observation time t M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis was performed on the time series data to predict the future evaluation period t. F1 ~t Fn The maximum value of the internal corrosion index Qmax (t Fi ).

[0271] Extreme value statistical analysis is a statistical method used to predict the magnitude and probability of certain values ​​based on given temporal and spatial data. That is, by observing time t... M1 ~t Mn Corrosion index Q(t) Mi W M By setting the data as extreme value statistical analysis, it is possible to predict the magnitude of future corrosiveness index Q(t) based on past meteorological observations. Mi W M Specifically, let's assume that the corrosivity index Q(t) is... Mi W M The data is arranged in descending order, and a Gumbel probability distribution plot is created based on this data distribution. Furthermore, the Gumbel probability distribution plot is used to predict the future evaluation period t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi ).

[0272] exist Figure 4A as well as Figure 4B The image shows an example of the results of extreme value statistical analysis. Through extreme value statistical analysis, the results are derived from... Figure 4A The observation time t shown M1 ~t Mn Corrosion index Q(t) Mi W M Time series data can be used to obtain Figure 4B The future evaluation period t shown F1 ~t Fn A chart showing the maximum values ​​of the internal corrosion index. (Refer to...) Figure 4B Charts, for example, can reveal the maximum value Q of the corrosion index at location M 10 years from now. max (t Fi ).in addition, Figure 4A as well as Figure 4B This is an example of the analysis results when a location approximately 75m from the coast of Japan is set as a predetermined location M.

[0273] (Steps for estimating corrosion levels)

[0274] In the corrosion estimation step, the measured corrosion amount and the corrosion index Q(t) are used based on the pre-calculated corrosion test results. Zi W ZThe relationship between the maximum values ​​of () is explained below.

[0275] To derive the relationship, we need to calculate the measured corrosion amount obtained from the corrosion test of the steel at test site Z, and the meteorological observation value W based on observation site X as the test site Z. Z According to each observation time t during the corrosion test... Zi The obtained corrosion index Q(t) of the steel Zi W Z ).

[0276] The measured corrosion amount of the steel is determined by actually placing the steel outdoors at test site Z and observing its corrosion. The evaluation of corrosion amount can be, for example, corrosion reduction, corrosion depth, or an evaluation based on RN.

[0277] The amount of corrosion of steel varies depending on the type of steel. Therefore, a corrosion test can be conducted at the predetermined location M on the same type of steel as the steel to be used.

[0278] Furthermore, the test site Z can be the same location as the predetermined utilization site M of the steel, or it can be a different location. Moreover, the test site Z can be one location or multiple locations. To improve prediction accuracy, multiple test sites Z are preferred. That is, although the test site Z can be a single location Z... α However, it is preferable to have multiple different locations Z. α Z β Z γ …When there are multiple test sites Z, it is more preferable to include sites with different distances from the sea among these multiple test sites. Furthermore, it is even more preferable to include sites with different meteorological conditions among the multiple test sites. By setting multiple test sites in this way, the corrosion index Q(t) obtained through corrosion testing can be… Zi W Z The maximum value of Q) Rmax Expanding the scope can improve the accuracy of predictions. Additionally, the Z area at each test site... α Z β Z γ The test period at each location Z can be either the same length or different lengths. Furthermore, the test locations Z... α Z β Z γ The observation time t included in the experiment at ... Z1 t Z2 …t Zn Specifically, it is t Zα1 ~t Zαn t Zβ1 ~t Zβn tZγ1 ~t Zγn "Each" can be the same moment or different moments.

[0279] In addition, regarding the corrosion index Q(t) Zi W Z The test period t is based on the corrosion test at test site Z. Z1 ~t Zn Meteorological observation value W Z According to each observation time t Zi To determine the corrosion index Q(t) of the steel. Zi W Z Corrosion index Q(t) Zi W Z The calculation of ) is performed through the aforementioned calculation steps. Therefore, according to the test period t at test site Z... Z1 ~t Zn Each observation time t included Z1 t Z2 …t Zn Calculate the corrosion index Q(t) Z1 W Z ), Q(t) Z2 W Z ), …Q(t) Zn W Z ).

[0280] When there are multiple test sites Z, the corrosion index Q(t) is calculated for each test site. Z1 W Z The meteorological observation value W under this condition. Z Using test site Z α Z β Z γ Each observation time t included in each experiment period at … Z1 t Z2 …t Zn Specifically, it is t Zα1 ~t Zαn t Zβ1 ~t Zβn t Zγ1 ~t Zγn Meteorological observation values, etc.

[0281] Next, for each corrosion test, the obtained corrosion index Q(t) is... Z1 W Z ), Q(t) Z2 W Z ), …Q(t) Zn W Z Extract the maximum value Q from )Rmax And extract the maximum value Q Rmax A correlation was established with the test results of each corrosion test, i.e., the measured corrosion amount. Furthermore, the maximum value Q for establishing the correlation was determined. Rmax The relationship between the logarithm and the measured corrosion amount. Figure 5 The figure shows the maximum value Q for SUS304. Rmax An example of plotting the logarithm and measured corrosion amount (RN) and the relationship graph. Figure 5 The dashed lines in the diagram represent the relational expressions.

