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

In the present invention, there is employed a method for predicting the amount of corrosion in a steel material, the method comprising a corrosion amount inference step in which: a corrosiveness index Q(tMi, WM) is derived for each past observed time tMi on the basis of a weather observation value WM at a scheduled usage location M of a steel material; extremal value statistical analysis is performed on time series data pertaining to the corrosiveness index Q(tMi, WM) between observed times tM1 and tMn; a local maximum value Qmax(tFi) of the corrosiveness index within the range of future evaluation periods tF1 to tFn is inferred; a relational expression is derived for the relationship between an actual corrosion amount obtained from a corrosion experiment at an experimentation location Z and the maximum value of the corrosiveness index Q(tZi, WZ) obtained for each observed time tZi during an experimentation period for the corrosion experiment on the basis of a weather observation value WZ at the experimentation location Z; the local maximum value Qmax(tFi) of the corrosiveness index that was inferred earlier is introduced into the relational expression; and an inference value for the amount of corrosion of the steel material in an estimation period is obtained.

Description

[Technical field]

[0001] The present invention relates to a method for predicting the amount of corrosion of a steel material, a system for predicting the amount of corrosion of a steel material, a program for predicting the amount of corrosion of a steel material, and a method for proposing a steel material. This application claims priority based on Japanese Patent Application No. 2021-59457, filed on March 31, 2021, the contents of which are incorporated herein by reference. [Background technology]

[0002] Architectural steel materials are used as materials for building exterior walls, columns, beams, etc. Customers who are considering or have decided to purchase architectural steel materials have a desire to predict the future amount of corrosion of architectural steel materials when used in outdoor environments for a long period of time.

[0003] Patent Document 1 describes a method for predicting corrosion of weathering steel, which includes a step of calculating a predicted amount of corrosion of weathering steel using an electronic computer, using extrinsic corrosion information including annual wet time, annual average wind speed, annual average temperature, amount of airborne salt, and amount of sulfur oxide at the planned location where the weathering steel is to be used, as well as intrinsic corrosion information related to the components of the weathering steel.

[0004] However, the corrosion of steel materials can be greatly affected by sudden weather changes. For example, when a typhoon approaches near the coast and the wind speed increases significantly compared to normal weather conditions, or when the wind speed increases significantly in a short period of time due to atmospheric pressure distribution, a large amount of salt adheres to the steel materials for buildings in a relatively short period of time. This can significantly increase the amount of corrosion of the steel materials.

[0005] In Patent Document 1, when calculating the predicted future corrosion amount, a prediction is made based on annual average values ​​such as the annual average wind speed and the annual average temperature. However, a prediction based on the annual average values ​​does not take into account short-term sudden weather fluctuations that can have a significant effect on the corrosion of steel materials as described above due to the structure of the data. Therefore, there is room for improving the prediction accuracy of the corrosion amount compared to conventional prediction methods. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] International Publication No. 2003 / 006957 Summary of the Invention [Problem to be solved by the invention]

[0007] An object of the present invention is to provide a method for predicting the amount of corrosion of a steel material, a system for predicting the amount of corrosion of a steel material, and a program for predicting the amount of corrosion of a steel material, which are capable of improving the prediction accuracy of the amount of corrosion of a steel material. Another object of the present invention is to provide a method for proposing a steel material, which utilizes the amount of corrosion of a steel material predicted by the method for predicting the amount of corrosion of a steel material. [Means for solving the problem]

[0008] In order to solve the above problems, the present invention employs the following configuration. [1] As an index for evaluating the amount of corrosion when steel is exposed outdoors, the observation time t Xi (where i is an integer from 1 to n) Meteorological observation values ​​W of wind speed, wind direction, and rainfall for each X The corrosion index Q(t Xi ,W X A method for predicting the amount of corrosion of a steel material using The observation point X is a meteorological observation value W M Based on the past observation time t Mi The corrosion index Q(t Mi ,W M ) and calculate the observation time t M1 ~t Mn Corrosion index Q(t Mi ,W M ) time series data, extreme value statistical analysis is performed to obtain the future evaluation period t F1 ~t Fn The maximum value of the corrosive index Q max (t Fi ) and an extreme value estimation step. The actual corrosion amount obtained from the corrosion test of the steel material at the test point Z and the meteorological observation value W at the observation point X at the test point Z Z Based on the observation time t during the test period of the corrosion test Zi The corrosion index Q(t Zi ,W Z ) is calculated in advance, and the maximum value Q of the corrosiveness index estimated in the extreme value estimation step is calculated in this relational expression. max (t Fi ) is introduced, Evaluation Period and obtaining an estimated value of the corrosion amount of the steel material in the steel material. The above formula is based on the relationship between the amount of corrosion actually measured from the corrosion test of steel and the corrosion index Q(t Zi ,W Z ) may be expressed as a relational expression of the logarithm of the maximum value of [2] In the extreme value estimation step and the corrosion amount estimation step, the corrosiveness index Q(t Xi ,W X ) The calculation step comprises: The observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi ) and a first step of obtaining at least a rainfall amount; The observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X ) according to the following formula (1): The observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X ) according to the following formula (2): The observation time t Xi For each, the corrosive index Q(t Xi ,W X and a fourth step of calculating the corrosion amount of a steel material based on the calculated corrosion amount by the following formula (3A): Q 1 (t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q 2 (t Xi ,W X )={Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=Q 2 (t Xi ,W X )·p(t Xi ) …(3A) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Xi ) is the observation time t Xi is the wind speed (m / s), and θw(t Xi ) is the observation time t when north is 0° Xi is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Xi ) is the corrosion loss coefficient at the observation time t Xi-1 ~t Xi If the amount of rainfall during the period exceeds the threshold, c(t Xi )=0, observation time t Xi-1 ~t Xi If the amount of rainfall during the period is less than the threshold, c(t Xi )=1. In equation (2), s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), s(tXi )=0, observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), s(t Xi )=1. p(t Xi ) is the corrosiveness expression coefficient at the observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), p(t Xi )=0, observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), p(t Xi )=1. [3] In the extreme value estimation step and the corrosion amount estimation step, the corrosiveness index Q(t Xi ,W X ) The calculation step comprises: The observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi ) and a first step of obtaining at least a rainfall amount; The observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X ) according to the following formula (1): The observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X ) according to the following formula (2): The observation time t Xi For each, the corrosive index Q(t Xi ,W X and a fourth step of calculating the corrosion amount of a steel material based on the calculated corrosion amount by the following formula (3B): Q 1 (t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|}2 …(1) Q 2 (t Xi ,W X )={Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tb Q 2 (t Xi ,W X )·p(t Xi ) …(3B) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Xi ) is the observation time t Xi is the wind speed (m / s), and θw(t Xi ) is the observation time t when north is 0° Xi is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Xi ) is the corrosion loss coefficient at the observation time t Xi-1 ~t Xi If the amount of rainfall during the period exceeds the threshold, c(t Xi )=0, observation time t Xi-1 ~t Xi If the amount of rainfall during the period is less than the threshold, c(t Xi )=1. In equation (2), s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), s(t Xi )=0, observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), s(t Xi )=1. In formula (3B), Tb is any temperature in the range of Temp (°C) to (Temp+2)°C, where Temp (°C) is the air temperature at sunrise. p(t Xi ) is the corrosiveness expression coefficient at the observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), p(t Xi )=0, observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), p(t Xi )=1. [4] In the extreme value estimation step and the corrosion amount estimation step, the corrosiveness index Q(t Xi ,W X ) The calculation step comprises: The observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi ) and a first step of obtaining at least a rainfall amount; The observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X ) according to the following formula (1): The observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X ) according to the following formula (2): The observation time t Xi For each, the corrosive index Q(t Xi ,W X and a fourth step of calculating the corrosion amount of a steel material based on the calculated corrosion amount by the following formula (3C): Q 1 (t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q 2 (t Xi ,W X )={Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tc Q 2 (t Xi ,W X )·p(t Xi ) …(3C) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Xi ) is the observation time t Xi is the wind speed (m / s), and θw(t Xi ) is the observation time t when north is 0° Xi is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Xi ) is the corrosion loss coefficient at the observation time t Xi-1 ~t Xi If the amount of rainfall during the period exceeds the threshold, c(t Xi )=0, observation time t Xi-1 ~t Xi If the amount of rainfall during the period is less than the threshold, c(t Xi )=1. In equation (2), s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi The amount of moisture adhering to the steel surface ws(t Xi ) is 0, then s(t Xi )=0, and Xi ) is greater than 0, s(t Xi )=1. Xi ) is the observation time t XiIt is an estimated value obtained by subtracting the amount of water evaporation from the amount of moisture adhered due to rainfall and condensation at the steel surface, and is calculated from the surface temperature of the steel, air temperature (℃), air pressure (hPa), wind speed (m / s) at the steel surface, and relative humidity (%). Tc in formula (3C) is the observation time t Xi The amount of moisture on the steel surface ws(t Xi ) is the temperature (℃) at which it becomes 0. p(t Xi ) is the corrosiveness expression coefficient, and Xi ) exceeds 0, p(t Xi )=0, and Xi ) is less than or equal to 0, then p(t Xi )=1. [5] A method for predicting the amount of corrosion of a steel material, as described in any one of [1] to [4], characterized in that the steel material is a steel material made of stainless steel. [6] As an index for evaluating the amount of corrosion when steel is exposed outdoors, the observation time t Xi (where i is an integer from 1 to n) Meteorological observation values ​​W of wind speed, wind direction, and rainfall for each X The corrosion index Q(t Xi ,W X ) is used in a computer-based steel corrosion prediction system, The observation point X is a meteorological observation value W M Based on the past observation time t Mi The corrosion index Q(t Mi ,W M ) and calculate the observation time t M1 ~t Mn Corrosion index Q(t Mi ,W M ) time series data, extreme value statistical analysis is performed to obtain the future evaluation period t F1 ~t Fn The maximum value of the corrosive index Q max (t Fi ) and an extreme value estimation unit that estimates The actual corrosion amount obtained from the corrosion test of the steel material at the test point Z and the meteorological observation value W at the observation point X at the test point Z Z Based on the observation time t during the test period of the corrosion test Zi The corrosion index Q(t Zi ,W Z ) is calculated in advance, and the maximum value Q of the corrosiveness index estimated in the extreme value estimation unit is added to this relationship. max (t Fi ) is introduced, Evaluation Period and a corrosion amount estimation unit that obtains an estimated value of the corrosion amount of the steel material in the steel material. The above formula is based on the relationship between the amount of corrosion actually measured from the corrosion test of steel and the corrosion index Q(t Zi ,W Z ) may be expressed as a relational expression of the logarithm of the maximum value of [7] The extreme value estimation unit and the corrosion amount estimation unit each include the corrosiveness index Q(t Xi ,W X ) is provided, The calculation unit is The observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi A first processing unit that acquires at least the rainfall amount and the weather information; The observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X A second processing unit that calculates the following formula (1): The observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X A third processing unit that calculates the following equation (2): The observation time t Xi For each, the corrosive index Q(t Xi ,W X ) using the following formula (3A): Q 1 (t Xi,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q 2 (t Xi ,W X )={Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=Q 2 (t Xi ,W X )·p(t Xi ) …(3A) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Xi ) is the observation time t Xi is the wind speed (m / s), and θw(t Xi ) is the observation time t when north is 0° Xi is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Xi ) is the corrosion loss coefficient at the observation time t Xi-1 ~t Xi If the amount of rainfall during the period exceeds the threshold, c(t Xi )=0, observation time t Xi-1 ~t Xi If the amount of rainfall during the period is less than the threshold, c(t Xi )=1. In equation (2), s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), s(t Xi )=0, observation time t Xi-1 ~tXi If the period is nighttime (from sunset to sunrise), s(t Xi )=1. p(t Xi ) is the corrosiveness expression coefficient at the observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), p(t Xi )=0, observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), p(t Xi )=1. [8] The extreme value estimation unit and the corrosion amount estimation unit each include the corrosiveness index Q(t Xi ,W X ) is provided, The calculation unit is The observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi A first processing unit that acquires at least the rainfall amount and the weather information; The observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X A second processing unit that calculates the following formula (1): The observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X A third processing unit that calculates the following equation (2): The observation time t Xi For each, the corrosive index Q(t Xi ,W X ) using the following formula (3B): Q 1 (t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q 2 (tXi ,W X )={Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tb Q 2 (t Xi ,W X )·p(t Xi ) …(3B) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Xi ) is the observation time t Xi is the wind speed (m / s), and θw(t Xi ) is the observation time t when north is 0° Xi is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Xi ) is the corrosion loss coefficient at the observation time t Xi-1 ~t Xi If the amount of rainfall during the period exceeds the threshold, c(t Xi )=0, observation time t Xi-1 ~t Xi If the amount of rainfall during the period is less than the threshold, c(t Xi )=1. In equation (2), s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), s(t Xi )=0, observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), s(t Xi )=1. In formula (3B), Tb is any temperature in the range of Temp (°C) to (Temp+2)°C, where Temp (°C) is the air temperature at sunrise. p(t Xi ) is the corrosiveness expression coefficient at the observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), p(t Xi )=0, observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), p(t Xi )=1. [9] The extreme value estimation unit and the corrosion amount estimation unit each include the corrosiveness index Q(t Xi ,W X ) is provided, The calculation unit is The observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi A first processing unit that acquires at least the rainfall amount and the weather information; The observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X A second processing unit that calculates the following formula (1): The observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X A third processing unit that calculates the following equation (2): The observation time t Xi For each, the corrosive index Q(t Xi ,W X ) using the following formula (3C): Q 1 (t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q 2(t Xi ,W X )={Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tc Q 2 (t Xi ,W X )·p(t Xi ) …(3C) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Xi ) is the observation time t Xi is the wind speed (m / s), and θw(t Xi ) is the observation time t when north is 0° Xi is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Xi ) is the corrosion loss coefficient at the observation time t Xi-1 ~t Xi If the amount of rainfall during the period exceeds the threshold, c(t Xi )=0, observation time t Xi-1 ~t Xi If the amount of rainfall during the period is less than the threshold, c(t Xi )=1. In equation (2), s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi The amount of moisture adhering to the steel surface ws(t Xi ) is 0, then s(t Xi )=0, and Xi ) is greater than 0, s(t Xi )=1. Xi ) is the observation time t XiIt is an estimated value obtained by subtracting the amount of water evaporation from the amount of moisture adhered due to rainfall and condensation at the steel surface, and is calculated from the surface temperature of the steel, air temperature (℃), air pressure (hPa), wind speed (m / s) at the steel surface, and relative humidity (%). Tc in formula (3C) is the observation time t Xi The amount of moisture on the steel surface ws(t Xi ) is the temperature (℃) at which it becomes 0. p(t Xi ) is the corrosiveness expression coefficient, and Xi ) exceeds 0, p(t Xi )=0, and Xi ) is less than or equal to 0, then p(t Xi )=1.

