Corrosion estimation method and system

Abstract

In a third step S103, the relationship between environment data at underground locations and the magnitude of the corrosion speed in each of a plurality of corrosion samples is derived. In a fourth step S104, a score corresponding to the magnitude of the corrosion speed is set for environment data at the underground locations of each of the plurality of corrosion samples on the basis of the relationship between the environment data and the magnitude of the corrosion speed. In a fifth step S105, the scores set for the environment data are determined as scores of the corresponding plurality of corrosion samples. In a sixth step S106, the risk that a metal structure buried at the underground locations of the plurality of corrosion samples will corrode is estimated on the basis of the scores set through the process described above.

Description

[Technical Field]

[0001] The present invention relates to a corrosion estimation method and system for estimating corrosion of a buried structure. [Background technology]

[0002] The social infrastructure that supports our lives has been rapidly developed since the period of high economic growth. For this reason, it is said that by 2030, more than half of all facilities will be 50 years old or older. To prevent breakdowns in this aging infrastructure, maintenance and operation have traditionally been carried out through regular inspections. However, in recent years, the increase in aging facilities and the decrease in inspection engineers have led to delays in inspection work, and proper treatment cannot be taken for deteriorated facilities that should be inspected, which could lead to collapses. Furthermore, depending on the location of the facilities, visual inspections are difficult, so inspections are often not carried out at all. A typical example of a place where visual inspections are difficult is underground.

[0003] In light of the situation described above, active research has been conducted in recent years to establish technology to predict and estimate the deterioration state of facilities buried in the soil. If a technology for predicting and estimating the deterioration state is established, it will be possible to distinguish between facilities that are rapidly deteriorating and those that are slowly deteriorating without the need for on-site inspections. Not only will safety be ensured by prioritizing the renewal of facilities that are rapidly deteriorating, but cost efficiency is also expected by using facilities that are slowly deteriorating for longer periods.

[0004] One example of the deterioration prediction and estimation method mentioned above is the soil corrosivity evaluation method described in the American national standard ANSI / AWWA AC105 / A21.5-1999 "Polyethylene Encasement for Ductile-Iron Pipe Systems." This ANSI standard uses the results of analysis of five items: soil resistivity, pH, redox (oxidation-reduction) potential, moisture, and sulfide, and a total score of 10 or more indicates high corrosivity. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] M. Barbalat et al., "Electrochemical study of the corrosion rate of carbon steel in soil: Evolution with time and determination of residual corrosion rates under cathodic protection", Corrosion Science, vol. 55, pp. 246-253, 2012. Summary of the Invention [Problem to be solved by the invention]

[0006] However, there have been cases where corrosion deterioration was minor even in soils that received a total score of 10 or more according to ANSI. For this reason, when the ANSI evaluation score and the actual amount of corrosion were investigated, there were reports that no correlation was found between the two. Therefore, even if the evaluation method established as a standard is used, it is difficult to accurately predict or estimate soil corrosion.

[0007] Soil corrosion is known to progress based on the oxidation reaction of iron and the reduction reaction of dissolved oxygen, similar to corrosion in neutral solutions. However, soil is a special environment where three phases - solid, gas, and liquid - coexist, and it is thought that there are a wide variety of factors that contribute to corrosion reactions (Non-Patent Document 1). In particular, information on the solid phase, which is unique to soil, is important for understanding soil corrosion.

[0008] Soil particle size distribution is one example of solid-phase information that affects the state of water and oxygen, which determines whether corrosion reactions occur. Differences in soil particle size and particle size distribution change the structure of interparticle voids and the particle packing rate, significantly affecting the ease of oxygen supply from the soil surface and the wetted area of ​​metal surfaces by water captured by capillary action. Therefore, soil particle size is the most effective environmental factor for estimating the liquid and gas phase information that determines whether corrosion will occur in soil corrosion.

[0009] However, the only solid-phase information handled by the ANSI standard is soil resistivity, which is one of the factors that makes it difficult to predict and estimate soil corrosion. Furthermore, moisture is an essential factor in the progression of corrosion reactions. Although the ANSI standard also uses moisture as one of the evaluation points, it is difficult to predict and estimate corrosion based on soil moisture at a certain point in time by sampling soil.