[0282] The relation is set to the maximum value Q. Rmax The logarithm and the measured corrosion amount (RN) can be plotted using a function that approximates the line using the least squares method. For example, as a relation, it can be set as the maximum value Q. Rmax The logarithm of the α-corrosion coefficient (X) is used as the independent variable, and the measured corrosion amount (RN) is used as the dependent variable, which is a linear function Y = αX + β. α and β are constants. This relationship can be improved in accuracy by setting the number of test sites Z to multiple locations. Furthermore, without setting the maximum value Q... Rmax When converted to logarithms, we can also avoid using the linear function Y = αX + β, and instead use other functions that are easily approximated, and obtain the coefficients through regression calculations, etc.

[0283] like Figure 5 As shown, SUS304 is in a state where the maximum value Q increases. Rmax The trend is towards increased RN (reduction rate) and decreased appearance of the steel. Additionally, Figure 5 This represents the maximum value Q when multiple locations in the Seto Inland Sea region of Japan, at distances from the coast ranging from 10 to 1000 meters, are designated as test sites Z. Rmax A graph showing the relationship between the logarithm and the measured corrosion amount.

[0284] Furthermore, in the corrosion estimation step, the value Q is calculated beforehand. Rmax The relationship between the logarithm and the measured corrosion amount is incorporated into the maximum value Q of the corrosion index predicted in the extreme value prediction step. max (t Fi Therefore, it is possible to obtain an estimated value of the corrosion amount of the steel during a future estimated period at a predetermined location M.

[0285] Figure 6 This is an example of the output of the estimation method in this embodiment. For example, for various types of steel (Steel A to Steel E), it is possible to obtain estimated values ​​of RN from one year ago to several decades ago.

[0286] As explained above, in the steel corrosion prediction method, corrosion prediction system, and corrosion prediction program of this embodiment, the corrosion amount is predicted by analyzing past observation times t at the predetermined utilization location M of the steel. Mi Corrosion index Q M (t Mi W M Extreme value statistical analysis was performed on the time series data to predict the future evaluation period t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi Next, the measured corrosion amount of the steel, pre-calculated based on the corrosion test results, is compared with the steel's corrosion index Q(t). Zi W Z The maximum value of the corrosion index Q is incorporated into the relationship between the two. max (t Fi This invention predicts the future corrosion rate of steel in such a way that it can improve the accuracy of corrosion prediction compared to methods that predict corrosion rate based on previous annual averages.

[0287] Furthermore, the corrosion prediction method, system, and procedure for steel in this embodiment predict the future corrosion amount of the steel at a predetermined location M based on the results of a corrosion test on the steel at test location Z. In this embodiment, as long as the measured corrosion amount and corrosion index Q(t) of the steel at test location Z can be obtained... Zi W Z The correspondence between them and the meteorological observation value W based on the predetermined location M M The obtained corrosion index Q(t) Mi W M The invention can be derived from time-series data. Therefore, it has excellent versatility.

[0288] Furthermore, based on the steel corrosion prediction method, corrosion prediction system, and corrosion prediction program of this embodiment, the primary corrosion index Q1(t) is calculated sequentially. Xi W X ) and secondary corrosion index Q2(t Xi W X ), and according to the secondary corrosion index Q2(t Xi W X ) Calculate the corrosion index Q(t) Xi W X Here, the primary corrosion index Q1(t) Xi W X () is an indicator of the amount of salt adhering to steel, based on wind speed u(t) Xi) and wind direction θs, θw(t Xi The calculation is based on the secondary corrosion index Q2(t). Xi W X The calculation is as follows: The observation time t... Xi The immediate observation time is the observation time t. Xi-1 The secondary corrosion index Q2(t) Xi-1 W X ) and observation time t Xi The primary corrosion index Q1(t) Xi W X The values ​​are added together, and the extent to which salt, the primary cause of corrosion, is washed away by rainfall is taken as the corrosion disappearance coefficient c(t). Xi The corrosion accumulation coefficient s(t) is introduced and the increased adhesion of salt at night, when condensation easily forms on the steel surface, is used as the corrosion accumulation coefficient. Xi And so it is imported. Furthermore, the corrosivity index Q(t) Xi W X The following calculation is performed: The phenomenon of corrosion development caused by the drying of salty condensation at night due to daytime sunlight is analyzed using the corrosion coefficient p(t). Xi The corrosion detection coefficient p(t) is quantified and then converted into numerical values. Xi ) and secondary corrosion index Q2(t Xi W X Multiply by t. From this, we can obtain the corrosion index Q(t), which reflects the influence of meteorological conditions on the amount of salt adhering to the steel. Xi W X This indicator can improve the accuracy of corrosion prediction.

[0289] Next, we will discuss the method for predicting the corrosion amount of steel in this embodiment, the corrosion amount prediction system, and the corrosion index Q(t) in the corrosion amount prediction program. Xi W X The following is an illustration of a variation of the corrosivity index Q(t). The first variation is related to the corrosion index Q(t). Xi W X The second variation example is a temperature correction variation based on the determination of the wettability of the steel surface to correct the corrosion index Q(t). Xi W X These are variations of the previous examples. By employing these variations, the accuracy of corrosion prediction can be further improved.