[10] A system for predicting the amount of corrosion of steel material, as described in any one of [6] to [9], characterized in that the steel material is made of stainless steel.

[11] As an index for evaluating the amount of corrosion when steel is exposed outdoors, the observation time t Xi (where i is an integer from 1 to n) Meteorological observation values ​​W of wind speed, wind direction, and rainfall for each X The corrosion index Q(t Xi ,W X ) is used in a computer to predict the corrosion amount of steel material, The observation point X is a meteorological observation value W M Based on the past observation time t Mi The corrosion index Q(t Mi ,W M ) and calculate the observation time t M1 ~t Mn Corrosion index Q(t Mi ,W M ) time series data, extreme value statistical analysis is performed to obtain the future evaluation period t F1 ~t Fn The maximum value of the corrosive index Q max (t Fi ) and an extreme value estimation step. The actual corrosion amount obtained from the corrosion test of the steel material at the test point Z and the meteorological observation value W at the observation point X at the test point Z Z Based on the observation time t during the test period of the corrosion test Zi The corrosion index Q(t Zi ,W Z ) is calculated in advance, and the maximum value Q of the corrosiveness index estimated in the extreme value estimation step is calculated in this relational expression. max (t Fi ) is introduced, Evaluation Period and a corrosion amount estimating step of obtaining an estimated value of the corrosion amount of the steel material in the steel material. The above formula is based on the relationship between the amount of corrosion actually measured from the corrosion test of steel and the corrosion index Q(t Zi ,W Z ) may be expressed as a relational expression of the logarithm of the maximum value of

[12] In the extreme value estimation step and the corrosion amount estimation step, the corrosiveness index Q(t Xi ,W X ) The calculation step comprises: The observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi ) and a first step of obtaining at least a rainfall amount; The observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X ) according to the following formula (1): The observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X ) according to the following formula (2): The observation time t Xi For each, the corrosive index Q(t Xi ,W X and a fourth step of calculating the corrosion amount of steel by the following formula (3A). Q1 (t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q 2 (t Xi ,W X )={Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t XiS-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=Q 2 (t Xi ,W X )·p(t Xi ) …(3A) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Xi ) is the observation time t Xi is the wind speed (m / s), and θw(t Xi ) is the observation time t when north is 0° Xi is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Xi ) is the corrosion loss coefficient at the observation time t Xi-1 ~t Xi If the amount of rainfall during the period exceeds the threshold, c(t Xi )=0, observation time t Xi-1 ~t Xi If the amount of rainfall during the period is less than the threshold, c(t Xi )=1. In equation (2), s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), s(t Xi)=0, observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), s(t Xi )=1. p(t Xi ) is the corrosiveness expression coefficient at the observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), p(t Xi )=0, observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), p(t Xi )=1.

[13] In the extreme value estimation step and the corrosion amount estimation step, the corrosiveness index Q(t Xi ,W X ) The calculation step comprises: The observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi ) and a first step of obtaining at least a rainfall amount; The observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X ) according to the following formula (1): The observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X ) according to the following formula (2): The observation time t Xi For each, the corrosive index Q(t Xi ,W X and a fourth step of calculating the corrosion amount of steel by the following formula (3B). Q 1 (t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|}2 …(1) Q 2 (t Xi ,W X )={Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t XiS-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tb Q 2 (t Xi ,W X )·p(t Xi ) …(3B) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Xi ) is the observation time t Xi is the wind speed (m / s), and θw(t Xi ) is the observation time t when north is 0° Xi is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Xi ) is the corrosion loss coefficient at the observation time t Xi-1 ~t Xi If the amount of rainfall during the period exceeds the threshold, c(t Xi )=0, observation time t Xi-1 ~t Xi If the amount of rainfall during the period is less than the threshold, c(t Xi )=1. In equation (2), s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), s(t Xi )=0, observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), s(t Xi )=1. In formula (3B), Tb is any temperature in the range of Temp (°C) to (Temp+2)°C, where Temp (°C) is the air temperature at sunrise. p(t Xi ) is the corrosiveness expression coefficient at the observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), p(t Xi )=0, observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), p(t Xi )=1.

[14] In the extreme value estimation step and the corrosion amount estimation step, the corrosiveness index Q(t Xi ,W X ) The calculation step comprises: The observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi ) and a first step of obtaining at least a rainfall amount; The observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X ) according to the following formula (1): The observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X ) according to the following formula (2): The observation time t Xi For each, the corrosive index Q(t Xi ,W X and a fourth step of calculating the corrosion amount of steel by the following formula (3C). Q 1 (t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1) Q 2 (t Xi ,W X )={Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t XiS-1 )}·c(t Xi ) …(2) Q(t Xi ,W X )=10 0.04Tc Q 2 (t Xi ,W X )·p(t Xi ) …(3C) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Xi ) is the observation time t Xi is the wind speed (m / s), and θw(t Xi ) is the observation time t when north is 0° Xi is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Xi ) is the corrosion loss coefficient at the observation time t Xi-1 ~t Xi If the amount of rainfall during the period exceeds the threshold, c(t Xi )=0, observation time t Xi-1 ~t Xi If the amount of rainfall during the period is less than the threshold, c(t Xi )=1. In equation (2), s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi The amount of moisture adhering to the steel surface ws(t Xi ) is 0, then s(t Xi )=0, and Xi ) is greater than 0, s(t Xi )=1. Xi ) is the observation time t XiIt is an estimated value obtained by subtracting the amount of water evaporation from the amount of moisture adhered due to rainfall and condensation at the steel surface, and is calculated from the surface temperature of the steel, air temperature (℃), air pressure (hPa), wind speed (m / s) at the steel surface, and relative humidity (%). Tc in formula (3C) is the observation time t Xi The amount of moisture on the steel surface ws(t Xi ) is the temperature (℃) at which it becomes 0. p(t Xi ) is the corrosiveness expression coefficient, and Xi ) exceeds 0, p(t Xi )=0, and Xi ) is less than or equal to 0, then p(t Xi )=1.

[15] A program for predicting the amount of corrosion of steel material, as described in any one of

[11] to

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

[16] A prediction step of predicting the amount of corrosion of a steel material at a planned utilization point M in the future by the method for predicting the amount of corrosion of a steel material according to any one of [1] to [5]; A method for proposing a steel product, comprising a step in which a sales representative presents to a customer the predicted value of the amount of corrosion obtained by the prediction step or an image of the steel product surface corresponding to the predicted value of the amount of corrosion.

[17] A prediction step of predicting the future corrosion amount of steel materials at a planned utilization point M for each of a plurality of types of steel materials by the method for predicting the corrosion amount of steel materials according to any one of [1] to [5]; A method for proposing steel products comprising a step in which a sales representative presents to a customer the predicted value of the amount of corrosion for each steel product obtained by the prediction step or an image of the steel product surface corresponding to the predicted value of the amount of corrosion.

[18] The corrosion prediction is a rating number rounded to at least one decimal place,

[16] or [ 17 ] A method for proposing steel materials.

[19] The image of the steel surface corresponding to the predicted corrosion amount is an image corresponding to a rating number at least to one decimal place,

[16] or [ 17] A method for proposing steel materials. Effect of the Invention

[0009] In the method for predicting the amount of corrosion of a steel material, the system for predicting the amount of corrosion of a steel material, and the program for predicting the amount of corrosion of a steel material according to the present invention, Mi Corrosiveness index Q per M (t Mi ,W M ) time series data, extreme value statistical analysis is performed to estimate the future evaluation period t F1 ~t Fn The maximum value of the corrosive index Q max (t Fi Next, the actual corrosion amount of the steel material, which was previously obtained from the results of the corrosion test, and the corrosion index Q(t Zi ,W Z ) is expressed as the maximum value of the corrosive index Q max (t Fi Since the present invention estimates the future corrosion amount of steel materials in this way, it is possible to improve the accuracy of prediction of the corrosion amount compared to the conventional method of predicting the corrosion amount based on the annual average value. The method, system, and program for predicting the amount of corrosion of steel material according to the present invention predict the future amount of corrosion of steel material at the planned utilization point M based on the results of a corrosion test of steel material at the test point Z. In the present invention, the actual corrosion amount of steel material at the test point Z and the corrosivity index Q(t Zi ,W Z ) and the meteorological observation value W at the planned use point M M The corrosivity index Q(t Mi ,W M ) time series data is required. Therefore, the present invention is highly versatile.

[0010] In addition, according to the method for predicting the corrosion amount of steel material, the system for predicting the corrosion amount of steel material, and the program for predicting the corrosion amount of steel material of the present invention, the primary corrosivity index Q 1 (t Xi ,W X ) and secondary corrosivity index Q 2 (t Xi ,WX ) and then the secondary corrosivity index Q 2 (t Xi ,W X ) to the corrosion index Q(t Xi ,W X ) is calculated. Here, the primary corrosivity index Q 1 (t Xi ,W X ) is an index of the amount of salt attached to the steel material, and the wind speed u(t Xi ) and wind directions θs, θw(t Xi ) is calculated based on the secondary corrosivity index Q 2 (t Xi ,W X ) is the observation time t Xi The observation time t Xi-1 Secondary corrosivity index Q 2 (t Xi-1 ,W X ) at observation time t Xi Primary corrosivity index Q 1 (t Xi ,W X ) is added, and the corrosion loss coefficient c(t Xi ) and the corrosion accumulation coefficient s(t Xi ) and the corrosion index Q(t Xi ,W X ) is the corrosion manifestation coefficient p(t Xi ) and this corrosiveness expression coefficient p(t Xi ) is the secondary corrosivity index Q 2 (t Xi ,W X ) is calculated. This gives the corrosion index Q(t Xi ,W X ) can be obtained, and by using this, the accuracy of predicting the amount of corrosion can be improved.

[0011] The method for proposing steel materials of the present invention predicts the future corrosion amount of steel materials at the planned usage point M using the method for predicting the corrosion amount of steel materials of the present invention, and the sales representative presents the obtained predicted value of the corrosion amount to the customer. In this way, the present invention can meet the customer's request to know the predicted value of the future corrosion amount when the steel material is used in an outdoor environment for a long period of time. Furthermore, since the present invention can provide a highly accurate predicted value of the corrosion amount, it is possible to recommend an appropriate steel material to the customer.

[0012] Furthermore, according to the method for proposing steel materials of the present invention, the future corrosion amount of steel materials at the planned usage point M is predicted for each of a plurality of types of steel materials by the method for predicting the amount of corrosion of steel materials of the present invention, and the sales representative presents the obtained predicted value of the amount of corrosion for each steel material to the customer. In this way, the present invention can meet the customer's desire to know the predicted value of the future amount of corrosion when steel materials are used in an outdoor environment for a long period of time. Furthermore, since the present invention can provide a highly accurate predicted value of the amount of corrosion for each type of steel, it is possible to select an appropriate steel material and recommend it to the customer. [Brief description of the drawings]

[0013] [Figure 1] 1 is a schematic diagram illustrating a corrosion amount prediction system for a steel material according to an embodiment of the present invention; [Diagram 2] FIG. 2 is a schematic diagram illustrating a corrosion amount prediction program for a steel material according to an embodiment of the present invention. [Diagram 3] 1 is a graph showing an example of a change in the corrosive index Q(tMi,WM) over time. [Figure 4A] 1 is a graph showing an example of time series data of the corrosive index Q(tMi,WM) between observation times tM1 to tMn. [Figure 4B] 1 is a graph showing the maximum value Qmax(tFi) of the corrosiveness index within future evaluation periods tF1 to tFn. [Diagram 5] Graph showing the relationship between the logarithm of the maximum value QRmax and the measured amount of corrosion (RN) for SUS304. [Figure 6] 4 is a graph showing an example of output of the method for estimating the amount of corrosion according to the present embodiment. [Figure 7]Graph showing the relationship between the logarithm of the maximum value QRmax and the measured amount of corrosion (RN) for SUS304. [Figure 8] Graph showing the relationship between the logarithm of the maximum value QRmax after temperature correction and the measured amount of corrosion (RN) for SUS304. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Steel materials placed in outdoor environments are subject to the influence of wind and other factors, which cause corrosion-promoting substances, such as salt, to adhere to the steel material surface, causing gradual corrosion. General buildings such as buildings and civil engineering structures such as bridges and towers are required to be durable over a long period of time in outdoor environments. Therefore, it is preferable that the future corrosion rate of steel materials used in these buildings and structures can be predicted. As a conventional technique, a method for predicting the amount of corrosion based on annual wet hours, annual average wind speed, annual average temperature, etc. has been known.