[0010] In real-world environments, soil moisture is supplied by rainfall. However, the soil dries between rainfalls, and the soil moisture content constantly fluctuates due to repeated wetting and drying. Measuring changes in soil moisture over time can take a long time depending on the soil, making it difficult to measure the soil moisture content of numerous soil samples. For this reason, when predicting and estimating corrosion due to soil moisture, it is preferable to obtain more information about rainfall, such as annual precipitation and rainfall intervals. Furthermore, the ANSI standard is optimized for underground steel structures in the United States and does not reflect country-specific soil parameters such as temperature and elevation.

[0011] There is also a known method for predicting the corrosion of buried pipes using ANSI evaluation scores. To estimate soil corrosion using ANSI evaluation scores, soil containing buried steel must be collected and an ANSI evaluation score must be calculated for each collected soil. In other words, to estimate soil corrosion using the above-mentioned method, soil corresponding to each buried steel must be collected and an ANSI evaluation score must be calculated for each of the collected soils.

[0012] As described above, conventionally, there has been a problem in that estimating corrosion of buried structures is time-consuming and it is not easy to accurately estimate corrosion.

[0013] The present invention has been made to solve the above problems, and aims to make it possible to accurately estimate corrosion of buried structures without much effort. [Means for solving the problem]

[0014] The corrosion estimation method of the present invention includes a first step of measuring the amount of corrosion loss of multiple corrosion samples, each consisting of a metal structure buried in a different location; a second step of calculating the corrosion rate for each of the multiple corrosion samples from the measured amount of corrosion loss and the burial period; a third step of calculating the correlation between environmental data at the buried locations and the corrosion rate of each of the multiple corrosion samples; a fourth step of setting a score for the environmental data at the buried locations of each of the multiple corrosion samples based on the correlation between the environmental data and the corrosion rate, depending on the corrosion rate; a fifth step of using the scores set for the environmental data as scores for the corresponding multiple corrosion samples; and a sixth step of estimating the risk of corrosion of metal structures buried in the locations where the multiple corrosion samples were buried based on the scores.

[0015] The corrosion estimation system according to the present invention also includes a measuring device and a computing device. The measuring device measures the amount of corrosion thinning of a plurality of corrosion samples, each consisting of a metal structure buried in a different location. The computing device includes a first computing circuit that calculates the corrosion rate for each of the plurality of corrosion samples from the amount of corrosion thinning and the burial period; a second computing circuit that calculates the correlation between environmental data at the buried locations and the magnitude of the corrosion rate for each of the plurality of corrosion samples; a third computing circuit that sets a score for the environmental data at the buried locations of each of the plurality of corrosion samples based on the correlation between the environmental data and the magnitude of the corrosion rate; a fourth computing circuit that sets the score set for the environmental data as the score for the corresponding plurality of corrosion samples; and an estimation circuit that estimates the risk of corrosion of a metal structure buried in the locations where the plurality of corrosion samples were buried based on the scores. [Effects of the Invention]

[0016] As described above, according to the present invention, the score set for environmental data is used as the score for multiple corresponding corrosion samples, and the risk of corrosion is estimated based on the scores, so that corrosion of buried structures can be accurately estimated without much effort. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a flowchart illustrating a corrosion estimation method according to an embodiment of the present invention. [Figure 2] FIG. 2 is a configuration diagram showing the configuration of a corrosion estimation system according to an embodiment of the present invention. [Figure 3] FIG. 3 is a configuration diagram showing the configuration of measuring device 101 of the corrosion estimation system according to the embodiment of the present invention. [Figure 4] FIG. 4 is a characteristic diagram showing an example of the results of a correlation analysis between the corrosion parameter k and the environmental data. [Figure 5] FIG. 5 is a characteristic diagram showing the relationship between the corrosion parameter k and the total corrosion risk score determined for each corrosion sample. [Figure 6] FIG. 6 is an explanatory diagram showing a corrosion risk map for estimating corrosion risk for each region. DETAILED DESCRIPTION OF THE INVENTION

[0018] A corrosion estimation method according to an embodiment of the present invention will be described below with reference to FIG. 1. In this method, first, in a first step S101, the corrosion loss of a plurality of corrosion samples, each consisting of a metal structure buried in a different location, is measured. The difference between the initial thickness and the remaining thickness of the metal structure can be taken as the corrosion loss. Next, in a second step S102, the corrosion rate is calculated for each of the plurality of corrosion samples from the measured corrosion loss and the buried period.