[0290] (First variation)

[0291] In the first variation, in the fourth step of the corrosion amount prediction method or corrosion amount prediction procedure, and in the fourth processing unit of the corrosion amount prediction system, instead of the above equation (3A), the following equation (3B) is used to calculate the corrosion amount at each observation time t. Xi To calculate the corrosion index Q(t) Xi W X ).

[0292] Q(t Xi W X ) = 10 0.04Tb ·Q2(t Xi W X )·p(t Xi …(3B)

[0293] In equation (3B), Tb is any temperature within the range of Temp (°C) to (Temp+2)°C, where the sunrise temperature is set to Temp (°C). Tb is preferably the sunrise temperature Temp (°C).

[0294] The corrosion of steel is affected not only by the amount of salt adhering to the steel surface but also by temperature. Therefore, when the temperature difference between the predetermined location M and the test location Z (where observation point X is located) increases, the accuracy of corrosion prediction may sometimes decrease. Therefore, to improve the accuracy of corrosion prediction, it is preferable to include a correction term for the temperature at observation point X in Q(t). Xi W X In the calculation formula of ).

[0295] Through research, the inventors have discovered that it is preferable to make the correction term a power of 10, and the exponent of the power is preferably the product of the temperature Tb (°C) and the coefficient. Furthermore, based on the measured corrosion amount of the steel and the steel's corrosion index Q (t... Zi W Z When deriving the relationship, after calculating the exponent in the correction term through regression, a value of 0.04 is obtained. Therefore, the coefficient of the exponent in the temperature correction term is set to 0.04.

[0296] The temperature correction term includes an air temperature Tb (°C) within the range of Temp (°C) to (Temp+2)°C, assuming the sunrise temperature is set to Temp. When steel is wet, salt dissolves into the surface moisture. Corrosion easily occurs when the salt concentration increases due to moisture evaporation. Furthermore, the air temperature at the time of moisture evaporation affects the amount of corrosion. This evaporation occurs when the air temperature rises around sunrise. Therefore, the air temperature Tb added to the correction term is set to any temperature within the range of Temp (°C) to (Temp+2)°C, assuming the sunrise temperature is set to Temp (°C). Preferably, the sunrise temperature Temp (°C) is used. The temperature Temp is set to the meteorological observation value W. X The temperature data included is sufficient.

[0297] Based on the above, the temperature correction term that should be included in the above formula (3B) is set to 10. 0.04Tb .

[0298] Figure 7 This represents the maximum value Q for SUS304. Rmax A graph showing the relationship between the logarithm of the coefficient of variation and the measured corrosion amount (RN), presented before temperature correction. Additionally, Figure 8 Yes Figure 7 The chart is a temperature-corrected chart. (Compared to...) Figure 5 same, Figure 7 as well as Figure 8 This represents the maximum value Q when multiple locations in the Seto Inland Sea region of Japan, at distances of 10 to 1000 meters from the coast, are used as test sites Z. Rmax A graph showing the relationship between the logarithm and the measured corrosion amount, but except... Figure 5 In addition to the drawing shown, new drawings were added. Figure 7 as well as Figure 8 The dashed lines in the diagram represent the maximum value Q. Rmax The relationship between the logarithm of the equation and the measured corrosion amount (RN) is expressed as a linear equation. The equation is formed by taking the maximum value Q. Rmax The logarithm of the equation is taken as the independent variable X, and the measured corrosion amount (RN) is taken as the dependent variable Y, which is a linear function Y = αX + β. α and β are constants. Additionally, regarding... Figure 5 The description is the same, the maximum value Q Rmax It doesn't necessarily have to be converted to a logarithm.

[0299] Figure 7 The maximum value Q in Rmax The square of the correlation coefficient (R) between the logarithm of the figure and the measured corrosion amount (RN). 2 The value is approximately 0.81. Figure 8 R in 2 The value is approximately 0.86. Figure 7 R in 2 Values ​​exceeding 0.80 indicate sufficiently high reliability of the relation even without temperature correction, but temperature correction further enhances its reliability. Therefore, the maximum value Q of the corrosion index, predicted in the extreme value prediction step, is imported into the temperature-corrected relation. max (t Fi This allows us to obtain an estimated value of the corrosion amount of steel during a future inference period at a predetermined location M, and improves its reliability.

[0300] (Second variation)

[0301] In the second variation, s(t) in equation (2) Xi ) and p(t) in equation (3A) Xi Based on the provisions as follows, import them into equation (2) or equation (3C), and calculate the secondary corrosion index Q2(t) according to equation (2). Xi W X Furthermore, the corrosion index Q(t) is calculated according to formula (3C). Xi W X ).

[0302] In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi The amount of moisture adhering to the surface of the steel, ws(t) Xi If ) is 0, then set it as s(t) Xi ) = 0, in ws(t Xi If the value exceeds 0, set it to s(t). Xi ) = 1.

[0303] p(t) in equation (3C) Xi ) is the corrosion detection coefficient, in Ws(t) Xi If the value exceeds 0, set it to p(t). Xi ) = 0, in ws(t Xi If the value is less than or equal to 0, then p(t) is set to p(t). Xi ) = 1.