[0015] However, corrosion of steel materials can progress rapidly when salt is attached to the material, and can be significantly affected by sudden weather changes. Therefore, conventional predictions based on annual average values ​​do not take into account short-term, sudden weather changes that can have a significant impact on steel corrosion due to the structure of the data. Therefore, there is room for improving the accuracy of corrosion predictions compared to conventional prediction methods.

[0016] In other words, the amount of corrosion of steel materials placed in outdoor environments is affected by the amount of salt that adheres to the steel materials. Since salt is mainly carried to steel materials by winds blowing from the coast, it was previously thought that the amount of corrosion could be predicted to some extent from the monthly and annual average wind speeds and the trends in wind direction. However, when corrosion tests were actually conducted by leaving steel materials in outdoor environments, there were cases where the amount of corrosion could not be explained by only considering the monthly and annual average wind speeds and wind direction.

[0017] Also, corrosion due to salt adhesion may progress more rapidly in steel materials made of stainless steel than in ordinary steel, etc. Furthermore, stainless steel is often used unpainted for the exterior walls of buildings, etc., due to its excellent design properties, and is therefore more susceptible to the effects of salt than ordinary steel, etc.

[0018] Therefore, there is a need to develop a method that can accurately predict the amount of corrosion of steel materials.

[0019] As a result of the inventors' intensive study, they found that the weather conditions under which corrosion of steel materials left outdoors is likely to progress are those under which there is no rainfall between night and day. That is, when condensation water adheres to the steel material surface as the temperature drops at night, salt carried by the sea breeze blowing from the coast direction dissolves in the condensation water. Then, they found that the salt-containing condensation water evaporates due to sunlight during the day, concentrating the salt on the steel material surface, which makes corrosion more likely to occur. In particular, they found that a large amount of salt is likely to adhere to the steel material surface when a strong wind blows from the direction of the nearest coast due to the approach of a typhoon, hurricane, cyclone, etc., or the influence of atmospheric pressure. They also found that unless there is rainfall, salt continues to accumulate on the steel material surface at night, which makes corrosion more likely to occur. On the other hand, they found that if there is rainfall, the salt is washed away from the steel material surface by rainwater, making it difficult for corrosion to progress. Then, taking such weather conditions into consideration, they found that the observation time t Xi Meteorological observations of wind speed, wind direction and rainfall for each day X The corrosivity index Q(t Xi ,W X ) we have found a method to predict the amount of corrosion of steel in the future with high accuracy.

[0020] Regarding the presence or absence of sunlight, in the present invention, Xi In order to determine whether it is daytime or nighttime, the sunshine duration is calculated based on the meteorological observation value W X In order to determine whether or not there is sunlight, the sunrise and sunset times are calculated from the latitude, longitude, and altitude of the observation point X, and the observation time t XiIt may be determined whether the time period is daytime or nighttime, as will be described in more detail below.

[0021] Furthermore, in order to accurately predict the amount of corrosion of steel materials, it is necessary to refer to the results of corrosion tests on the steel materials. Conventional prediction methods are limited to predicting the amount of corrosion at the point where the corrosion test was performed. In the corrosion prediction method of the present invention, even if the test point Z of the corrosion test and the planned use point M of the steel materials are different locations, the corrosion index Q(t Xi ,W X ) can be used to predict with high accuracy the future amount of corrosion at the planned utilization point M based on the results of the corrosion test at the test point Z.

[0022] Hereinafter, an embodiment of the present invention will be described.

[0023] In this embodiment, the steel material to be predicted for the amount of corrosion is not particularly limited, and may be a steel material made of ordinary steel (carbon steel), alloy steel, special steel, or the like. Also, plated steel material obtained by plating these steel materials may be used. There is no particular limit to the type of plating, and any plating generally used for steel may be used. Also, the steel material may be a steel material made of stainless steel.

[0024] Furthermore, the steel material according to this embodiment is not limited to use as a building material, but can be suitably used for predicting the amount of corrosion of steel material used outdoors.

[0025] (Steel corrosion prediction system and steel corrosion prediction program) First, a corrosion degree prediction system and a corrosion degree prediction program for steel materials according to the present embodiment will be described.

[0026] The corrosion amount prediction system of the present embodiment includes an extreme value estimation unit and a corrosion amount estimation unit, as shown in Fig. 1. The extreme value estimation unit and the corrosion amount estimation unit are realized as functions provided in a central processing unit of a computer, for example.

[0027] The extreme value estimation part estimates the weather observation value W MBased on the past observation time t Mi The corrosion index Q(t Mi ,W M ) and calculate the observation time t M1 ~t Mn Corrosion index Q(t Mi ,W M ) time series data, extreme value statistical analysis is performed to obtain the future evaluation period t F1 ~t Fn The maximum value of the corrosive index Q max (t Fi ) is capable of predicting

[0028] The corrosion amount estimation unit is a unit that estimates the amount of corrosion by comparing the actual corrosion amount obtained from the corrosion test of the steel material at the test point Z with the meteorological observation value W Z Based on the observation time t during the corrosion test Zi The corrosion index Q(t Zi ,W Z ) is expressed as the maximum value of the corrosive index Q max (t Fi ) to obtain an estimated value of the amount of corrosion of the steel material in the estimation period. The corrosion amount estimation unit is provided with the above-mentioned relational expression that has been calculated in advance. The above formula is based on the relationship between the amount of corrosion actually measured from the corrosion test of steel and the corrosion index Q(t Zi ,W Z ) may be expressed as a relational expression of the logarithm of the maximum value of

[0029] In addition, the extreme value estimation part and the corrosion amount estimation part are each equipped with the corrosivity index Q(t Mi ,W M ), Q(t Zi ,W Z ) may be provided.

[0030] The calculation part includes the observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi ) and a first processing unit that acquires at least the amount of rainfall and an observation time t Xi Each time, the primary corrosivity index Q1 (t Xi ,W X ), and a second processing unit that calculates the observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X ), and the third processing unit calculates the observation time t Xi For each, the corrosive index Q(t Xi ,W X and a fourth processing unit that calculates

[0031] In addition, in the extreme value estimation section, the corrosive index Q(t Mi ,W M In the calculation section for calculating W, the point X is the planned use point M of the steel material, and W X W M Let t Xi A Mi Let u(t Xi ) to u(t Mi ) and θw(t Xi ) to θw(t Mi ) and c(t Xi ) to c(t Mi ), and s(t Xi ) to s(t Mi ), and p(t Xi ) to p(t Mi )

[0032] In addition, the corrosion index Q(t Zi ,W Z In the calculation section for determining the stress distribution, point X is set as the steel test point Z, and W X W Z Let t Xi A Zi Let u(t Xi ) to u(t Zi ) and θw(t Xi ) to θw(t Zi ) and c(t Xi ) to c(t Zi ), and s(t Xi ) to s(t Zi ), and p(t Xi ) to p(t Zi )

[0033] The corrosion amount prediction system of this embodiment generates an estimated value of the corrosion amount of a steel material by an extreme value estimation unit and a corrosion amount estimation unit, and outputs this estimated value.

[0034] Next, as shown in Fig. 2, the corrosion amount prediction program of this embodiment includes an extreme value estimation step and a corrosion amount estimation step. The corrosion amount prediction program of this embodiment is executed in a computer. The computer equipped with the corrosion amount prediction program sequentially executes the extreme value estimation step and the corrosion amount estimation step to output an estimated value of the corrosion amount of a steel material. The corrosion amount prediction program can also be used as a program for the above-mentioned corrosion amount prediction system.

[0035] The extreme value estimation step of the corrosion prediction program is based on meteorological observation value W M Based on the past observation time t Mi The corrosion index Q(t Mi ,W M ) is then calculated for the observation time t M1 ~t Mn Corrosion index Q(t Mi ,W M ) time series data, extreme value statistical analysis is performed to obtain the future evaluation period t F1 ~t Fn The maximum value of the corrosive index Q max (t Fi The extreme value estimation step may be performed in the extreme value estimation unit shown in FIG.

[0036] In addition, the corrosion amount prediction step of the corrosion amount prediction program is based on the actual corrosion amount obtained from the corrosion test of the steel material at the test point Z and the meteorological observation value W Z Based on the observation time t during the corrosion test Zi The corrosion index Q(t Zi ,W Z ) is expressed as the maximum value of the corrosivity index Q max (t Fi) is introduced to obtain an estimated value of the amount of corrosion of the steel material during the estimated period. The above relational expression is one that has been determined in advance. The above relational expression is calculated by dividing the actual amount of corrosion obtained from the corrosion test of the steel material by the corrosion index Q(t Zi ,W Z The corrosion amount estimation step may be performed in the corrosion amount estimation unit shown in FIG.

[0037] In addition, the extreme value estimation step and the corrosion amount estimation step each use the corrosiveness index Q(t Mi ,W M ), Q(t Zi ,W Z ) may be included.

[0038] The calculation steps may include a first step, a second step, a third step, and a fourth step. The first step is to calculate the time t Xi Wind speed u(t Xi ), wind direction θw(t Xi ) and rainfall amount. The second step is to obtain the observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X The third step is to calculate the Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X The fourth step is to calculate the Xi For each, the corrosive index Q(t Xi ,W X ) to calculate

[0039] In addition, in the extreme value estimation step, the corrosive index Q(t Mi ,W M In the calculation step for calculating W, the point X is the planned use point M of the steel material, and X W M Let t Xi A Mi Let u(t Xi ) to u(t Mi ) and θw(tXi ) to θw(t Mi ) and c(t Xi ) to c(t Mi ), and s(t Xi ) to s(t Mi ), and p(t Xi ) to p(t Mi )

[0040] In addition, in the corrosion amount estimation step, the corrosiveness index Q(t Zi ,W Z In the calculation step for finding the stress distribution, point X is set as the steel test point Z, and W X W Z Let t Xi A Zi Let u(t Xi ) to u(t Zi ) and θw(t Xi ) to θw(t Zi ) and c(t Xi ) to c(t Zi ), and s(t Xi ) to s(t Zi ), and p(t Xi ) to p(t Zi )

[0041] A detailed explanation of the operation of the extreme value estimation unit, corrosion amount estimation unit, and calculation unit of the corrosion amount prediction system of this embodiment, as well as the operation of the extreme value estimation step, corrosion amount estimation step, and calculation step of the corrosion amount prediction program will be provided in the explanation of the method for predicting the corrosion amount of steel material.

[0042] The input values ​​in the corrosion amount prediction system and the corrosion amount prediction program of this embodiment are at least the meteorological observation value W Z , the meteorological observation value W at the planned use point M of the steel material M, includes the amount of corrosion actually measured by corrosion tests. The output value is a predicted value of the future amount of corrosion when the steel is exposed outdoors at the planned site M of use. The amount of corrosion that can be used includes corrosion weight loss, corrosion depth, and the RN value (RN: rating number), which is an appearance rating for corrosion. For stainless steel, the RN value can be evaluated using JIS G 0595:2004 (Method of evaluating the degree of surface rust formation on stainless steel).

[0043] (Method of predicting the amount of corrosion of steel materials) Next, a method for predicting the corrosion amount of a steel material according to this embodiment will be described.

[0044] In the method for predicting the amount of corrosion of a steel material according to the present embodiment, an index for evaluating the amount of corrosion when the steel material is exposed outdoors is set to an observation time t Xi (where i is an integer from 1 to n) Meteorological observation values ​​W of wind speed, wind direction, and rainfall for each X The corrosion index Q(t Xi ,W X ) is used.

[0045] The method for predicting the amount of corrosion of a steel material according to this embodiment is to estimate the maximum value Q max (t Fi ) and a corrosion amount estimation step of estimating the amount of corrosion of the steel material at the planned utilization point M after the estimated period has elapsed.

[0046] The extreme value estimation step of the above prediction method is to estimate the extreme value W M Based on the past observation time t Mi The corrosion index Q(t Mi ,W M ) and calculate the observation time t M1 ~t Mn Corrosion index Q(t Mi ,W M ) time series data, extreme value statistical analysis is performed to obtain the future evaluation period t F1 ~t Fn The maximum value of the corrosive index Q max (t Fi ) to guess.

[0047] The corrosion amount estimation step of the above prediction method is to estimate the amount of corrosion of the steel material by using the measured corrosion amount and the corrosion index Q(t Zi ,W Z ) is expressed as the maximum value of the corrosivity index Q max (t Fi ) is introduced to estimate the amount of corrosion of steel after the estimated period has elapsed.