[0019] Next, in a third step S103, the correlation between the environmental data at the buried locations and the magnitude of the corrosion rate of each of the multiple corrosion samples is calculated. The environmental data at the buried locations of the multiple corrosion samples is acquired, and the correlation between the acquired environmental data and the magnitude of the corrosion rate is calculated.

[0020] Next, in a fourth step S104, based on the correlation between the environmental data and the magnitude of the corrosion rate, a score is assigned to the environmental data at the buried locations of each of the multiple corrosion samples according to the magnitude of the corrosion rate. Here, the environmental data at the buried locations of the multiple corrosion samples is acquired, and a score is assigned to the acquired environmental data.

[0021] Next, in a fifth step S105, the score set for the environmental data is determined as the score for the corresponding plurality of corrosion samples. In the fifth step S105, the sum of the scores set for each of the plurality of environmental data for the buried location is determined as the score (total score) for the corresponding plurality of corrosion samples.

[0022] Next, in a sixth step S106, the risk of corrosion of a metal structure buried in the location where the multiple corrosion samples were buried is estimated based on the scores set as described above. In addition, in a fifth step S105, the risk can be estimated for each region.

[0023] Next, a corrosion estimation system for implementing the above-described corrosion estimation method will be described with reference to Fig. 2. This system includes a measuring device 101 and a computing device 102. The operations of the measuring device 101 and the computing device 102 are controlled, for example, by a controller (not shown). The measuring device 101 measures the amount of corrosion thinning of a plurality of corrosion samples each made of a metal structure buried in a different location.

[0024] The arithmetic device 102 includes a first arithmetic circuit 103, a second arithmetic circuit 104, a third arithmetic circuit 105, an estimation circuit 107, a memory circuit 108, and a display unit 109. When the measurement of the corrosion thinning amount is performed by the measurement device 101, the arithmetic device 102 starts operating under the control of a controller (not shown).

[0025] The first arithmetic circuit 103 calculates the corrosion rate for each of the plurality of corrosion samples from the corrosion thinning amount and the buried period. The calculated corrosion rate is stored in the memory circuit 108 together with identification information for identifying the corresponding corrosion sample.

[0026] The second arithmetic circuit 104 determines the correlation between the environmental data at the buried locations and the magnitude of the corrosion rate of each of the multiple corrosion samples. The arithmetic device 102 acquires the environmental data at the buried locations of the multiple corrosion samples, and the second arithmetic circuit 104 determines the correlation from the acquired environmental data.

[0027] For example, the environmental data is pre-stored in the memory circuit 108. Furthermore, the environmental data is associated with identification information that identifies the corrosion sample and stored in the memory circuit 108. The set (given) score is associated with the corresponding corrosion sample and stored in the memory circuit 108.

[0028] The third arithmetic circuit 105 assigns a score to the environmental data at the buried location of each of the plurality of corroded samples according to the magnitude of the corrosion rate, based on the correlation between the environmental data and the magnitude of the corrosion rate.

[0029] The fourth arithmetic circuit 106 determines the score set for the environmental data as the score for the corresponding plurality of corrosion samples. The fourth arithmetic circuit 106 determines the sum of the scores set for each of the plurality of environmental data for the buried location as the score (total score) for the corresponding plurality of corrosion samples.

[0030] The estimation circuit 107 estimates the risk of corrosion of a metal structure buried in a location where multiple corrosion samples were buried, based on the score (total score) set as described above. The estimation circuit 107 can also estimate for each region. The estimation circuit 107 estimates the risk based on the score stored in the memory circuit 108.

[0031] The arithmetic device 102 is a computer device equipped with a CPU (Central Processing Unit), memory, etc. The CPU operates (executes) a program loaded in the memory, thereby realizing the above-mentioned functions (second to fifth steps). The arithmetic device 102 can also be configured with a programmable logic device (PLD) such as an FPGA (field-programmable gate array). A program for realizing the operation of each step can be written into the FPGA by connecting a predetermined writing device.

[0032] Next, the measuring device 101 will be described in more detail with reference to Fig. 3. The measuring device 101 includes a washer 111, a dryer 112, a rust remover 113, and a wall-thinning measuring instrument 114.

[0033] The measuring device 101 measures the amount of corrosion thinning of buried steel that has deteriorated after use in a real environment. First, a corrosion sample for which the amount of corrosion thinning is to be measured is prepared and placed in the cleaning device 111 of the measuring device 101. The cleaning device 111 removes soil and other deposits from the surface of the corrosion sample. The method for removing surface deposits in the cleaning device 111 is not particularly limited as long as it is a method that can remove all deposits except for the rust layer.