[0304] Additionally, Ws(t) Xi ) is due to the observation time t Xi The estimated value is obtained by subtracting the evaporation rate from the amount of moisture adhering to the surface of the steel from the amount of rainfall and condensation. This value is calculated based on the surface temperature of the steel, air temperature (°C), air pressure (hPa), wind speed (m / s) on the steel surface, and relative humidity (%). Moisture adhering amount ws(t) XiThe value is obtained using the calculation method described later. The wind speed (m / s) on the steel surface can be obtained from meteorological observation data.

[0305] The corrosion index Q(t) of this invention Xi W X The salt content on the surface of steel is an index that takes into account climate change. In steel, condensation forms on the surface at night, and salt dissolves into this condensation. During the day, as the temperature rises, the salt-containing condensation evaporates, causing salt concentration on the steel surface and promoting corrosion. Therefore, the amount of moisture adhering to the steel surface significantly affects corrosion. Thus, in the described embodiment, assuming that the moisture content on the steel surface increases due to condensation at night and decreases due to evaporation caused by rising daytime temperatures, the salt content is measured based on the observation time t. Xi-1 ~t Xi Is the time daytime or nighttime? Xi ) and p(t Xi The value is determined to be either 0 or 1.

[0306] On the other hand, in this variant example, the estimated observation time t is... Xi The amount of moisture adhering to the surface of the steel, ws(t) Xi Based on the inferred ws(t) Xi ) to s(t Xi ) and p(t Xi The value is determined to be either 0 or 1. Therefore, compared to inferring the moisture state of the steel surface based on whether it is day or night, the amount of corrosion can be predicted with greater accuracy.

[0307] Furthermore, in the second variation, in the fourth step of the corrosion quantity prediction method or corrosion quantity prediction procedure, and in the fourth processing unit of the corrosion quantity prediction system, instead of the above formula (3A), the following formula (3C) is used to calculate the corrosion quantity at each observation time t. Xi To calculate the corrosion index Q(t) Xi W X ).

[0308] Q(t Xi W X ) = 10 0.04Tc ·Q2(t Xi W X )·p(t Xi …(3C)

[0309] In equation (3C), Tc is the time difference between the observation time t and the time of observation. Xi The earliest moment, and the amount of moisture adhering to the steel surface, ws(t) Xi The temperature (°C) at the moment when it becomes 0.

[0310] The corrosion of steel is affected not only by the amount of salt adhering to the steel surface but also by temperature. Therefore, when the temperature difference between the predetermined location M and the test location Z (where observation point X is located) increases, the accuracy of corrosion prediction may sometimes decrease. Therefore, to improve the accuracy of corrosion prediction, it is preferable to include a correction term for the temperature at observation point X in Q(t). Xi W X In the calculation formula of ).

[0311] Through research, the inventors have discovered that it is preferable to make the correction term a power of 10, and the exponent of the power is preferably the product of the temperature Tb (°C) and the coefficient. Furthermore, based on the measured corrosion amount of the steel and the steel's corrosion index Q (t... Zi W Z When deriving the relationship, after calculating the exponent in the correction term through regression, a value of 0.04 is obtained. Therefore, the coefficient of the exponent in the temperature correction term is set to 0.04.

[0312] The temperature Tc (°C) in the correction term is relative to the observation time t. Xi The earliest time and the amount of moisture adhering to the steel surface ws(t) Xi The temperature (°C) at the moment when the temperature becomes 0. When steel is wet, salt dissolves into the surface moisture. Corrosion easily occurs when the moisture evaporates and the salt concentration increases. Furthermore, the temperature at which the moisture evaporates affects the amount of corrosion. Therefore, the temperature Tc added to the correction term is set to be the temperature at the observation time t. Xi The earliest time and the amount of moisture adhering to the steel surface ws(t) Xi The temperature (°C) at the moment when it becomes 0. This temperature Tc only needs to be set as the meteorological observation value W. X The included temperature data is sufficient. Additionally, the amount of moisture adhering to the steel surface, ws(t), is also required. Xi Use the value obtained through the calculation method described later.

[0313] In this variation, the observation time t is inferred. Xi The amount of moisture adhering to the surface of the steel, ws(t) Xi And calculate the amount of water adhering to it, ws(t). Xi The temperature Tc becomes 0. Furthermore, Tc is used to adjust Q(t). Xi W X Temperature correction is performed. Therefore, compared to inferring the moisture state of the steel surface based on whether it is day or night, corrosion levels can be predicted with greater accuracy.

[0314] (Moisture adhering to the surface of steel ws(t)) Xi ))

[0315] Next, the amount of moisture adhering to the surface of the steel used in the first and second modified examples, ws(t), is measured. Xi This will be explained. The amount of moisture adhering to the container, ws(t), is... Xi It is derived by the following formula (A).

[0316] ws(t Xi ) = (W R +ΔW-V a )·Δt …(A)

[0317] In equation (A), W R The amount of water adhering to the surface of the steel due to rainfall (kg / m) 2 / s), ΔW is the rate of condensation formation (kg / m³). 2 / s), V a It is the evaporation rate of water (kg / m³) 2 / s), Δt is Δt=t Xi-1 -t Xi (s).

[0318] ΔW is derived by the following equation (B).