[0048] The measured amount of corrosion of steel and the corrosion index Q(t Zi ,W Z It is preferable to obtain a relational expression in advance. This relational expression is expressed by the relationship between the actual corrosion amount obtained from a corrosion test of steel material at test point Z and the meteorological observation value W Z Based on the observation time t during the corrosion test Zi The corrosion index Q(t Zi ,W Z The above relational expression is based on the relationship between the amount of corrosion actually measured from the corrosion test of steel and the corrosion index Q(t Zi ,W Z ) may be expressed as a relational expression of the logarithm of the maximum value of

[0049] In addition, the extreme value estimation step and the corrosion amount estimation step each use the corrosiveness index Q(t Mi ,W M ), Q(t Zi ,W Z ) may be included.

[0050] First, in order to explain the method for evaluating the corrosion amount of a steel material according to the present embodiment, the corrosion index Q(t Xi ,W X ) will be explained.

[0051] In the method for predicting the amount of corrosion of a steel material according to the present embodiment, the following is used as an index for evaluating the amount of corrosion when the steel material is exposed outdoors: Xi (where i is an integer from 1 to n) XThe corrosion index Q(t Xi ,W X ) is used.

[0052] As mentioned above, the inventors have found that weather conditions in which steel corrosion is likely to occur are when wind is observed between night and day, but no rainfall occurs. Under these weather conditions, condensation water forms on the steel surface during the night between sunset and sunrise, and salt carried by the wind dissolves into the condensation water. The condensation water then evaporates due to sunlight during the day, concentrating the salt, which results in the progression of corrosion. Salt accumulates as days pass without rainfall. On the other hand, when rainfall occurs, salt disappears from the steel surface. The corrosivity index Q(t Xi ,W X ) is an index of the amount of salt on the steel surface taking such weather changes into account.

[0053] In addition, the corrosive index Q(t Xi ,W X ) used to calculate the corrosion accumulation factor s(t i ) and the corrosive expression factor p(t Xi ) will be explained. i ) and p(t Xi ) is a value related to the wetness of the steel surface, and can be 0 or 1. Whether the value is 0 or 1 depends on the observation time t Xi This depends on whether it is daytime or nighttime.

[0054] That is, at night, the steel surface becomes wet due to the adhesion of condensed water. On the other hand, during the day, the condensed water that adhered at night evaporates, and the steel surface is no longer wet. Therefore, in this embodiment, whether the steel surface is wet or not is indirectly determined depending on whether it is daytime or nighttime, and s(t i ) and p(t Xi ) shall be determined.

[0055] In this embodiment, the period from sunset to sunrise is defined as nighttime, and the period from sunrise to sunset is defined as daytime.

[0056] Weather observation value W is used to determine whether it is daytime or nighttime. X If you want to calculate it from the weather observation value W X At the observation time t Xi-1 ~t Xi Obtain the sunshine duration between observation time t Xi When it is nighttime, there is no observation of the sunshine hours, so depending on the presence or absence of observations, the observation time t Xi This allows the weather observation value W X from the corrosive accumulation factor s(t i ) and the corrosiveness expression factor p(t Xi ) can be determined.

[0057] In addition, the sunrise and sunset times can be calculated from the date (year, month, day) and the longitude, latitude, and altitude of point X. Therefore, it is possible to determine whether it is daytime or nighttime from the calculated sunrise and sunset times, and the corrosive accumulation coefficient s(t i ) and the corrosiveness expression factor p(t Xi ) may be determined.

[0058] Below, the corrosivity index Q(t Xi ,W X In this embodiment, the primary corrosivity index Q is calculated by the following calculation steps, which are the first to fourth steps: 1 (t Xi ,W X ) and secondary corrosivity index Q 2 (t Xi ,W X ) and then the secondary corrosivity index Q 2 (t Xi ,W X ) to obtain the corrosivity index Q(t Xi ,W X ) is required.

[0059] In addition, the subscript X in various parameter symbols used in the description of this embodiment means an observation point X, and is intended to be replaced with M, which is an available point, or Z, which is a test point.Xi is the observation time t X1 , t X2 , t X3 , …t Xn is a generalized notation, and any observation time is expressed as observation time t Xi Also, the observation time t Xi ~t Xn The set whose elements are Xi ~t Xn It may be written as:

[0060] First, in the first step, the observation time t Xi Wind speed u(t Xi ), wind direction θw(t Xi ) and rainfall amount. Also, the observation time t Xi The sunshine hours may be obtained every t Xi ), wind direction θw(t Xi ), precipitation and sunshine hours are collectively called meteorological observation values ​​W X This may be the case.

[0061] Observation point X and meteorological observation value W in the first step X can be appropriately selected according to the stage of the forecasting method. That is, in the extreme value estimation step, the observation point X is set as the planned steel use point M, and the meteorological observation value W X Weather observation value W at the planned usage point M M In the corrosion amount estimation step, the observation point X is set as the test point Z, and the meteorological observation value W X The meteorological observation value W at the test point Z Z Let us assume that.

[0062] Weather Observation Value W X For example, the weather observation value W may be measured at a meteorological observation station managed by a public meteorological observation organization and published. For example, in the case of Japan, the weather observation value W may be measured at a meteorological observation station managed by the Japan Meteorological Agency and published. X is the data measured at a meteorological observation station near the observation point X. Also, the meteorological observation value W XThe data is not limited to those measured at meteorological observation stations managed by public meteorological observation agencies, but may be those measured at temporary meteorological observation facilities installed near the intended use point M of the steel material or the test point Z.

[0063] Weather observation period t at observation point X Xi ~t Xn The length of the period is not particularly limited, but is preferably as described below.

[0064] Past observation period t at the planned usage point M Mi ~t Mn The length of the observation period t may be, for example, any period between 1 year and 50 years, preferably between 5 years and 50 years, and more preferably between 10 years and 50 years. Mi ~t Mn The longer the observation period t, the more data will be included in the time series data used in the extreme value statistical analysis, and the more reliable the extreme value statistical analysis will be. Mi ~t Mn In the prediction method of the present embodiment, the future corrosion amount is predicted from the salt dispersion state by extreme value statistical analysis, but the amount of salt dispersion varies on an annual basis. For example, there are years when a lot of sea salt is dispersed and years when a little is dispersed. Mi ~t Mn If the observation period t is short and includes a year in which a lot of sea salt is dispersed, the future predicted corrosion amount may be higher. Mi ~t Mn The longer the better.

[0065] In addition, for the future evaluation period t F1 ~t Fn The length may be, for example, any period between one month and 100 years, and preferably any period between two years and 100 years.

[0066] Furthermore, in the corrosion amount estimation step, the observation period t Zi ~tZn The length of the period may be, for example, any period between one day and 30 years, preferably any period between one month and 10 years, or may be any period between one month and 5 years. At each observation time t X1 , …t Xn The observation interval may be, for example, every 10 minutes, every 15 minutes, or every 30 minutes. Zi ~t Zn is the corrosion index Q(t Zi ,W Z When determining the relationship between the maximum value of Q(t Zi ,W Z ) should be within a certain range. Zi ,W Z ) may be converted into a logarithm.

[0067] Wind speed u(t Xi ) is the observation time t Xi The instantaneous wind speed (m / s) at observation time t Xi-1 ~t Xi The average wind speed (m / s) during the period may also be used. Xi ) is the observation time t when the north direction is 0° Xi The wind direction at observation time t Xi The rainfall and sunshine hours at observation time t Xi-1 From observation time t Xi The accumulated rainfall and sunshine hours from the observation time t Xi If every 10 minutes, the observation time t Xi-1 ~t Xi The accumulated rainfall and accumulated sunshine hours for a 10-minute period are used.

[0068] Next, in the second step, at observation time t Xi Each time, the primary corrosivity index Q 1 (t Xi ,W X ) is calculated using the following formula (1).

[0069] Q 1(t Xi ,W X )=(d+1) -0.6 {u(t Xi )·cos|θs-θw(t Xi )|} 2 …(1)

[0070] In equation (1), u(t Xi ) is the observation time t Xi is the wind speed (m / s), and θw(t Xi ) is the observation time t Xi where θs is the direction of observation point X from the coast closest to observation point X, and ...

[0071] Furthermore, d in equation (1) is the distance (m) between observation point X and the coast closest to observation point X.

[0072] Primary Corrosivity Index Q 1 (t Xi ,W X ) is an index of the amount of salt attached to the steel material, and the wind speed u(t Xi ) and θs, θw(t Xi ) in formula (1). Xi )|" term is the wind speed u(t Xi ) is a coefficient for determining the wind speed component of the wind blowing from the coast direction. The amount of salt adhesion to the steel material is calculated by the wind speed u(t Xi ) squared. Furthermore, (d+1) -0.6 is the attenuation coefficient that takes into account the attenuation of sea salt that comes flying in due to the distance d from the coast. Therefore, the primary corrosivity index Q, which is an index of the amount of salt attached to the steel material, 1 (t Xi ,W X ) is as shown in the above formula (1).

[0073] Next, in the third step, at observation time t Xi Each time, the secondary corrosivity index Q 2 (t Xi ,W X ) is calculated using the following formula (2).

[0074] Q 2 (t Xi ,W X )={Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )}·c(t Xi ) …(2)

[0075] However, c(t Xi ) is the corrosion loss coefficient at the observation time t Xi-1 ~t Xi If the amount of rainfall during the period exceeds the threshold, c(t Xi )=0, observation time t Xi-1 ~t Xi If the amount of rainfall during the period is less than the threshold, c(t Xi ) = 1. The threshold value can be experimentally determined by conducting tests in advance, and is the amount of rainfall at which the salt attached to the steel surface disappears. The threshold value may be determined, for example, in the range of 0 to 5 mm / hour.

[0076] In addition, s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), s(t Xi )=0, observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), s(t Xi )=1. Corrosive accumulation coefficient s(t Xi The method for determining the weather data W X Alternatively, the time may be determined based on the sunrise time and the sunset time calculated based on the latitude, longitude, and altitude of the point X.

[0077] In equation (2), the observation time t Xi The observation time t Xi-1Secondary corrosivity index Q 2 (t Xi-1 ,W X ) at observation time t i Primary corrosivity index Q 1 (t Xi ,W X ) is added. In addition, the primary corrosivity index Q 1 (t Xi ,W X ) is the corrosion accumulation factor s(t i ) and (t Xi -t Xi-1 ) is multiplied. Therefore, Q, which is part of equation (2), 1 (t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 ) polynomials at observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), the value is 0. Xi-1 ~t Xi If the period is nighttime (the period from sunset to sunrise), the value will be greater than 0.

[0078] In addition, in equation (2), {Q 2 (t Xi-1 ,W X )+Q 1 (t Xi ,W X )·s(t Xi )·(t Xi -t Xi-1 )} polynomials, c(t Xi ) is multiplied. Therefore, Q 2 (t Xi ,W X ) is the observation time t Xi-1 ~t Xi If the amount of rainfall during the period is greater than the threshold, the value is set to 0. Xi-1 ~t Xi If the amount of rainfall between the two points is below the threshold, the value will be greater than 0.

[0079] Therefore, the secondary corrosivity index Q 2 (t Xi ,W X) is an indicator of the amount of salt that accumulates during the night, from sunset to sunrise, when condensation water forms on the surface of steel and salt carried by the wind dissolves into the condensation water.

[0080] Next, in the fourth step, at observation time t Xi For each, the corrosive index Q(t Xi ,W X ) is calculated according to the following formula (3A).

[0081] Q(t Xi ,W X )=Q 2 (t Xi ,W X )·p(t Xi ) …(3A)

[0082] p(t Xi ) is the corrosiveness expression coefficient at the observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), p(t Xi )=0, observation time t Xi-1 ~t Xi If the period is daytime (from sunrise to sunset), p(t Xi ) = 1. The corrosiveness expression coefficient p(t Xi The method for determining the weather data W X Alternatively, the time may be determined based on the sunrise time and the sunset time calculated based on the latitude, longitude, and altitude of the point X.

[0083] In equation (3A), Q 2 (t Xi ,W X ) to p(t Xi ) is multiplied. Therefore, Q(t Xi ,W X ) is the observation time t Xi-1 ~t Xi If the period is nighttime (from sunset to sunrise), it is set to 0. Xi-1 ~t Xi If the period is daytime (the period from sunrise to sunset), the value will be greater than 0.

[0084] Therefore, the corrosive index Q(t Xi ,W X ) is an indicator of the amount of salt that occurs when condensation forms on the surface of steel during the night, from sunset to sunrise, and salt carried by the wind dissolves into the condensation water, and then when the condensation water evaporates due to the sunlight during the day, the salt becomes concentrated. In general, the greater the amount of salt adhesion, the more susceptible the steel material is to corrosion. Xi ,W X ) is larger, the amount of corrosion of the steel tends to increase.

[0085] By performing the above calculation steps, time t Xi The corrosion index Q(t Xi ,W X ) is obtained. In the calculation step of the extreme value estimation step, the point X is the planned use point M of the steel material, and W X W M Let t Xi A Mi Let u(t Xi ) to u(t Mi ) and θw(t Xi ) to θw(t Mi ), and c(t Xi ) to c(t Mi ), and s(t Xi ) to s(t Mi ), and p(t Xi ) to p(t Mi ) to obtain the corrosion index Q(t Mi ,W M ) is obtained. In the calculation step of the corrosion amount estimation step, point X is set as the steel test point Z, and W X W Z Let t Xi A Zi Let u(t Xi ) to u(t Zi ) and θw(t Xi ) to θw(t Zi ), and c(t Xi ) to c(t Zi ), and s(t Xi ) to s(t Zi ), and p(t Xi) to p(t Zi ) to obtain the corrosion index Q(t Zi ,W Z ) is obtained.