[0034] For example, the surface of the corroded sample can be cleaned using a washer 111 consisting of a high-pressure washer. For example, if highly viscous mud is attached to the surface of the corroded sample, the soil moisture content in the mud can be reduced to 0% using a dryer 112 before cleaning, and then the mud can be mechanically removed using the washer 111, which uses a hammer or the like. However, when mechanically removing the mud, it is important to take care not to deform the corroded sample.

[0035] After the cleaning machine 111 has completed removing the deposits from the surface of the corroded sample, the corroded sample is sent to the rust remover 113. The rust layer adhering to the surface of the corroded sample is removed in the rust remover 113. The mechanism (method) for removing the rust layer by the rust remover 113 is not limited as long as it is possible to remove the rust layer. For example, mechanical removal methods or pickling methods can be used as the rust removal method.

[0036] Mechanical removal methods that can be used include, for example, the blank replica method, in which methyl acetate is dropped onto the surface and then the rust is mechanically removed with an acetyl cellulose film before it evaporates, and the cathodic electrolysis method, in which hydrogen is generated in a dilute sulfuric acid or sodium hydroxide aqueous solution and rust is removed by gas pressure.

[0037] Additionally, rust can be removed using pickling methods such as 3% HCl alcohol or a 50% citric acid solution + 50% ammonium citrate solution. However, if HCl alcohol is used without an inhibitor (corrosion suppressant), there is a risk of pitting corrosion occurring in the corroded sample itself, so care must be taken with the immersion time.

[0038] After the rust removal of the corroded sample is completed using the rust remover 113, the moisture on the surface of the corroded sample is removed using the dryer 112. If the corrosion thickness loss is measured using the thickness loss measuring device 114 while moisture remains on the surface of the corroded sample, an accurate value may not be obtained depending on the measurement method. The drying method in the dryer 112 may be, for example, application of heat or reduction of pressure. However, when applying heat, it is important to take care not to thermally deform the corroded sample.

[0039] After the pretreatment of the corrosion sample using the washer 111, rust remover 113, and dryer 112 is completed, the corrosion thinning amount of the corrosion sample is measured using a thinning measuring instrument 114. The corrosion thinning amount is actually calculated by measuring the remaining thickness of the corrosion sample and subtracting it from the design value. The remaining thickness can be measured using, for example, a vernier caliper. When using a vernier caliper, measurements can be taken at any desired location about 10 times, and the average value of these measurements can be used to determine the remaining thickness.

[0040] Furthermore, the average remaining wall thickness can be calculated by measuring the surface irregularities using a 3D macroscope. Alternatively, a laser measuring instrument can be used to irradiate a corrosion sample from above and below, and the average remaining wall thickness can be measured from the reflectivity. When targeting equipment where holes could cause serious accidents, the minimum remaining wall thickness can be measured instead of the average remaining wall thickness, and the maximum corrosion wall thickness can be calculated. However, when measuring the minimum remaining wall thickness, it is not possible to use a caliper; it is preferable to use a 3D macroscope or a laser measuring instrument. The measurement method used by the wall thickness reduction measuring instrument 114 is not limited to the above-mentioned method, as long as it is possible to measure the corrosion wall thickness desired by the user.

[0041] The amount of thinning measured by the thinning measuring instrument 114 is sent to the arithmetic device 102 and stored in the memory circuit 108. As described above, in the arithmetic device 102, the first arithmetic circuit 103 calculates the corrosion rate from the amount of thinning due to corrosion and the buried period (the number of years that have passed since installation). For example, if the corroded sample is a steel material, the corrosion of the steel material is calculated as "D=kT nIt is known that corrosion progresses according to the power law model given by (1). In equation (1), D is the amount of corrosion thinning [mm], k is the corrosion parameter [mm / y], T is the number of years since the buried steel was laid [y], and n is the corrosion evaluation value of the material. Empirically, the value of n is said to be between 0.4 and 0.6, so the intermediate value of 0.5 can be used.

[0042] The corrosion parameter k is calculated from the corrosion thinning amount D measured by the thinning measuring device 114 and the number of years T that have passed since the corrosion sample was examined in advance. Since the corrosion parameter k has a unit of [mm / y], the corrosion parameter k can be treated as the corrosion rate. Furthermore, substituting 1 for T in equation (1) gives D=k, so the corrosion parameter k can be understood as the corrosion thinning amount over the first year.