[0319] ΔW=α'·(VH-VHs) …(B)

[0320] α' Moisture transfer rate between the air surrounding the steel and the steel surface (kg / m²) 2 / s), VH is the observation time t Xi The absolute humidity in the air (kg / kg), VHs is the surface saturation absolute humidity (kg / kg) determined by the temperature of the steel surface.

[0321] V a It is derived by the following formula (C).

[0322] V a =Sh·D·(c1-c2) / L …(C)

[0323] Sh is the Schrödinger number, derived from the Schmidt number Sc and the Reynolds number Re on the steel surface via the following equation (C-1). D is the diffusion coefficient of water vapor, determined based on the observation time t. Xi The air temperature T (°C) and air pressure p (hPa) are derived using the following equation (C-2). c1 is the observation time t. Xi Saturated water vapor content (kg / m³) at temperature T (°C) 3 c2 is the observation time t. Xi Water vapor content in the air at temperature T (°C) (kg / m³) 3 L represents length. The Reynolds number Re is derived by the following equation (C-3). Ρ is the air density (kg / m³).3 v is the wind speed on the steel surface (m / s), L is the characteristic length, and μ is the viscosity coefficient of air (Pa / s).

[0324] Sh = 0.332·Re 1 / 2 ·Sc 1 / 3 …(C-1)

[0325] D = 0.241 × 10 -4 ·{(T+273.15) / 288} 1.75 ·1013.25 / (p·t) …(C-2)

[0326] Re = ρv 2 / (μv / L) …(C-3)

[0327] The amount of water adhering to the membrane, ws(t), is calculated in this way. Xi To calculate the corrosion index Q(t) Xi W X This allows for more accurate prediction of corrosion levels.

[0328] Moisture adhering to the surface of steel ws(t) Xi The upper limit of the value can vary depending on the surface condition of the steel, the tilt angle of the surface when the steel is installed, etc. Therefore, when setting an upper limit value, it can be set appropriately according to the type of steel, the tilt state, etc.

[0329] Furthermore, in the first and second variations described above, the time is set as the time t at location X. Xi The meteorological observation value is set as the meteorological observation value W at location X. X The explanations will be provided separately, but when location X is set as the location M where the steel is utilized, it is only necessary to set time t. Xi Replace with observation time t at location M Mi That is, the meteorological observation value W X Replace with meteorological observation value W at location M M That's sufficient. Additionally, if location X is set as test location Z, simply set time t... Xi Replace with time t during the test period Zi That is, the meteorological observation value W X Replace with meteorological observation value W at test site Z Z That's all.

[0330] Next, the proposed method for using steel in this embodiment will be described.

[0331] The proposed method for steel in this embodiment includes: a prediction step for predicting the corrosion amount of steel at a predetermined location M in the future using a previously described method for predicting the corrosion amount of steel; and a step for the sales manager to provide the customer with the predicted corrosion amount obtained through the prediction step.

[0332] Furthermore, the proposed method for steel in this embodiment includes: a prediction process for predicting the corrosion amount of various types of steel at a predetermined location M in the future using the previously described method for predicting the corrosion amount of steel; and a process in which the sales manager provides the customer with the predicted corrosion amount of each type of steel obtained through the prediction process.

[0333] The predicted value of corrosion can also be a rating, for example. The rating can be the rating specified in JIS G 0595:2004 (Method for evaluating the degree of surface corrosion of stainless steel).

[0334] In the process of providing information to customers, the predicted corrosion value can be displayed instead of the predicted value, or an image of the steel surface corresponding to the predicted corrosion value can be displayed simultaneously. Specifically, for example, the image of the steel surface corresponding to the predicted corrosion value can be displayed on the display unit of the steel corrosion prediction device, or it can be presented to the customer in paper form.

[0335] Additionally, the predicted corrosion level provided to customers can be a rating accurate to at least one decimal place. Similarly, the image of the steel surface corresponding to the predicted corrosion level can also be an image corresponding to a rating accurate to at least one decimal place.

[0336] Based on previously documented methods for predicting the corrosion rate of steel, such as... Figure 6 As shown, it is possible to obtain, for example, the predicted RN value 50 years later for steels made of various types of steel. Therefore, according to the proposed method for steel in this embodiment, by predicting the future corrosion amount of steel at a predetermined location M, the sales manager provides the customer with the obtained corrosion prediction value, thereby responding to the customer's expectation of knowing the future corrosion amount when the steel is used in an outdoor environment for a long time. In addition, the corrosion prediction value can be improved with higher accuracy, so suitable steel can be recommended to the customer.

[0337] In addition, based on previously documented methods for predicting the corrosion rate of steel, such as... Figure 6As shown, for various types of steel, the predicted RN value 50 years from now can be obtained for each steel grade. Therefore, according to the steel proposal method of this embodiment, the corrosion amount of various types of steel at a predetermined location M in the future can be predicted. The sales manager provides the customer with the predicted corrosion amount for each type of steel, thereby providing a highly accurate corrosion amount prediction for each steel grade and enabling the selection of appropriate steel. Thus, suitable steel can be recommended to the customer.

[0338] Furthermore, by displaying an image of the steel surface corresponding to the predicted corrosion level, customers can intuitively understand the degree of corrosion. Additionally, by showing a rating at least to one decimal place or an image of the corresponding steel surface as the predicted corrosion level, a high-precision prediction result can be provided.

