Pipeline damage prediction system, pipeline damage prediction method, and pipeline damage prediction program

Abstract

This system provides a pipeline damage prediction system that can obtain appropriate damage prediction information for each pipeline by taking into account the effects of soft ground and ground boundaries. [Solution] A pipeline damage prediction system that uses a computer to predict damage to pipelines caused by natural disasters, comprising: pipeline attribute data indicating the attributes of the pipeline; ground characteristic data indicating the characteristics of the ground in which the pipeline is buried, which includes at least one of liquefaction data, ground boundary data, and topographic modification data in units of polygons in a GIS; a basic data storage unit that stores damage data indicating the degree of damage caused by natural disasters that have occurred in the past; and a damage prediction calculation unit that generates damage prediction information predicting damage to the pipeline based on the pipeline attribute data stored in the basic data storage unit, the ground characteristic data, and the damage data.

Description

Technical Field

[0001] The present invention relates to a pipeline damage prediction system, a pipeline damage prediction method, and a pipeline damage prediction program for predicting damage to pipelines caused by natural disasters using a computer.

Background Art

[0002] It is desired to formulate an efficient renewal plan for the aging of pipelines such as water supply pipe networks. At that time, by predicting the damage to pipelines caused by natural disasters such as earthquakes, landslides, volcanic eruptions, and floods, and preferentially renewing pipelines with large predicted damage, it becomes possible to reconstruct a pipe network that is resistant to disasters.

[0003] For example, as a method for predicting damage caused by an earthquake, the damage estimation formula Rm(v) shown in Fig. 8(a) proposed by the Japan Water Works Association (JWWA), a public interest incorporated foundation, and the damage estimation formula Rm(v) shown in Fig. 8(b) proposed by the Water Technology Research Center (JWRC), a public interest incorporated foundation, were used.

[0004] The former is obtained by multiplying the standard damage rate R(v) (=3.11×10 ―3 ×(v - 15) 1.30 ) based on the maximum ground surface velocity v by the pipe type correction coefficient Cp, the diameter correction coefficient Cd, the terrain correction coefficient Cg, and the liquefaction correction coefficient Cl. The latter is classified according to the presence or absence of liquefaction. In the case of no liquefaction, it is obtained by multiplying the standard damage rate R(v) (=9.92×10 ―3 ×(v - 15) 1.14 ) based on the maximum ground surface velocity v by the pipe type correction coefficient Cp, the diameter correction coefficient Cd, and the terrain correction coefficient Cg. In the case of liquefaction, it is obtained by multiplying the liquefaction standard damage rate R L by the pipe type correction coefficient Cp and the diameter correction coefficient Cd.

[0005] In both cases, each correction coefficient is a value obtained using methods such as multiple regression analysis based on the energy and damage situation of past earthquakes (number of damages per pipe length (damage rate)). Furthermore, in both cases, each correction coefficient was calculated by dividing a standard regional mesh set based on latitude and longitude into four sections, each section being approximately 250m on each side. The main microtopographic data contained in each mesh was used as the representative microtopographic data for that mesh, and the total of 24 types of microtopographic data were divided into four categories in JWWA and five categories in JWRC, with a topographic correction coefficient Cg set for each category.

[0006] Therefore, the values ​​calculated using the damage estimation formula Rm(v) based on the terrain correction coefficient Cg set for each mesh were adopted as damage prediction information for pipelines laid in that mesh. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2001-329574 [Patent Document 2] Japanese Patent Publication No. 2004-310307 [Overview of the project] [Problems that the invention aims to solve]

[0008] However, with the conventional pipeline damage prediction information calculated on a mesh-based basis as described above, the damage prediction information for multiple pipelines laid in the same mesh would be the same, making it difficult to properly evaluate which pipelines should be replaced first. For example, pipeline damage is said to be common in soft ground areas such as residential land developed by embankment or land created by modifying old rivers or water bodies (such as reservoirs), but conventional methods have not been able to evaluate soft ground conditions.

[0009] The objective of the present invention is to provide a pipeline damage prediction system, a pipeline damage prediction method, and a pipeline damage prediction program that can obtain appropriate damage prediction information for each pipeline by taking into account the effects of soft ground and ground boundaries. [Means for solving the problem]

[0010] To achieve the above objectives, the first characteristic configuration of the pipeline damage prediction system according to the present invention is a pipeline damage prediction system that uses a computer to predict damage to pipelines caused by natural disasters, comprising: pipeline attribute data indicating the attributes of the pipeline; ground characteristic data indicating the characteristics of the ground in which the pipeline is buried, which includes at least one of liquefaction data, ground boundary data, and topographic modification data in units of polygons in a GIS; a basic data storage unit that stores damage data indicating the degree of damage caused by natural disasters that have occurred in the past; and a damage prediction calculation unit that generates damage prediction information predicting the damage to the pipeline based on the pipeline attribute data, ground characteristic data, and damage data stored in the basic data storage unit.