[0043] It is preferable to measure as many corrosion samples as possible using the measuring device 101 for steel materials buried in an area where corrosion risk is desired to be measured. In order to analyze the correlation between the amount of corrosion thinning and environmental data, it is preferable to measure at least 50 samples using the measuring device 101.

[0044] Next, the second arithmetic circuit 104 analyzes the correlation between the corrosion thinning amount measured by the measuring device 101 and the environmental data. The corrosion parameter k calculated by the first arithmetic circuit 103 as described above is sent to the memory circuit 108. Publicly available environmental data is also stored in the memory circuit 108. The environmental data corresponds to information about the land and environment in which the corrosion sample was buried, and among these, factors thought to be involved in the progression of soil corrosion correspond to the environmental data.

[0045] For example, because repeated wetting and drying are essential for the progression of soil erosion, information from Radar AMeDAS can be used to obtain information such as the average annual rainfall, average annual rainfall frequency, average annual interval between rainfall, the number of times per year there is no rain for 24 hours or more, and the number of times per year there is rainfall of 10 mm or more.

[0046] For example, information on solid phases specific to soil erosion can be obtained from soil map data sold by the Japan Soil Association, such as soil group, which indicates the type of soil, topsoil, which indicates the size of soil particles in shallow areas, and subtopsoil, which indicates the size of soil particles in deep areas.

[0047] For example, to estimate groundwater information related to corrosion, the distance to the nearest water body can be obtained from the ESRI detailed map. For example, to determine whether a terrain is prone to water collection, the elevation, maximum slope angle, distance to the valley line, and distance to the ridge line can be obtained from the National Land Digital Information. For example, to estimate the drying rate of soil, the annual hours of sunshine can be obtained from the National Land Digital Information.

[0048] From the viewpoint of kinetics, information related to corrosion rate can be obtained from climate data, including the annual mean temperature, the annual maximum temperature, and the annual minimum temperature. Environmental data is not limited to the above information, as long as it can extract parameters related to corrosion progression.

[0049] The second arithmetic circuit 104 analyzes the correlation between the corrosion parameter k (corrosion rate) stored in the memory circuit 108 and the environmental data. For example, FIG. 4 shows an example of the results of a correlation analysis between the corrosion parameter k and the environmental data for all 117 corrosion samples collected from the Kanto region. FIG. 4 is an example graph showing the correlation between the corrosion parameter k and the distance to the nearest water body. From FIG. 4, it is possible to obtain a correlation in which the closer the distance to the nearest water body, the larger the average value of the corrosion parameter k.

[0050] In each bar graph in Figure 4, the standard deviation is calculated and an error bar is added. As shown in Figure 4, an analysis of the correlation between each environmental data in the memory circuit 108 and the corrosion parameter k is performed. In the example of Figure 4, the distance to the nearest water body is divided into 200 m intervals for analysis, but it may be further divided into 100 m intervals or 300 m intervals. The user can freely decide how to divide each environmental data.

[0051] The third arithmetic circuit 105 assigns a score (corrosion risk score) to the environmental data based on the correlation between the corrosion parameter k analyzed (obtained) by the second arithmetic circuit 104 as described above and the environmental data. The third arithmetic circuit 105 assigns a corrosion risk score to each piece of environmental data. For example, considering the example in FIG. 4, it can be seen that the closer the distance to the nearest water body, the larger the value of the corrosion parameter k. As a result, the environmental data with a larger value of the corrosion parameter k is assigned a higher corrosion risk score.

[0052] The distribution of points may be determined, for example, by the magnitude of the average value of the corrosion parameter k. Alternatively, a t-test may be performed for each bar graph, and the gradient of the corrosion risk score may be determined depending on whether or not there is a significant difference. For example, looking at Figure 4, a t-test was performed between the average value of the corrosion parameter k when the distance to the nearest water body is 200 m or less and the average value of the corrosion parameter k when the distance to the nearest water body is 400 to 600 m and when the distance is 600 m or more. The p-value, which is the value used to determine a significant difference, was 0.01 or less.

[0053] This indicates that there are significant differences between the populations with a distance to the nearest water body of 200m or less and 400-600m, and between those with a distance of 200m or less and 600m or more. Generally, a p-value of 0.05 or less is considered to indicate a significant difference, with the bar graph marked with an "*" for p<0.05, a "**" for p<0.01, and a "***" for p<0.001. Note that in Figure 4, the "***" for p<0.001 is not shown as it has no meaning. The smaller the p-value, the greater the significance is considered, so the corrosion risk score can be scaled according to the number of "*".