[0086] The above calculation steps may be executed in an extreme value estimation unit or a corrosion amount estimation unit of the above-mentioned corrosion amount prediction system. Also, the first to fourth steps may be executed in first to fourth calculation units provided in the extreme value estimation unit or the corrosion amount estimation unit, respectively.

[0087] Next, the extreme value estimation step and the corrosion amount estimation step of the prediction method of the present embodiment will be described. Note that the extreme value estimation step and the corrosion amount estimation step may be executed by the extreme value estimation unit and the corrosion amount estimation unit of the above-mentioned corrosion amount prediction system.

[0088] (Extreme value estimation step) In the extreme value estimation step, first, the meteorological observation value W M Based on the past observation time t Mi The corrosion index Q(t Mi ,W M ) is calculated. Mi ,W M ) is calculated by the calculation steps described above.

[0089] This allows us to estimate the steel material usage time in the past period t M1 ~t Mn Observation time t included in M1 , t M2 , …t Mn For each, the corrosive index Q(t M1 ,W M ), Q(t M2 ,W M ), …Q(t Mn ,W M In this way, the observation time t M1 ~t Mn Corrosion index Q(t Mi ,W M ) time series data can be obtained.

[0090] Figure 3 shows the observation time t M1 ~t Mn Corrosion index Q(t Mi ,W M ) is a graph of the time series data. Mi is the horizontal axis, and the corrosiveness index Q(t Mi ,W M ) on the vertical axis. Figure 3 shows the corrosion index Q(t Mi ,W M ) is an example of time series data.

[0091] Next, in the extreme value estimation step, the previously obtained observation time t M1 ~t Mn Corrosion index Q(t Mi ,W M ) time series data, extreme value statistical analysis is performed to obtain the future evaluation period t F1 ~t Fn The maximum value of the corrosive index Q max (t Fi ) to guess.

[0092] Extreme value statistical analysis is a statistical method for estimating the probability of occurrence of large values ​​from given temporal and spatial data. M1 ~t Mn Corrosion index Q(t Mi ,W M ) data and extreme value statistical analysis, it is possible to determine the magnitude of the corrosive index Q(t Mi ,W M Specifically, the corrosive index Q(t Mi ,W M ) are sorted in descending order, and a Gumbel distribution probability paper is created assuming that the data distribution follows the Gumbel distribution. Then, using the Gumbel distribution probability paper, F1 ~t Fn The maximum value of the corrosive index Q max (t Fi ) to guess.

[0093] 4A and 4B show an example of the analysis result of the extreme value statistical analysis. By performing the extreme value statistical analysis, M1 ~t Mn Corrosion index Q(t Mi ,W M ) from the time series data for the future evaluation period t F1 ~t Fn A graph of the maximum value of the corrosive index at the utilization point M after 10 years, for example, is obtained by referring to the graph of FIG. max (t Fi 4A and 4B are examples of analysis results when a point about 75 m away from the coast of Okinawa Prefecture, Japan, is set as the planned utilization point M.

[0094] (Corrosion amount estimation step) In the corrosion amount estimation step, the actual corrosion amount and the corrosivity index Q(t Zi ,W Z The procedure for deriving this relational expression will be described below.

[0095] In order to obtain the relational expression, the actual corrosion amount obtained from the corrosion test of the steel material at the test point Z and the meteorological observation value W at the observation point X at the test point Z are Z Based on the observation time t during the corrosion test Zi The corrosion index Q(t Zi ,W Z ) is required.

[0096] The actual corrosion amount of the steel material is measured by actually placing the steel material outdoors at test point Z and observing the amount of corrosion. The amount of corrosion may be evaluated, for example, by corrosion weight loss or pitting depth, or may be evaluated by RN.

[0097] Since the amount of corrosion of steel varies depending on the type of steel, it is advisable to conduct a corrosion test at the planned utilization point M on the same type of steel as the steel to be used.

[0098] Moreover, the test point Z may be the same as the planned use point M of the steel material, or may be a different point. Furthermore, the test point Z may be one point or multiple points. In order to increase the prediction accuracy, it is preferable to have multiple test points Z. That is, the test point Z is one point Z. α However, preferably, multiple different points Z α , Z β , Z γ ... is preferable. When multiple test points Z are used, it is more preferable that the multiple test points include points with different distances from the sea. Also, it is more preferable that the multiple test points include points with different weather conditions. By using multiple test points in this way, the corrosivity index Q(t Zi ,W Z ) maximum value Q Rmax The range of Z is wider, and the accuracy of the prediction can be improved. α , Z β , Z γ The test periods at each of the test points Z may be of equal or different length. α , Z β , Z γ Observation time t included in the test period in Z1 , t Z2 , …t Zn , specifically t Zα1 ~t Zαn , t Zβ1 ~t Zβn , t Zγ1 ~t Zγn etc. may be the same time or may be different times.

[0099] In addition, the corrosive index Q(t Zi ,W Z ) for the corrosion test period t Z1 ~t Zn Weather observation value W Z Based on the observation time t Zi The corrosion index Q(t Zi ,W Z ) is calculated. Zi ,W Z) is calculated by the above-mentioned calculation steps. Z1 ~t Zn Observation time t included in Z1 , t Z2 , …t Zn For each, the corrosive index Q(t Z1 ,W Z ), Q(t Z2 ,W Z ), …Q(t Zn ,W Z ) is obtained. If there are multiple test points Z, the corrosivity index Q(t Z1 ,W Z ) is calculated. In this case, the meteorological observation value W Z is test point Z α , Z β , Z γ ...at each test period Z1 , t Z2 , …t Zn Every t Zα1 ~t Zαn , t Zβ1 ~t Zβn , t Zγ1 ~t Zγn Weather observation values ​​such as:

[0100] Next, for each corrosion test, the corrosivity index Q(t Z1 ,W Z ), Q(t Z2 ,W Z ), …Q(t Zn ,W Z ) among the maximum Q Rmax Extract the maximum value Q Rmax is linked to the measured corrosion amount, which is the test result for each corrosion test. The linked maximum value Q Rmax The relationship between the logarithm of and the amount of corrosion actually measured is determined. Rmax 5 shows an example of a plot of the logarithm of the corrosion rate (RN) and the relational expression. The dotted line in FIG. 5 is the relational expression line.

[0101] The relationship is the maximum value Q RmaxThe function equation for the approximate line that is approximated by the least squares method for the plot of the logarithm of and the measured corrosion amount (RN) can be used. For example, the relational equation is Rmax The logarithm of the measured corrosion amount (RN) may be used as the independent variable X, and the measured corrosion amount (RN) may be used as the dependent variable Y, as a linear function of Y = αX + β, where α and β are constants. The accuracy of this relational expression can be improved by increasing the number of test points Z. Rmax If you do not want to convert to a logarithm, you can use a different function that is easy to approximate instead of the linear function Y=αX+β, and find the coefficients by regression calculation or the like.

[0102] As shown in Figure 5, SUS304 has a maximum Q Rmax As Q increases, RN decreases and the appearance of the steel tends to deteriorate. Figure 5 shows the maximum Q value when multiple test points Z are located at distances of 10 to 1,000 m from the coast of the Setouchi region and Okinawa Prefecture in Japan. Rmax 1 is a graph showing the relationship between the logarithm of and the actually measured corrosion amount.

[0103] In the corrosion amount estimation step, the maximum value Q Rmax The maximum value Q of the corrosivity index estimated in the extreme value estimation step is used as the relational expression between the logarithm of max (t Fi ) is introduced. This makes it possible to obtain an estimate of the amount of corrosion of steel at the planned utilization point M for the estimated future period.

[0104] An example of the output of the estimation method of this embodiment is shown in Fig. 6. For example, it is possible to know the estimated RN value for various steels (Steel A to Steel E) from one year to several decades in the future.

[0105] As described above, in the method, system, and program for predicting the amount of corrosion of a steel material according to the present embodiment, a corrosion amount prediction system is used. Mi Corrosiveness index Q per M (t Mi ,W M) time series data, extreme value statistical analysis is performed to estimate the future evaluation period t F1 ~t Fn The maximum value of the corrosive index Q max (t Fi Next, the actual corrosion amount of the steel material, which was previously obtained from the results of the corrosion test, and the corrosion index Q(t Zi ,W Z ) is expressed as the maximum value of the corrosive index Q max (t Fi In this way, the present invention estimates the future corrosion amount of steel materials, and therefore can improve the accuracy of corrosion amount prediction compared to the conventional method of predicting the corrosion amount based on the annual average value. The corrosion amount prediction method, corrosion amount prediction system, and corrosion amount prediction program of the present embodiment are for predicting the future corrosion amount of the steel material at the planned utilization point M based on the results of a corrosion test of the steel material at the test point Z. In the present embodiment, the actual corrosion amount of the steel material at the test point Z and the corrosivity index Q(t Zi ,W Z ) and the meteorological observation value W at the planned use point M M The corrosivity index Q(t Mi ,W M ) time series data is required. Therefore, the present invention is highly versatile.

[0106] In addition, according to the method for predicting the amount of corrosion of a steel material, the corrosion amount prediction system, and the corrosion amount prediction program of this embodiment, the primary corrosivity index Q 1 (t Xi ,W X ) and secondary corrosivity index Q 2 (t Xi ,W X ) and then the secondary corrosivity index Q 2 (t Xi ,W X ) to the corrosion index Q(t Xi ,W X ) is calculated. Here, the primary corrosivity index Q 1 (t Xi ,W X ) is an index of the amount of salt attached to the steel material, and the wind speed u(t Xi ) and wind directions θs, θw(tXi ) is calculated based on the secondary corrosivity index Q 2 (t Xi ,W X ) is the observation time t Xi The observation time t Xi-1 Secondary corrosivity index Q 2 (t Xi-1 ,W X ) at observation time t Xi Primary corrosivity index Q 1 (t Xi ,W X ) is added, and the corrosion loss coefficient c(t Xi ) and the corrosion accumulation coefficient s(t Xi ) and the corrosion index Q(t Xi ,W X ) is the corrosion manifestation coefficient p(t Xi ) and the corrosiveness expression coefficient p(t Xi ) is the secondary corrosivity index Q 2 (t Xi ,W X ) is calculated. This gives the corrosion index Q(t Xi ,W X ) can be obtained, and by using this, the accuracy of predicting the amount of corrosion can be improved.

[0107] Next, the corrosivity index Q(t Xi ,W X The first modification is a modification of the corrosive index Q(t Xi ,W X ) based on the result of the determination of the wetness of the steel surface. Xi ,W XBy adopting these modified examples, it becomes possible to further improve the accuracy of prediction of the amount of corrosion.

[0108] (First Modification) In the first modification, in the fourth step of the corrosion amount prediction method or the corrosion amount prediction program, or in the fourth processing unit of the corrosion amount prediction system, the observation time t is calculated by the following formula (3B) instead of the above formula (3A). Xi For each, the corrosive index Q(t Xi ,W X ) to calculate

[0109] Q(t Xi ,W X )=10 0.04Tb Q 2 (t Xi ,W X )·p(t Xi ) …(3B)

[0110] However, Tb in formula (3B) is any temperature in the range of Temp (°C) to (Temp+2)°C, where Temp (°C) is the temperature at sunrise. Preferably, Tb may be the temperature Temp (°C) at sunrise.

[0111] The corrosion of steel is affected by temperature as well as the amount of salt adhering to the steel surface. For this reason, if the temperature difference between the observation point X, the planned use point M of the steel, and the test point Z becomes large, the accuracy of predicting the amount of corrosion may decrease. Therefore, in order to improve the accuracy of predicting the amount of corrosion, a correction term for the temperature at the observation point X is added as Q(t Xi ,W X ) formula.

[0112] As a result of the study by the inventors, it was found that it is preferable to use a correction term in the form of a power of 10, and to use a power exponent that is the product of the temperature Tb (℃) and a coefficient. Zi ,W ZWhen calculating the relational equation from the above, the exponent in the correction term was calculated by regression and the value was 0.04. Therefore, the exponent coefficient in the temperature correction term is set to 0.04.

[0113] The temperature Tb (°C) included in the index of the temperature correction term is set to any temperature in the range of Temp (°C) to (Temp + 2)°C, where Temp is the temperature at sunrise. When the surface of steel is wet, salt dissolves in the moisture on the surface, and then when the moisture evaporates and the salt concentration increases, corrosion is likely to occur, and the temperature when the moisture evaporates affects the amount of corrosion. This evaporation of moisture occurs when the temperature rises around sunrise. Therefore, the temperature Tb added to the correction term is set to any temperature in the range of Temp (°C) to (Temp + 2)°C, where Temp is the temperature at sunrise. Preferably, the temperature Tb is the temperature at sunrise (°C). The temperature Temp is calculated by multiplying the meteorological observation value W by the temperature at sunrise. X The temperature data included in the

[0114] From the above, the temperature correction term to be incorporated into the above formula (3B) is 10 0.04Tb Let us assume that.