[0339] Industrial availability

[0340] This invention provides a method, system, and program for predicting the corrosion amount of steel, which can improve the accuracy of steel corrosion prediction. Furthermore, this invention provides a proposed method for predicting the corrosion amount of steel using the corrosion amount prediction method. These methods are industrially applicable.

Claims

1. A method for predicting the corrosion amount of steel, used as an indicator to evaluate the corrosion amount of steel exposed outdoors, employing data from each observation time t at observation location X. Xi Meteorological observations of wind speed, wind direction, and rainfall W X The corrosion index Q(t) of the steel was calculated. Xi W X ),in, i is an integer from 1 to n, characterized by having: The extreme value prediction step is based on the meteorological observation value W of the predetermined location M for steel at the observation location X. M According to each past observation time t Mi Calculate the corrosion index Q(t) of the steel. Mi W M ), for the observation time t M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis is performed on the time series data to predict future evaluation periods t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi To speculate; and The corrosion estimation step involves pre-calculating the measured corrosion amount obtained from the corrosion test of the steel at test site Z, and comparing it with the meteorological observation value W based on the observation site X being the test site Z. Z And according to each observation time t during the corrosion test. Zi The obtained corrosion index Q(t) of the steel Zi W Z The relationship between the maximum values ​​of ) is expressed, and the maximum value Q of the corrosivity index predicted in the extreme value prediction step is input into this relationship. max (t Fi This allows us to obtain an estimated value of the amount of corrosion of the steel during the evaluation period.

2. The method for predicting the corrosion amount of steel as described in claim 1, wherein, The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for ) The calculation steps include: The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall; The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X ); The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X ); as well as The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3A). Xi W X ), Q1(t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q2(t Xi ,W X )={Q2(t Xi-1 ,W X )+Q1(t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=Q2(t Xi ,W X )·p(t Xi ) …(3A) Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°. In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1, In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1, In equation (3A), p(t) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t Xi Let p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

3. The method for predicting the corrosion amount of steel as described in claim 1, wherein, The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for ) The calculation steps include: The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall; The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X ); The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X ); as well as The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3B). Xi W X ), Q1(t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q2(t Xi ,W X )={Q2(t Xi-1 、W X )+Q1(t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tb ·Q2(t Xi ,W X )·p(t Xi ) …(3B) Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°. In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1, In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1, In equation (3B), Tb is any temperature within the range of Temp (°C) to (Temp+2)°C, where the sunrise temperature is set to Temp (°C). p(t) in equation (3B) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t Xi Let p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

4. The method for predicting the corrosion amount of steel as described in claim 1, wherein, The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for ) The calculation steps include: The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall; The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X ); The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X ); as well as The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3C). Xi W X ), Q1(t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q2(t Xi ,W X )={Q2(t Xi-1 、W X )+Q1(t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tc ·Q2(t Xi ,W X )·p(t Xi ) …(3C) Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°. In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1, In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi The amount of moisture adhering to the surface of the steel, ws(t) Xi If ) is 0, then set it as s(t) Xi ) = 0, in the ws(t) Xi If the value exceeds 0, set it to s(t). Xi ) = 1, the ws(t) Xi ) is from the observation time t Xi The estimated value is obtained by subtracting the evaporation rate from the amount of moisture adhering to the surface due to rainfall and condensation. It is calculated based on the steel's surface temperature, air temperature (°C), air pressure (hPa), wind speed at the steel surface (m / s), and relative humidity (%). In equation (3C), Tc is the time relative to the observation time t. Xi The earliest time and the amount of moisture adhering to the steel surface ws(t) Xi The temperature (°C) at the moment when it becomes 0. p(t) in equation (3C) Xi ) is the corrosion detection coefficient, in the ws(t) Xi If the value exceeds 0, set it to p(t). Xi ) = 0, in the ws(t) Xi If the value is less than or equal to 0, then p(t) is set to p(t). Xi ) = 1.

5. The method for predicting the corrosion amount of steel as described in any one of claims 1 to 4, characterized in that, The steel is made of stainless steel.

6. A corrosion prediction system for steel, a computer-based system for predicting the corrosion of steel when exposed to outdoor conditions, used as an indicator to evaluate the corrosion of steel at each observation time t at observation location X. Xi Meteorological observations of wind speed, wind direction, and rainfall W X The corrosion index Q(t) of the steel was calculated. Xi W X ),in, i is an integer from 1 to n, characterized by having: The extreme value prediction department is based on the meteorological observation value W of the predetermined location M for the utilization of steel at the observation location X. M According to each past observation time t Mi Calculate the corrosion index Q(t) of the steel. Mi W M ), for the observation time t M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis is performed on the time series data to predict future evaluation periods t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi To speculate; and The corrosion estimation unit pre-calculates the measured corrosion amount obtained from the corrosion test of the steel at test site Z, and the meteorological observation value W based on the observation site X being the test site Z. Z And according to each observation time t during the corrosion test. Zi The obtained corrosion index Q(t) of the steel Zi W Z The relationship between the maximum values ​​of ) is expressed, and the maximum value Q of the corrosivity index predicted in the extreme value prediction section is input into this relationship. max (t Fi This allows us to obtain an estimated value of the amount of corrosion of the steel during the evaluation period.