[0054] Based on the above, for the results shown in Figure 4, a corrosion risk score of 5 points can be assigned when the distance to the nearest water body is 200 m or less, 3 points when it is 200 to 400 m, 2 points when it is 400 to 600 m, and 1 point when it is 600 m or more. Furthermore, for environmental data that shows no correlation with the corrosion parameter k, the corrosion risk score can be set to 0 points. The method of assigning corrosion risk scores is not limited to the above-mentioned method, as long as it reflects the correlation between the corrosion parameter k and the environmental data.

[0055] After the third calculation circuit 105 sets a corrosion risk score for each environmental data, the fourth calculation circuit 106 adds up the corrosion risk scores of all environmental data to calculate a total corrosion risk score, and determines this as the score for the corresponding corrosion sample.

[0056] Figure 5 is an example graph showing the relationship between the corrosion parameter k and the total corrosion risk score determined for each corrosion sample. The total corrosion risk score in Figure 5 is an example of the result of adding up the scores of seven environmental data items, including the distance to the nearest water body in Figure 4. It can be seen that corrosion samples with exceptionally high corrosion parameter k received high total corrosion risk scores of 15 and 14 points. Furthermore, the example in Figure 5 does not include any corrosion samples with severe corrosion degradation for total corrosion risk scores of 13 points or less.

[0057] The estimation circuit 107 sets a risk standard for selecting equipment to be replaced based on the graph of the corrosion parameter k and the total corrosion risk score of the corrosion samples in Figure 5. First, it sets the level of thickness reduction required for the target equipment to be replaced. For example, if the equipment in Figure 5 has a plate thickness of 3.2 mm, it is assumed that replacement is required when the corrosion thickness reduction reaches half of that, 1.6 mm. Furthermore, since the maximum age of the collected corrosion samples was 45 years, the age is set to 45 years.

[0058] Based on the above, substituting 1.6 mm for D and 45 y for T in equation (1) gives k of 0.349 mm / y. This value corresponds to the line shown in (a) in Figure 5, and it can be recognized (estimated) that corroded samples with a value of the corrosion parameter k greater than (a) are samples with severe corrosion degradation.

[0059] Next, in Figure 5, we set the total corrosion risk score that will actually be required to renew. Since corrosion samples exceeding (a) were within the range of 14 and 15 points, equipment corresponding to corrosion samples with a total corrosion risk score of 14 points or more can be set as equipment to be renewed. Corrosion samples with a score of 14 points or more are points plotted to the right of the line in Figure 5(b), and equipment corresponding to corrosion samples with scores to the right of the line in Figure 5(b) will be eligible for renewal.

[0060] When the line in FIG. 5(b) is set, the operation in the risk standard setting unit 33 is completed. The estimation circuit 107 sets the corrosion risk based on the calculated corrosion risk total score. Figure 5 is a schematic diagram showing the corrosion risk for each region calculated from the corrosion risk total score. First, a corrosion risk can be assigned for each corrosion risk total score.

[0061] For example, in descending order of corrosion risk, a corrosion risk total score of 14 to 15 points can be assigned as Risk A, 10 to 13 points as Risk B, 5 to 9 points as Risk C, and 0 to 4 points as Risk D. This allows a corrosion risk map to be created that estimates corrosion risk for each region, as shown in FIG. 6. The estimation results are displayed, for example, on the display unit 109, so that the operator can see them. Using these estimation results, the corrosion risk of buried steel materials can be estimated over a wide area without performing any measurements or analyses, including corrosion thinning measurements.

[0062] All 117 corrosion samples plotted in Figure 5 are over 40 years old and were all recognized as items requiring replacement, and were subsequently collected. If the corrosion risk estimation device 1 were to renew only those samples with a total risk score of 14 or more, it would be possible to determine that only 30 of the 117 samples need to be replaced. Of these, all samples with severe corrosion deterioration that require replacement, i.e., samples exceeding the reference value (a) in Figure 5, can be replaced without exception.

[0063] Therefore, in consideration of the example in Figure 5, by using this invention, safety can be ensured by replacing steel materials buried in the same area without overlooking those that are severely corroded and deteriorated, and the total number of replacements can be reduced by 75% compared to the previous method, so that safe materials can be used for a longer period of time, eliminating waste and ensuring economy.