[0115] Figure 7 shows the maximum value Q for SUS304. Rmax 7 and 8 are graphs showing the relationship between the logarithm of and the measured corrosion amount (RN), and are graphs before temperature correction. Also, FIG. 8 is a graph after temperature correction has been applied to the graph of FIG. 7. Similar to FIG. 5, FIG. 7 and FIG. 8 are graphs showing the relationship between the logarithm of and the measured corrosion amount (RN), and are graphs before temperature correction. Similarly, FIG. 7 and FIG. 8 are graphs showing the relationship between the logarithm of and the measured corrosion amount (RN), and are graphs before temperature correction. Rmax 7 and 8 show the relationship between the logarithm of Q and the measured corrosion amount. In addition to the plots shown in Fig. 5, new plots have been added. The dotted lines in Fig. 7 and Fig. 8 indicate the maximum value Q Rmax This is a line showing the relationship between the logarithm of and the measured corrosion amount (RN). The relationship is the maximum value Q Rmax The logarithm of the independent variable X is used, and the measured corrosion amount (RN) is used as the dependent variable Y, forming a linear function of Y = αX + β, where α and β are constants. As explained in Figure 5, the maximum value Q Rmaxdoes not necessarily have to be transformed into a logarithm.

[0116] The maximum value Q in Fig. 7 Rmax The squared value of the correlation coefficient between the logarithm of and the measured corrosion amount (RN) (R 2 The R value in Fig. 8 is approximately 0.81. 2 The value is about 0.86. 2 The value exceeds 0.80, and it can be seen that the reliability of the relational equation is sufficiently high even without temperature correction, but the reliability of the relational equation becomes higher by performing temperature correction. Therefore, the relational equation obtained by temperature correction includes the maximum value Q of the corrosivity index estimated in the extreme value estimation step. max (t Fi By introducing this method, it is possible to obtain an estimate of the amount of corrosion of steel at the planned utilization point M for the estimated future period, and to increase the reliability of the estimate.

[0117] (Second Modification) In the second modification, s(t Xi ) and p(t Xi ) are defined as follows, and then introduced into equation (2) or equation (3C), and the secondary corrosivity index Q 2 (t Xi ,W X ) is calculated, and the corrosivity index Q(t Xi ,W X ) is required.

[0118] In equation (2), s(t Xi ) is the corrosive accumulation coefficient at the observation time t Xi The amount of moisture adhering to the steel surface ws(t Xi ) is 0, then s(t Xi )=0, and ws(t Xi ) is greater than 0, s(t Xi )=1. p(t Xi ) is the corrosiveness expression coefficient, ws(t Xi ) exceeds 0, p(t Xi )=0, and ws(tXi ) is less than or equal to 0, then p(t Xi )=1.

[0119] In addition, ws(t Xi ) is the observation time t Xi This is an estimated value obtained by subtracting the amount of water evaporation from the amount of water adhesion due to rainfall and condensation at the surface of the steel, and is calculated from the surface temperature of the steel, air temperature (℃), air pressure (hPa), wind speed (m / s) at the steel surface, and relative humidity (%). Xi ) is the value obtained by the calculation method described later. Wind speed (m / s) on the steel surface can be substituted with wind speed values ​​obtained from meteorological observation data.

[0120] The corrosivity index Q(t Xi ,W X ) is an index of the amount of salt on the steel surface taking into account meteorological changes. Condensation water forms on the steel surface at night, and salt dissolves into the condensation water. As the temperature rises during the day, the salt-containing condensation water evaporates, concentrating the salt on the steel surface and causing corrosion. In this way, corrosion of steel is greatly influenced by the amount of moisture attached to the steel surface. Therefore, in the above embodiment, it is assumed that the amount of moisture on the steel surface increases at night due to the formation of condensation water, and decreases during the day due to the temperature rising and the moisture evaporating, and the amount of moisture is measured at observation time t Xi-1 ~t Xi Depending on whether it is daytime or nighttime, s(t Xi ) and p(t Xi ) is determined to be either 0 or 1.

[0121] On the other hand, in this modified example, Xi The amount of moisture adhering to the steel surface ws(t Xi ) and guess the guessed ws(t Xi ), s(t Xi ) and p(t Xi ) is set to either 0 or 1. This allows the amount of corrosion to be predicted with greater accuracy than if the moisture state on the steel surface was estimated based on whether it was daytime or nighttime.

[0122] In the second modification, in the fourth step of the corrosion amount prediction method or the corrosion amount prediction program, or in the fourth processing unit of the corrosion amount prediction system, the observation time t Xi For each, the corrosive index Q(t Xi ,W X ) to calculate

[0123] Q(t Xi ,W X )=10 0.04Tc Q 2 (t Xi ,W X )·p(t Xi ) …(3C)

[0124] In equation (3C), Tc is the observation time t Xi The amount of moisture on the steel surface ws(t Xi ) is the temperature (℃) at which it becomes 0.

[0125] The corrosion of steel is affected by temperature as well as the amount of salt adhering to the steel surface. For this reason, if the temperature difference between the observation point X, the planned use point M of the steel, and the test point Z becomes large, the accuracy of predicting the amount of corrosion may decrease. Therefore, in order to improve the accuracy of predicting the amount of corrosion, a correction term for the temperature at the observation point X is added as Q(t Xi ,W X ) formula.

[0126] As a result of the study by the inventors, it was found that it is preferable to use a correction term in the form of a power of 10, and to use a power exponent that is the product of the temperature Tc (℃) and a coefficient. Zi ,W Z When calculating the relational equation from the above, the exponent in the correction term was calculated by regression and the value was 0.04. Therefore, the exponent coefficient in the temperature correction term is set to 0.04.

[0127] The temperature Tc (℃) in the correction term is the temperature at the observation time t XiThe amount of moisture on the steel surface ws(t Xi ) is the temperature (℃) at which the temperature Tc becomes 0. When the surface of steel is wet, salt dissolves in the water on the surface, and then when the water evaporates and the salt concentration increases, corrosion is likely to occur. The temperature when the water evaporates also affects the amount of corrosion. Therefore, the temperature Tc added to the correction term is calculated by dividing the temperature Tc by the observation time t Xi The amount of moisture on the steel surface ws(t Xi ) is the temperature (℃) when the meteorological observation value W X The temperature data included in the above table can be used. In addition, the amount of moisture on the steel surface ws(t Xi ) is the value obtained by the calculation method described below.

[0128] In this modified example, the observation time t Xi The amount of moisture adhering to the steel surface ws(t Xi ) and estimate the amount of moisture adhering to the Xi ) is 0. Then, use Tc to find Q(t Xi ,W X This allows the amount of corrosion to be predicted with greater accuracy than if the moisture condition on the steel surface was estimated based on whether it was daytime or nighttime.

[0129] (Amount of moisture on the steel surface ws(t Xi )) Next, the amount of moisture adhering to the steel surface ws(t Xi ) will be explained. Xi ) is derived from the following formula (A):

[0130] ws(t Xi )=(W R +ΔW-V a )·Δt …(A)

[0131] In formula (A), W R is the amount of water (kg / m) adhering to the steel surface due to rainfall. 2 / s), and ΔW is the rate of condensation water generation (kg / m2 / s) and V a is the amount of water evaporated (kg / m 2 / s), and Δt is Δt=t Xi-1 -t Xi (s).

[0132] ΔW is derived by the following formula (B).

[0133] ΔW=α'·(VH-VHs) …(B)

[0134] α' is the moisture transmission rate between the air surrounding the steel and the steel surface (kg / m 2 / s), VH is the observation time t Xi VHs is the absolute humidity (kg / kg) of the air at the surface saturation temperature, which is determined by the temperature of the steel surface.

[0135] V a is derived by the following formula (C).

[0136] V a = Sh D (c 1 -c 2 ) / L …(C)

[0137] Sh is the Sherwood number, which is calculated from the Schmidt number Sc and the Reynolds number Re at the steel surface by the following formula (C-1). D is the diffusion coefficient of water vapor, and Xi It is calculated from the temperature T (℃) and pressure p (hPa) at the following formula (C-2): 1 is the observation time t Xi Amount of saturated water vapor (kg / m) at temperature T (℃) 3 ), and c 2 is the observation time t Xi The amount of water vapor in the air (kg / m) at temperature T (℃) 3 ), where L is the characteristic length. The Reynolds number Re is derived from the following equation (C-3). ρ is the air density (kg / m 3 ), v is the wind speed at the steel surface (m / s), L is the characteristic length, and μ is the viscosity coefficient of air (Pa / s).

[0138] Sh=0.332 Re 1 / 2 ·Sc 1 / 3 …(C-1) D = 0.241 x 10 -4 ·{(T+273.15) / 288} 1.75 1013.25 / (p t) …(C-2) Re=ρv 2 / (μv / L) …(C-3)

[0139] The amount of moisture adhering to the body ws(t Xi ) to determine the corrosion index Q(t Xi ,W X ) makes it possible to predict the amount of corrosion with greater accuracy.

[0140] Amount of moisture on the steel surface ws(t Xi ) may vary depending on the surface condition of the steel material, the inclination angle of the surface when the steel material is installed, etc., so when setting an upper limit value, it may be set appropriately depending on the type of steel material, the inclination state, etc.

[0141] In the first and second modified examples, the time is the time t Xi The weather observation value at point X is W X However, if point X is the steel material usage point M, then time t Xi is the observation time t at the utilization point M. Mi The meteorological observation value W X The meteorological observation value W at the usage point M M If point X is the test point Z, then time t Xi Let t be the time during the test period. Zi The meteorological observation value W X The meteorological observation value W at test point Z Z Just replace it with:

[0142] Next, a method for proposing a steel material according to this embodiment will be described. The method for proposing steel products in this embodiment includes a prediction process for predicting the future corrosion amount of steel products at the planned utilization point M using the method for predicting the corrosion amount of steel products described above, and a process in which a sales representative presents the predicted corrosion amount obtained by the prediction process to a customer. In addition, the method for proposing steel products in this embodiment is a method for proposing steel products that includes a prediction process for predicting the future corrosion amount of steel products at the intended utilization point M for each of a plurality of types of steel products using the method for predicting the corrosion amount of steel products described above, and a process in which a sales representative presents to a customer the predicted corrosion amount values ​​for each steel product obtained by the prediction process. The predicted value of the amount of corrosion may be, for example, a rating number, which may be one specified in JIS G 0595:2004 (method of evaluating the degree of surface rust formation on stainless steel).

[0143] In the step of presenting to the customer, instead of presenting the predicted value of the corrosion amount, or together with presenting the predicted value, an image of the steel surface corresponding to the predicted value of the corrosion amount may be presented. Specifically, for example, the image of the steel surface corresponding to the predicted value of the corrosion amount may be displayed on a display unit of the steel corrosion amount prediction device, or may be presented to the customer in the form of a document.

[0144] The predicted value of the amount of corrosion presented to the customer may be a rating number rounded to at least one decimal place. Similarly, the image of the steel surface corresponding to the predicted value of the amount of corrosion may be an image corresponding to a rating number rounded to at least one decimal place.

[0145] According to the method for predicting the amount of corrosion of steel materials described above, it is possible to know the estimated RN value, for example, 50 years into the future, for steel materials made of various steels, as shown in FIG. 6. Therefore, according to the method for proposing steel materials of this embodiment, the amount of corrosion of steel materials in the future at the planned use point M is predicted, and the sales representative presents the obtained predicted amount of corrosion to the customer, thereby satisfying the customer's request to know the predicted amount of future corrosion when the steel material is used in an outdoor environment for a long period of time. In addition, since a highly accurate predicted amount of corrosion can be provided, it is possible to recommend an appropriate steel material to the customer.

[0146] According to the method for predicting the amount of corrosion of steel materials described above, it is possible to know the estimated RN value for each steel type, for example, 50 years into the future, for multiple types of steel materials, as shown in FIG. 6. Therefore, according to the method for proposing steel materials of this embodiment, the amount of corrosion of steel materials into the future at the planned utilization point M is predicted for each of multiple types of steel materials, and the sales representative presents the obtained predicted value of the amount of corrosion for each steel material to the customer, so that a highly accurate predicted value of the amount of corrosion can be provided for each steel type, making it possible to select an appropriate steel material. This makes it possible to recommend an appropriate steel material to the customer.

[0147] Also, by presenting an image of the steel surface corresponding to the predicted value of the amount of corrosion, the customer can intuitively understand the degree of corrosion. Also, by showing a rating number with at least one decimal place as the predicted value of the amount of corrosion, or a corresponding image of the steel surface, it is possible to show a highly accurate prediction result. [Industrial Applicability]

[0148] The present invention can provide a method for predicting the amount of corrosion of a steel material, a system for predicting the amount of corrosion of a steel material, and a program for predicting the amount of corrosion of a steel material, which can improve the prediction accuracy of the amount of corrosion of a steel material. The present invention can also provide a method for proposing a steel material, which utilizes the amount of corrosion of a steel material predicted by the method for predicting the amount of corrosion of a steel material. These have industrial applicability.