7. The corrosion prediction system for steel as described in claim 6, wherein, The extreme value estimation unit and the corrosion amount estimation unit respectively include the ability to calculate the corrosion index Q(t) Xi W X The computing department of ) The computing unit includes: The first processing unit acquires at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall; The second processing unit processes data according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X ); The third processing unit, according to each observation time t Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as The fourth processing unit, according to each observation time t Xi The corrosion index Q(t) is calculated using the following formula (3A). Xi W X ), Q1(t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q2(t Xi ,W X )={Q2(t Xi-1 ,W X )+Q1(t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=Q2(t Xi ,W X )·p(t Xi ) …(3A) Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°. In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1, In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1, In equation (3A), p(t) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t Xi Let p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

8. The corrosion prediction system for steel as described in claim 6, wherein, The extreme value estimation unit and the corrosion amount estimation unit respectively include the ability to calculate the corrosion index Q(t) Xi W X The computing department of ) The computing unit includes: The first processing unit acquires at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall; The second processing unit processes data according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X ); The third processing unit, according to each observation time t Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as The fourth processing unit, according to each observation time t Xi The corrosion index Q(t) is calculated using the following formula (3B). Xi W X ), Q1(t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q2(t Xi ,W X )={Q2(t Xi-1 、W X )+Q1(t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tb ·Q2(t Xi ,W X )·p(t Xi ) …(3B) Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°. In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1, In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1, In equation (3B), Tb is any temperature within the range of Temp (°C) to (Temp+2)°C, where the sunrise temperature is set to Temp (°C). p(t) in equation (3B) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t Xi Let p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

9. The corrosion prediction system for steel as described in claim 6, wherein, The extreme value estimation unit and the corrosion amount estimation unit respectively include the ability to calculate the corrosion index Q(t) Xi W X The computing department of ) The computing unit includes: The first processing unit acquires at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall; The second processing unit processes data according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X ); The third processing unit, according to each observation time t Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X );as well as The fourth processing unit, according to each observation time t Xi The corrosion index Q(t) is calculated using the following formula (3C). Xi W X ), Q1(t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q2(t Xi ,W X )={Q2(t Xi-1 、W X )+Q1(t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tc ·Q2(t Xi ,W X )·p(t Xi ) …(3C) Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°. In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1, In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi The amount of moisture adhering to the surface of the steel, ws(t) Xi If ) is 0, then set it as s(t) Xi ) = 0, in the ws(t) Xi If the value exceeds 0, set it to s(t). Xi ) = 1, the ws(t) Xi ) is from the observation time t Xi The estimated value is obtained by subtracting the evaporation rate from the amount of moisture adhering to the surface due to rainfall and condensation. It is calculated based on the steel's surface temperature, air temperature (°C), air pressure (hPa), wind speed at the steel surface (m / s), and relative humidity (%). In equation (3C), Tc is the time relative to the observation time t. Xi The earliest time and the amount of moisture adhering to the steel surface ws(t) Xi The temperature (°C) at the moment when it becomes 0. p(t) in equation (3C) Xi ) is the corrosion detection coefficient, in the ws(t) Xi If the value exceeds 0, set it to p(t). Xi ) = 0, in the ws(t) Xi If the value is less than or equal to 0, then p(t) is set to p(t). Xi ) = 1.

10. The corrosion prediction system for steel as described in any one of claims 6 to 9, characterized in that, The steel is made of stainless steel.

11. A corrosion prediction program for steel, which is a steel corrosion prediction program used by an electronic computer, serves as an indicator for evaluating the corrosion amount of steel exposed to the outdoors, using data from each observation time t at observation location X. Xi Meteorological observations of wind speed, wind direction, and rainfall W X The corrosion index Q(t) of the steel was calculated. Xi W X ),in, i is an integer from 1 to n, characterized by having: The extreme value prediction step is based on the meteorological observation value W of the predetermined location M for steel at the observation location X. M According to each past observation time t Mi Calculate the corrosion index Q(t) of the steel. Mi W M ), for the observation time t M1 ~t Mn Corrosion index Q(t) Mi W M Extreme value statistical analysis is performed on the time series data to predict future evaluation periods t. F1 ~t Fn The maximum value of the internal corrosion index Q max (t Fi To speculate; and The corrosion estimation step involves pre-calculating the measured corrosion amount obtained from the corrosion test of the steel at test site Z, and comparing it with the meteorological observation value W based on the observation site X being the test site Z. Z And according to each observation time t during the corrosion test. Zi The obtained corrosion index Q(t) of the steel Zi W Z The relationship between the maximum values ​​of ) is expressed, and the maximum value Q of the corrosivity index predicted in the extreme value prediction step is input into this relationship. max (t Fi This allows us to obtain an estimated value of the amount of corrosion of the steel during the evaluation period.