[0064] Figure 5 shows one example. By increasing the amount of environmental data handled and improving the gradient of corrosion risk scoring, it becomes possible to estimate corrosion risk more accurately, thereby ensuring the safety and economy described above.

[0065] If the estimation results of the corrosion risk estimation device 1 are for the same population installed in the same region and for which the correlation between the corrosion parameter k and environmental data is ensured, it will be possible to calculate a total corrosion risk score for each facility and perform corrosion risk assessments without analyzing the correlation between corrosion risk. Furthermore, if the environment of the buried region is significantly different, it is preferable to obtain a new correlation between the corrosion parameter k and environmental data and derive corrosion risk estimation results that are highly suitable for the region.

[0066] As described above, according to the present invention, the scores set for environmental data are used as the scores for the corresponding multiple corrosion samples, and the risk of corrosion is estimated based on the scores, so that corrosion of buried structures can be accurately estimated without much effort. According to the present invention, as long as environmental data can be obtained, it is possible to create, for example, a corrosion risk map for each region, and once the corrosion risk map is obtained, it is possible to estimate the corrosion risk of steel materials buried in the regions within the map without even needing to obtain environmental data. [Explanation of symbols]

[0067] 101...Measuring device, 102...Arithmetic device, 103...First arithmetic circuit, 104...Second arithmetic circuit, 105...Third arithmetic circuit, 106...Fourth arithmetic circuit, 107...Estimating circuit, 108...Storage circuit, 109...Display section.

Claims

1. a first step of measuring the amount of corrosion thinning of a plurality of corrosion samples each consisting of a metal structure buried in a different location; a second step of determining a corrosion rate for each of the plurality of corrosion samples from the measured corrosion thinning amount and the buried period; a third step of determining a correlation between environmental data at the buried location and the magnitude of the corrosion rate of each of the plurality of corrosion samples; a fourth step of assigning a score to the environmental data at the buried location of each of the plurality of corrosion samples based on the correlation between the environmental data and the magnitude of the corrosion rate, according to the magnitude of the corrosion rate; a fifth step of setting the scores set for the environmental data as scores for the corresponding plurality of corrosion samples; a sixth step of estimating, based on the scores, the risk of corrosion of a metal structure buried in the location where the plurality of corrosion samples were buried; A corrosion estimation method comprising:

2. The corrosion estimation method according to claim 1, A corrosion estimation method, characterized in that the sum of scores set for each of a plurality of environmental data at the location where the sample was buried is used as the score for the corresponding plurality of corrosion samples.

3. 3. The corrosion estimation method according to claim 1, Environmental data of the locations where the plurality of corrosion samples were buried is acquired, and a correlation between the acquired environmental data and the magnitude of the corrosion rate is determined. A corrosion estimation method characterized by:

4. The corrosion estimation method according to claim 3, A corrosion estimation method characterized by estimating the risk for each region.

5. A measuring device and a computing device are provided, The measuring device measures the amount of corrosion thinning of a plurality of corrosion samples each made of a metal structure buried in a different location, The computing device a first calculation circuit that calculates a corrosion rate for each of the plurality of corrosion samples from the corrosion thinning amount and the embedment period; a second calculation circuit that calculates a correlation between environmental data at the buried location and the magnitude of the corrosion rate of each of the plurality of corrosion samples; a third calculation circuit that assigns a score to the environmental data at the buried location of each of the plurality of corrosion samples based on the correlation between the environmental data and the magnitude of the corrosion rate, according to the magnitude of the corrosion rate; a fourth calculation circuit that sets scores set for the environmental data as scores for the corresponding corrosion samples; an estimation circuit that estimates a risk of corrosion of a metal structure buried in a location where the plurality of corrosion samples were buried based on the scores; and Equipped with A corrosion estimation system characterized by:

6. 6. The corrosion estimation system according to claim 5, The fourth calculation circuit calculates the sum of the scores set for each of the plurality of environmental data at the buried location as the score for the corresponding plurality of corrosion samples. A corrosion estimation system characterized by:

7. 7. The corrosion estimation system according to claim 5, the computing device acquires environmental data of the locations where the plurality of corrosion samples were buried, The second calculation circuit calculates the correlation from the acquired environmental data. A corrosion estimation system characterized by:

8. The corrosion estimation system according to claim 7, A corrosion estimation system, wherein the estimation circuit performs the estimation for each region.

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