Claims

1. As an index for evaluating the amount of corrosion when steel is exposed outdoors, Ｘｉ (where i is an integer from 1 to n) Meteorological observation values ​​W of wind speed, wind direction, and rainfall for each Ｘ The corrosion index Q (t Ｘｉ , W Ｘ A method for predicting the amount of corrosion of a steel material using The observation point X is a meteorological observation value W Ｍ Based on this, the past observation time t Ｍｉ The corrosion index Q (t Ｍｉ , W Ｍ ) and calculate the observation time t Ｍ１ ~t Ｍｎ Corrosion index Q (t Ｍｉ , W Ｍ ) time series data, extreme value statistical analysis is performed to obtain the future evaluation period t Ｆ1 ~t Ｆｎ The maximum value Q of the corrosive index within ｍａｘ (t Ｆｉ ) and an extreme value estimation step. The actual corrosion amount obtained from the corrosion test of the steel material at the test point Z and the meteorological observation value W Ｚ Based on the observation time t during the test period of the corrosion test Ｚｉ The corrosion index Q (t Ｚｉ , W Ｚ ) is calculated in advance, and the maximum value Q of the corrosiveness index estimated in the extreme value estimation step is added to this relationship. ｍａｘ (t Ｆｉ and a corrosion amount estimating step of introducing a corrosion coefficient (C) into the evaluation period to obtain an estimated value of the corrosion amount of the steel material in the evaluation period.

2. The extreme value estimation step and the corrosion amount estimation step each include the corrosiveness index Q(t Ｘｉ , W Ｘ ) The calculation step comprises: The observation time t Ｘｉ Wind speed u (t Ｘｉ ), wind direction θw(t Ｘｉ A first step of acquiring at least a rainfall amount; The observation time t Ｘｉ Each time, the primary corrosivity index Q １ (t Ｘｉ , W Ｘ A second step of calculating the following formula (1): The observation time t Ｘｉ Each time, the secondary corrosivity index Q ２ (t Ｘｉ , W Ｘ A third step of calculating the following formula (2): The observation time t Ｘｉ For each, the corrosive index Q(t Ｘｉ , W Ｘ 2. The method for predicting the corrosion amount of a steel material according to claim 1, further comprising a fourth step of calculating the corrosion amount of the steel material by the following formula (3A): Q １ (t Ｘｉ ,W Ｘ )=(d+1) －０．６ {u(t Ｘｉ )・cos|θs-θw(t Ｘｉ )|} ２ …(1) Q ２ (t Ｘｉ ,W Ｘ )={Q ２ (t Ｘｉ－１ ,W Ｘ )+Q １ (t Ｘｉ ,W Ｘ )・s(t Ｘｉ )・(t Ｘｉ -t Ｘｉ－１ )}・c(t Ｘｉ ) …(2) Q(t Ｘｉ ,W Ｘ )=Q ２ (t Ｘｉ ,W Ｘ )・p(t Ｘｉ ) …(3A) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Ｘｉ ) is the observation time t Ｘｉ is the wind speed (m / s), and θw(t Ｘｉ ) is the observation time t when north is 0° Ｘｉ is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Ｘｉ ) is the corrosion loss coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period exceeds the threshold, c(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period is less than the threshold, c(t Ｘｉ ) = 1. In equation (2), s(t Ｘｉ ) is the corrosive accumulation coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), then s(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), then s(t Ｘｉ ) = 1. p(t Ｘｉ ) is the corrosiveness expression coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), p(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), p(t Ｘｉ ) = 1.

3. The extreme value estimation step and the corrosion amount estimation step each include the corrosiveness index Q(t Ｘｉ , W Ｘ ) The calculation step comprises: The observation time t Ｘｉ Wind speed u (t Ｘｉ ), wind direction θw(t Ｘｉ A first step of acquiring at least a rainfall amount; The observation time t Ｘｉ Each time, the primary corrosivity index Q １ (t Ｘｉ , W Ｘ A second step of calculating the following formula (1): The observation time t Ｘｉ Each time, the secondary corrosivity index Q ２ (t Ｘｉ , W Ｘ A third step of calculating the following formula (2): The observation time t Ｘｉ For each, the corrosive index Q(t Ｘｉ , W Ｘ 2. The method for predicting the amount of corrosion of a steel material according to claim 1, further comprising a fourth step of calculating the amount of corrosion of the steel material by the following formula (3B): Q １ (t Ｘｉ ,W Ｘ )=(d+1) －０．６ {u(t Ｘｉ )・cos|θs-θw(t Ｘｉ )|} ２ …(1) Q ２ (t Ｘｉ ,W Ｘ )={Q ２ (t Ｘｉ－１ ,W Ｘ )+Q １ (t Ｘｉ ,W Ｘ )・s(t Ｘｉ )・(t Ｘｉ -t Ｘｉ－１ )}・c(t Ｘｉ ) …(2) Q(t Ｘｉ ,W Ｘ )=10 ０．０４Ｔｂ ・Q ２ (t Ｘｉ ,W Ｘ )・p(t Ｘｉ ) …(3B) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Ｘｉ ) is the observation time t Ｘｉ is the wind speed (m / s), and θw(t Ｘｉ ) is the observation time t when north is 0° Ｘｉ is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Ｘｉ ) is the corrosion loss coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period exceeds the threshold, c(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period is less than the threshold, c(t Ｘｉ ) = 1. In equation (2), s(t Ｘｉ ) is the corrosive accumulation coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), then s(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), then s(t Ｘｉ ) = 1. In formula (3B), Tb is any temperature in the range of Temp (° C.) to (Temp+2)° C., where Temp (° C.) is the air temperature at sunrise. p(t Ｘｉ ) is the corrosiveness expression coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), p(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), p(t Ｘｉ ) = 1.

4. The extreme value estimation step and the corrosion amount estimation step each include the corrosiveness index Q(t Ｘｉ , W Ｘ ) The calculation step comprises: The observation time t Ｘｉ Wind speed u (t Ｘｉ ), wind direction θw(t Ｘｉ A first step of acquiring at least a rainfall amount; The observation time t Ｘｉ Each time, the primary corrosivity index Q １ (t Ｘｉ , W Ｘ A second step of calculating the following formula (1): The observation time t Ｘｉ Each time, the secondary corrosivity index Q ２ (t Ｘｉ , W Ｘ A third step of calculating the following formula (2): The observation time t Ｘｉ For each, the corrosive index Q(t Ｘｉ , W Ｘ 2. The method for predicting the corrosion amount of a steel material according to claim 1, further comprising a fourth step of calculating the corrosion amount of the steel material by the following formula (3C): Q １ (t Ｘｉ ,W Ｘ )=(d+1) －０．６ {u(t Ｘｉ )・cos|θs-θw(t Ｘｉ )|} ２ …(1) Q ２ (t Ｘｉ ,W Ｘ )={Q ２ (t Ｘｉ－１ ,W Ｘ )+Q １ (t Ｘｉ ,W Ｘ )・s(t Ｘｉ )・(t Ｘｉ -t Ｘｉ－１ )}・c(t Ｘｉ ) …(2) Q(t Ｘｉ ,W Ｘ )=10 ０．０４Ｔｃ ・Q ２ (t Ｘｉ ,W Ｘ )・p(t Ｘｉ ) …(3C) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Ｘｉ ) is the observation time t Ｘｉ is the wind speed (m / s), and θw(t Ｘｉ ) is the observation time t when north is 0° Ｘｉ is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Ｘｉ ) is the corrosion loss coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period exceeds the threshold, c(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period is less than the threshold, c(t Ｘｉ ) = 1. In equation (2), s(t Ｘｉ ) is the corrosive accumulation coefficient at the observation time t Ｘｉ The amount of moisture adhering to the steel surface at ws (t Ｘｉ ) is 0, then s(t Ｘｉ )=0, and Ｘｉ ) is greater than 0, then s(t Ｘｉ ) = 1. The ws(t Ｘｉ ) is the observation time t Ｘｉ This is an estimated value obtained by subtracting the amount of water evaporation from the amount of moisture adhered due to rainfall and condensation at the steel surface, and is calculated from the surface temperature of the steel, air temperature (°C), air pressure (hPa), wind speed (m / s) at the steel surface, and relative humidity (%). Tc in formula (3C) is the observation time t Ｘｉ The amount of moisture adhering to the steel surface ws (t Ｘｉ ) is the temperature (℃) at which it becomes zero. p(t Ｘｉ ) is the corrosiveness expression coefficient, and Ｘｉ ) exceeds 0, p(t Ｘｉ )=0, and Ｘｉ ) is 0 or less, then p(t Ｘｉ ) = 1.

5. 5. The method for predicting the amount of corrosion of a steel material according to claim 1, wherein the steel material is a steel material made of stainless steel.

6. As an index for evaluating the amount of corrosion when steel is exposed outdoors, Ｘｉ (where i is an integer from 1 to n) Meteorological observation values ​​W of wind speed, wind direction, and rainfall for each Ｘ The corrosion index Q (t Ｘｉ , W Ｘ A corrosion amount prediction system for steel materials using a computer, The observation point X is a meteorological observation value W Ｍ Based on this, the past observation time t Ｍｉ The corrosion index Q (t Ｍｉ , W Ｍ ) and calculate the observation time t Ｍ１ ~t Ｍｎ Corrosion index Q (t Ｍｉ , W Ｍ ) time series data, extreme value statistical analysis is performed to obtain the future evaluation period t Ｆ1 ~t Ｆｎ The maximum value Q of the corrosive index within ｍａｘ (t Ｆｉ ) and an extreme value estimation unit for estimating The actual corrosion amount obtained from the corrosion test of the steel material at the test point Z and the meteorological observation value W Ｚ Based on the observation time t during the test period of the corrosion test Ｚｉ The corrosion index Q (t Ｚｉ , W Ｚ ) is calculated in advance, and the maximum value Q of the corrosiveness index estimated in the extreme value estimation unit is added to this relationship. ｍａｘ (t Ｆｉ and a corrosion amount estimation unit that introduces a corrosion amount estimation signal to obtain an estimated value of the corrosion amount of the steel material in the evaluation period.

7. The extreme value estimation unit and the corrosion amount estimation unit each include the corrosiveness index Q(t Ｘｉ , W Ｘ ) is provided, The calculation unit is The observation time t Ｘｉ Wind speed u (t Ｘｉ ), wind direction θw(t Ｘｉ A first processing unit that acquires at least the weather information and a rainfall amount; The observation time t Ｘｉ Each time, the primary corrosivity index Q １ (t Ｘｉ , W Ｘ A second processing unit that calculates the following equation (1): The observation time t Ｘｉ Each time, the secondary corrosivity index Q ２ (t Ｘｉ , W Ｘ A third processing unit that calculates the following equation (2): The observation time t Ｘｉ For each, the corrosive index Q(t Ｘｉ , W Ｘ 7. The steel corrosion amount prediction system according to claim 6, further comprising: a fourth processing unit that calculates the corrosion amount of a steel material by the following formula (3A): Q １ (t Ｘｉ ,W Ｘ )=(d+1) －０．６ {u(t Ｘｉ )・cos|θs-θw(t Ｘｉ )|} ２ …(1) Q ２ (t Ｘｉ ,W Ｘ )={Q ２ (t Ｘｉ－１ ,W Ｘ )+Q １ (t Ｘｉ ,W Ｘ )・s(t Ｘｉ )・(t Ｘｉ -t Ｘｉ－１ )}・c(t Ｘｉ ) …(2) Q(t Ｘｉ ,W Ｘ )=Q ２ (t Ｘｉ ,W Ｘ )・p(t Ｘｉ ) …(3A) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Ｘｉ ) is the observation time t Ｘｉ is the wind speed (m / s), and θw(t Ｘｉ ) is the observation time t when north is 0° Ｘｉ is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Ｘｉ ) is the corrosion loss coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period exceeds the threshold, c(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period is less than the threshold, c(t Ｘｉ ) = 1. In equation (2), s(t Ｘｉ ) is the corrosive accumulation coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), then s(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), then s(t Ｘｉ ) = 1. p(t Ｘｉ ) is the corrosiveness expression coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), p(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), p(t Ｘｉ ) = 1.

8. The extreme value estimation unit and the corrosion amount estimation unit each include the corrosiveness index Q(t Ｘｉ , W Ｘ ) is provided, The calculation unit is The observation time t Ｘｉ Wind speed u (t Ｘｉ ), wind direction θw(t Ｘｉ A first processing unit that acquires at least the weather information and a rainfall amount; The observation time t Ｘｉ Each time, the primary corrosivity index Q １ (t Ｘｉ , W Ｘ A second processing unit that calculates the following equation (1): The observation time t Ｘｉ Each time, the secondary corrosivity index Q ２ (t Ｘｉ , W Ｘ A third processing unit that calculates the following equation (2): The observation time t Ｘｉ For each, the corrosive index Q(t Ｘｉ , W Ｘ 7. The steel corrosion amount prediction system according to claim 6, further comprising: a fourth processing unit that calculates the corrosion amount of a steel material by the following formula (3B): Q １ (t Ｘｉ ,W Ｘ )=(d+1) －０．６ {u(t Ｘｉ )・cos|θs-θw(t Ｘｉ )|} ２ …(1) Q ２ (t Ｘｉ ,W Ｘ )={Q ２ (t Ｘｉ－１ ,W Ｘ )+Q １ (t Ｘｉ ,W Ｘ )・s(t Ｘｉ )・(t Ｘｉ -t Ｘｉ－１ )}・c(t Ｘｉ ) …(2) Q(t Ｘｉ ,W Ｘ )=10 ０．０４Ｔｂ ・Q ２ (t Ｘｉ ,W Ｘ )・p(t Ｘｉ ) …(3B) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Ｘｉ ) is the observation time t Ｘｉ is the wind speed (m / s), and θw(t Ｘｉ ) is the observation time t when north is 0° Ｘｉ is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Ｘｉ ) is the corrosion loss coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period exceeds the threshold, c(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period is less than the threshold, c(t Ｘｉ ) = 1. In equation (2), s(t Ｘｉ ) is the corrosive accumulation coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), then s(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), then s(t Ｘｉ ) = 1. In formula (3B), Tb is any temperature in the range of Temp (° C.) to (Temp+2)° C., where Temp (° C.) is the air temperature at sunrise. p(t Ｘｉ ) is the corrosiveness expression coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), p(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), p(t Ｘｉ ) = 1.