12. The corrosion prediction program for steel as described in claim 11, wherein, The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for ) The calculation steps include: The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall; The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X ); The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X ); as well as The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3A). Xi W X ), Q1(t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q2(t Xi ,W X )={Q2(t Xi-1 ,W X )+Q1(t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=Q2(t Xi ,W X )·p(t Xi ) …(3A) Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°. In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1, In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1, In equation (3A), p(t) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t Xi Let p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

13. The corrosion prediction program for steel as described in claim 11, wherein, The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for ) The calculation steps include: The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall; The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X ); The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X ); as well as The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3B). Xi W X ), Q1(t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q2(t Xi ,W X )={Q2(t Xi-1 、W X )+Q1(t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tb ·Q2(t Xi ,W X )·p(t Xi ) …(3B) Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°. In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1, In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi-1 ~t Xi Let the interval be s(t) when it is daytime (the period from sunrise to sunset). Xi ) = 0, at observation time t Xi-1 ~t Xi If the time interval is nighttime (the period from sunset to sunrise), then s(t) is used. Xi ) = 1, In equation (3B), Tb is any temperature within the range of Temp (°C) to (Temp+2)°C, where the sunrise temperature is set to Temp (°C). p(t) in equation (3B) Xi ) is the corrosion detection coefficient, at observation time t Xi-1 ~t Xi Let p(t) be the interval between nighttime (sunset and sunrise). Xi ) = 0, at observation time t Xi-1 ~t Xi Let p(t) be the interval during the daytime (from sunrise to sunset). Xi ) = 1.

14. The corrosion prediction program for steel as described in claim 11, wherein, The extreme value estimation step and the corrosion amount estimation step respectively include calculating the corrosion index Q(t) Xi W X The calculation steps for ) The calculation steps include: The first step is to obtain at least each observation time t at the observation location X. Xi wind speed u(t) Xi ), wind direction θw(t) Xi and rainfall; The second step is to proceed according to each observation time t. Xi The corrosion index Q1(t) is calculated using the following formula (1). Xi W X ); The third step is to proceed according to each observation time t. Xi The secondary corrosion index Q2(t) is calculated using the following formula (2). Xi W X ); as well as The fourth step is to proceed according to each observation time t. Xi The corrosion index Q(t) is calculated using the following formula (3C). Xi W X ), Q1(t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q2(t Xi ,W X )={Q2(t Xi-1 、W X )+Q1(t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tc ·Q2(t Xi ,W X )·p(t Xi ) …(3C) Wherein, d in equation (1) is the distance (m) between the nearest coast to the observation location X and the observation location X, u(t) Xi ) is the observation time t Xi Wind speed (m / s), θw(t) Xi () represents the observation time t when north is set to 0°. Xi The wind direction (°) is given by θs, where θs is the azimuth (°) of the observation point X relative to the nearest coastline, with north set to 0°. In equation (2), c(t) Xi ) is the corrosion disappearance coefficient, at observation time t Xi-1 ~t Xi If the rainfall exceeds a threshold, it is set to c(t). Xi ) = 0, at observation time t Xi-1 ~t Xi If the rainfall between the two points is below the threshold, let c(t) be the threshold value. Xi ) = 1, In equation (2), s(t) Xi ) is the cumulative corrosion coefficient, at observation time t Xi The amount of moisture adhering to the surface of the steel, ws(t) Xi If ) is 0, then set it as s(t) Xi ) = 0, in the ws(t) Xi If the value exceeds 0, set it to s(t). Xi ) = 1, the ws(t) Xi ) is from the observation time t Xi The estimated value is obtained by subtracting the evaporation rate from the amount of moisture adhering to the surface due to rainfall and condensation. It is calculated based on the steel's surface temperature, air temperature (°C), air pressure (hPa), wind speed at the steel surface (m / s), and relative humidity (%). In equation (3C), Tc is the time relative to the observation time t. Xi The earliest time and the amount of moisture adhering to the steel surface ws(t) Xi The temperature (°C) at the moment when it becomes 0. p(t) in equation (3C) Xi ) is the corrosion detection coefficient, in the ws(t) Xi If the value exceeds 0, set it to p(t). Xi ) = 0, in the ws(t) Xi If the value is less than or equal to 0, then p(t) is set to p(t). Xi ) = 1.

15. The corrosion prediction program for steel as described in any one of claims 11 to 14, characterized in that, The steel is made of stainless steel.

16. A proposed method for producing steel, comprising: The prediction process, using the method for predicting the corrosion amount of steel according to any one of claims 1 to 5, predicts the future corrosion amount of steel at a predetermined location M; and The process by which the sales manager presents the customer with the predicted value of corrosion obtained through the prediction process, or an image of the steel surface corresponding to the predicted value of corrosion.

17. The proposed method for processing steel as described in claim 16, wherein, The predicted value of the corrosion amount is at least rated to the first decimal place.

18. The proposed method for processing steel as described in claim 16, wherein, The image of the steel surface corresponding to the predicted value of the corrosion amount is at least an image corresponding to the rating to the first decimal place.

19. A proposed method for producing steel, comprising: The prediction process, using the corrosion prediction method for steel as described in any one of claims 1 to 5, predicts the future corrosion amount of various types of steel at a predetermined location M; and The sales manager presents the customer with a predicted value of the corrosion amount of each type of steel obtained through the prediction process, or an image of the steel surface corresponding to the predicted corrosion amount.

20. The proposed method for processing steel as described in claim 19, wherein, The predicted value of the corrosion amount is at least rated to the first decimal place.

21. The proposed method for processing steel as described in claim 19, wherein, The image of the steel surface corresponding to the predicted value of the corrosion amount is at least an image corresponding to the rating to the first decimal place.

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