9. The extreme value estimation unit and the corrosion amount estimation unit each include the corrosiveness index Q(t Ｘｉ , W Ｘ ) is provided, The calculation unit is The observation time t Ｘｉ Wind speed u (t Ｘｉ ), wind direction θw(t Ｘｉ A first processing unit that acquires at least the weather information and a rainfall amount; The observation time t Ｘｉ Each time, the primary corrosivity index Q １ (t Ｘｉ , W Ｘ A second processing unit that calculates the following equation (1): The observation time t Ｘｉ Each time, the secondary corrosivity index Q ２ (t Ｘｉ , W Ｘ A third processing unit that calculates the following equation (2): The observation time t Ｘｉ For each, the corrosive index Q(t Ｘｉ , W Ｘ 7. The steel corrosion amount prediction system according to claim 6, further comprising: a fourth processing unit that calculates the corrosion amount of a steel material by the following formula (3C): Q １ (t Ｘｉ ,W Ｘ )=(d+1) －０．６ {u(t Ｘｉ )・cos|θs-θw(t Ｘｉ )|} ２ …(1) Q ２ (t Ｘｉ ,W Ｘ )={Q ２ (t Ｘｉ－１ ,W Ｘ )+Q １ (t Ｘｉ ,W Ｘ )・s(t Ｘｉ )・(t Ｘｉ -t Ｘｉ－１ )}・c(t Ｘｉ ) …(2) Q(t Ｘｉ ,W Ｘ )=10 ０．０４Ｔｃ ・Q ２ (t Ｘｉ ,W Ｘ )・p(t Ｘｉ ) …(3C) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Ｘｉ ) is the observation time t Ｘｉ is the wind speed (m / s), and θw(t Ｘｉ ) is the observation time t when north is 0° Ｘｉ is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Ｘｉ ) is the corrosion loss coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period exceeds the threshold, c(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period is less than the threshold, c(t Ｘｉ ) = 1. In equation (2), s(t Ｘｉ ) is the corrosive accumulation coefficient at the observation time t Ｘｉ The amount of moisture adhering to the steel surface at ws (t Ｘｉ ) is 0, then s(t Ｘｉ )=0, and Ｘｉ ) is greater than 0, then s(t Ｘｉ ) = 1. The ws(t Ｘｉ ) is the observation time t Ｘｉ This is an estimated value obtained by subtracting the amount of water evaporation from the amount of moisture adhered due to rainfall and condensation at the steel surface, and is calculated from the surface temperature of the steel, air temperature (°C), air pressure (hPa), wind speed (m / s) at the steel surface, and relative humidity (%). Tc in formula (3C) is the observation time t Ｘｉ The amount of moisture adhering to the steel surface ws (t Ｘｉ ) is the temperature (℃) at which it becomes zero. p(t Ｘｉ ) is the corrosiveness expression coefficient, and Ｘｉ ) exceeds 0, p(t Ｘｉ ) = 0, and Ｘｉ ) is 0 or less, then p(t Ｘｉ ) = 1.

10. 10. The system for predicting the corrosion amount of a steel material according to claim 6, wherein the steel material is a steel material made of stainless steel.

11. As an index for evaluating the amount of corrosion when steel is exposed outdoors, Ｘｉ (where i is an integer from 1 to n) Meteorological observation values ​​W of wind speed, wind direction, and rainfall for each Ｘ The corrosion index Q (t Ｘｉ , W Ｘ A corrosion amount prediction program for a steel material, which is used in a computer, The observation point X is a meteorological observation value W Ｍ Based on this, the past observation time t Ｍｉ The corrosion index Q (t Ｍｉ , W Ｍ ) and calculate the observation time t Ｍ１ ~t Ｍｎ Corrosion index Q (t Ｍｉ , W Ｍ ) time series data, extreme value statistical analysis is performed to obtain the future evaluation period t Ｆ1 ~t Ｆｎ The maximum value Q of the corrosive index within ｍａｘ (t Ｆｉ ) and an extreme value estimation step. The actual corrosion amount obtained from the corrosion test of the steel material at the test point Z and the meteorological observation value W Ｚ Based on the observation time t during the test period of the corrosion test Ｚｉ The corrosion index Q (t Ｚｉ , W Ｚ ) is calculated in advance, and the maximum value Q of the corrosiveness index estimated in the extreme value estimation step is added to this relationship. ｍａｘ (t Ｆｉ and a corrosion amount estimating step of introducing a corrosion amount estimation signal to obtain an estimated value of the corrosion amount of the steel material in the evaluation period.

12. The extreme value estimation step and the corrosion amount estimation step each include the corrosiveness index Q(t Ｘｉ , W Ｘ ) The calculation step comprises: The observation time t Ｘｉ Wind speed u (t Ｘｉ ), wind direction θw(t Ｘｉ A first step of acquiring at least a rainfall amount; The observation time t Ｘｉ Each time, the primary corrosivity index Q １ (t Ｘｉ , W Ｘ A second step of calculating the following formula (1): The observation time t Ｘｉ Each time, the secondary corrosivity index Q ２ (t Ｘｉ , W Ｘ A third step of calculating the following formula (2): The observation time t Ｘｉ For each, the corrosive index Q(t Ｘｉ , W Ｘ and a fourth step of calculating the corrosion amount of the steel material by the following formula (3A): Q １ (t Ｘｉ ,W Ｘ )=(d+1) －０．６ {u(t Ｘｉ )・cos|θs-θw(t Ｘｉ )|} ２ …(1) Q ２ (t Ｘｉ ,W Ｘ )={Q ２ (t Ｘｉ－１ ,W Ｘ )+Q １ (t Ｘｉ ,W Ｘ )・s(t Ｘｉ )・(t Ｘｉ -t ＸｉＳ－１ )}・c(t Ｘｉ ) …(2) Q(t Ｘｉ ,W Ｘ )=Q ２ (t Ｘｉ ,W Ｘ )・p(t Ｘｉ ) …(3A) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Ｘｉ ) is the observation time t Ｘｉ is the wind speed (m / s), and θw(t Ｘｉ ) is the observation time t when north is 0° Ｘｉ is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Ｘｉ ) is the corrosion loss coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period exceeds the threshold, c(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period is less than the threshold, c(t Ｘｉ ) = 1. In equation (2), s(t Ｘｉ ) is the corrosive accumulation coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), then s(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), then s(t Ｘｉ ) = 1. p(t Ｘｉ ) is the corrosiveness expression coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), p(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), p(t Ｘｉ ) = 1.

13. The extreme value estimation step and the corrosion amount estimation step each include the corrosiveness index Q(t Ｘｉ , W Ｘ ) The calculation step comprises: The observation time t Ｘｉ Wind speed u (t Ｘｉ ), wind direction θw(t Ｘｉ A first step of acquiring at least a rainfall amount; The observation time t Ｘｉ Each time, the primary corrosivity index Q １ (t Ｘｉ , W Ｘ A second step of calculating the following formula (1): The observation time t Ｘｉ Each time, the secondary corrosivity index Q ２ (t Ｘｉ , W Ｘ A third step of calculating the following formula (2): The observation time t Ｘｉ For each, the corrosive index Q(t Ｘｉ , W Ｘ and a fourth step of calculating the corrosion amount of the steel material by the following formula (3B): Q １ (t Ｘｉ ,W Ｘ )=(d+1) －０．６ {u(t Ｘｉ )・cos|θs-θw(t Ｘｉ )|} ２ …(1) Q ２ (t Ｘｉ ,W Ｘ )={Q ２ (t Ｘｉ－１ ,W Ｘ )+Q １ (t Ｘｉ ,W Ｘ )・s(t Ｘｉ )・(t Ｘｉ -t ＸｉＳ－１ )}・c(t Ｘｉ ) …(2) Q(t Ｘｉ ,W Ｘ )=10 ０．０４Ｔｂ ・Q ２ (t Ｘｉ ,W Ｘ )・p(t Ｘｉ ) …(3B) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Ｘｉ ) is the observation time t Ｘｉ is the wind speed (m / s), and θw(t Ｘｉ ) is the observation time t when north is 0° Ｘｉ is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Ｘｉ ) is the corrosion loss coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period exceeds the threshold, c(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period is less than the threshold, c(t Ｘｉ ) = 1. In equation (2), s(t Ｘｉ ) is the corrosive accumulation coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), then s(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), then s(t Ｘｉ ) = 1. In formula (3B), Tb is any temperature in the range of Temp (° C.) to (Temp+2)° C., where Temp (° C.) is the air temperature at sunrise. p(t Ｘｉ ) is the corrosiveness expression coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the period is nighttime (the period from sunset to sunrise), p(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the period is daytime (the period from sunrise to sunset), p(t Ｘｉ ) = 1.

14. The extreme value estimation step and the corrosion amount estimation step each include the corrosiveness index Q(t Ｘｉ , W Ｘ ) The calculation step comprises: The observation time t Ｘｉ Wind speed u (t Ｘｉ ), wind direction θw(t Ｘｉ A first step of acquiring at least a rainfall amount; The observation time t Ｘｉ Each time, the primary corrosivity index Q １ (t Ｘｉ , W Ｘ A second step of calculating the following formula (1): The observation time t Ｘｉ Each time, the secondary corrosivity index Q ２ (t Ｘｉ , W Ｘ A third step of calculating the following formula (2): The observation time t Ｘｉ For each, the corrosive index Q(t Ｘｉ , W Ｘ and a fourth step of calculating the corrosion amount of the steel material by the following formula (3C): Q １ (t Ｘｉ ,W Ｘ )=(d+1) －０．６ {u(t Ｘｉ )・cos|θs-θw(t Ｘｉ )|} ２ …(1) Q ２ (t Ｘｉ ,W Ｘ )={Q ２ (t Ｘｉ－１ ,W Ｘ )+Q １ (t Ｘｉ ,W Ｘ )・s(t Ｘｉ )・(t Ｘｉ -t ＸｉＳ－１ )}・c(t Ｘｉ ) …(2) Q(t Ｘｉ ,W Ｘ )=10 ０．０４Ｔｃ ・Q ２ (t Ｘｉ ,W Ｘ )・p(t Ｘｉ ) …(3C) In the formula (1), d is the distance (m) between the coast closest to the observation point X and the observation point X, and u(t Ｘｉ ) is the observation time t Ｘｉ is the wind speed (m / s), and θw(t Ｘｉ ) is the observation time t when north is 0° Ｘｉ is the wind direction (°) at the observation point X, and θs is the direction (°) of the observation point X from the coast closest to the observation point X, with north being 0°. c(t Ｘｉ ) is the corrosion loss coefficient at the observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period exceeds the threshold, c(t Ｘｉ ) = 0, observation time t Ｘｉ－１ ~t Ｘｉ If the amount of rainfall during the period is less than the threshold, c(t Ｘｉ ) = 1. In equation (2), s(t Ｘｉ ) is the corrosive accumulation coefficient at the observation time t Ｘｉ The amount of moisture adhering to the steel surface at ws (t Ｘｉ ) is 0, then s(t Ｘｉ )=0, and Ｘｉ ) is greater than 0, then s(t Ｘｉ ) = 1. The ws(t Ｘｉ ) is the observation time t Ｘｉ This is an estimated value obtained by subtracting the amount of water evaporation from the amount of moisture adhered due to rainfall and condensation at the steel surface, and is calculated from the surface temperature of the steel, air temperature (°C), air pressure (hPa), wind speed (m / s) at the steel surface, and relative humidity (%). Tc in formula (3C) is the observation time t Ｘｉ The amount of moisture adhering to the steel surface ws (t Ｘｉ ) is the temperature (℃) at which it becomes zero. p(t Ｘｉ ) is the corrosiveness expression coefficient, and Ｘｉ ) exceeds 0, p(t Ｘｉ )=0, and Ｘｉ ) is 0 or less, then p(t Ｘｉ ) = 1.

15. 15. The program for predicting the corrosion amount of a steel material according to claim 11, wherein the steel material is a steel material made of stainless steel.

16. A prediction step of predicting a future corrosion amount of a steel material at a planned utilization point M by the method for predicting a corrosion amount of a steel material according to any one of claims 1 to 5; A method for proposing a steel product, comprising a step in which a sales representative presents to a customer the predicted value of the amount of corrosion obtained by the prediction step or an image of the steel product surface corresponding to the predicted value of the amount of corrosion.

17. A prediction step of predicting the future corrosion amount of steel materials at a planned utilization point M for each of a plurality of types of steel materials by the method for predicting the corrosion amount of steel materials according to any one of claims 1 to 5; A method for proposing steel products comprising a step in which a sales representative presents to a customer the predicted value of the amount of corrosion for each steel product obtained by the prediction step or an image of the steel product surface corresponding to the predicted value of the amount of corrosion.

18. The method for proposing a steel material according to claim 16 or 17, wherein the predicted value of the amount of corrosion is a rating number rounded to at least one decimal place.

19. The method for proposing a steel material according to claim 16 or 17, wherein the image of the steel material surface corresponding to the predicted value of the amount of corrosion is an image corresponding to a rating number at least to one decimal place.

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