[0011] The damage prediction calculation unit generates damage prediction information that predicts damage to pipelines based on pipeline attribute data indicating the attributes of the pipeline, ground characteristic data, and damage data indicating the extent of damage caused by natural disasters such as earthquakes that have occurred in the past. By adopting ground characteristic data that includes at least one of the following as the ground characteristic data: liquefaction data, ground boundary data, and topographic modification data, which are based on polygons in a GIS (Geographic Information System) that demarcate boundaries such as the ground, it is possible to obtain damage prediction information based on the ground characteristics of the polygons in which the pipelines are actually laid, and as a result it becomes possible to formulate an appropriate pipeline renewal plan.

[0012] The second characteristic configuration is that, in addition to the first characteristic configuration described above, the ground boundary data is defined as the overlapping area when adjacent boundaries are expanded by a predetermined width based on the pipe length, using the polygon as the unit.

[0013] Even when pipelines are laid across the boundaries of adjacent polygons, making it difficult to appropriately evaluate the degree of influence of the ground constituting each polygon, by defining the overlapping area when the boundaries of adjacent ground are expanded by a predetermined width based on the length of the pipeline, using the outer length of each polygon as the unit, it becomes possible to appropriately evaluate the degree of influence of the ground on the pipeline included in each ground boundary data.

[0014] The third characteristic feature is that, in addition to the first characteristic feature described above, the topographic modification data includes either the former river channel / former water area or the land used for residential development.

[0015] As topographic modification data, former river channels, former reservoirs, and residential land development areas that have been reclaimed with earthworks are areas of land with a high risk of being affected by natural disasters. This allows for an appropriate evaluation of the degree of impact on pipelines laid on such land.

[0016] The fourth characteristic configuration is that, in addition to the first characteristic configuration described above, the damage prediction calculation unit includes a machine learning unit that generates a decision tree model by ensemble learning of multiple datasets consisting of pipeline attribute data, ground characteristic data, and damage data associated with each pipeline, and generates the damage prediction information based on the decision tree model generated by the machine learning unit.

[0017] Preferably, the damage prediction calculation unit includes a machine learning unit that generates the damage prediction information. The machine learning unit prepares a dataset for each of the multiple pipelines that includes the pipeline attribute data, ground characteristic data, and past damage data, and generates a decision tree model by repeatedly performing ensemble learning. By inputting the pipeline attribute data and ground characteristic data for each pipeline to be evaluated into the decision tree model generated by the machine learning unit, appropriate damage prediction information can be obtained.

[0018] The first characteristic configuration of the pipeline damage prediction method according to the present invention is a pipeline damage prediction method for predicting damage to a pipeline caused by natural disasters using a computer, comprising: a pipeline attribute data indicating the attributes of the pipeline; ground characteristic data indicating the characteristics of the ground where the pipeline is buried, the ground characteristic data including at least one of liquefaction data, ground boundary data, and terrain modification data in units of polygons in GIS; a basic data storage step of storing damage data indicating the degree of damage caused by past natural disasters in a basic data storage unit; and a damage prediction calculation step of generating damage prediction information for predicting damage occurring to the pipeline by a damage prediction calculation unit based on the pipeline attribute data, the ground characteristic data, and the damage data stored in the basic data storage unit in the basic data storage step.

[0019] The second characteristic configuration is that, in addition to the first characteristic configuration described above, the ground boundary data is defined as an overlapping area when the adjacent boundaries in units of the polygon are expanded by a predetermined width based on the pipeline extension.

[0020] The third characteristic configuration is that, in addition to the first characteristic configuration described above, the terrain modification data includes any one of an old river course, an old water area, and a reclaimed ground.

[0021] The fourth characteristic configuration is that, in addition to the first characteristic configuration described above, the damage prediction calculation step executes a machine learning step of generating a decision tree model by ensemble learning a plurality of data sets composed of the pipeline attribute data, the ground characteristic data, and the damage data associated with each pipeline, and generates the damage prediction information based on the decision tree model generated in the machine learning step.

[0022] The first characteristic configuration of the pipeline diagram design support program according to the present invention is to function a computer as a basic data storage processing unit that stores pipeline attribute data indicating the attributes of pipelines, ground characteristic data indicating the characteristics of the ground in which the pipelines are buried, the ground characteristic data including at least liquefaction data, ground boundary data, and terrain modification data in units of polygons in GIS, and damage data indicating the degree of damage caused by past natural disasters in a basic data storage unit, and a damage prediction calculation unit that generates damage prediction information predicting damage occurring in the pipeline based on the pipeline attribute data, the ground characteristic data, and the damage data stored in the basic data storage unit.

[0023] The second characteristic configuration is that, in addition to the first characteristic configuration described above, the ground boundary data is defined as an overlapping area when the adjacent boundary in units of the polygon is expanded by a predetermined width based on the pipeline extension.

[0024] The third characteristic configuration is that, in addition to the first characteristic configuration described above, the terrain modification data includes any of old river channels, old water areas, and reclaimed land.

[0025] The fourth characteristic configuration is that, in addition to the first characteristic configuration described above, the damage prediction calculation unit includes a machine learning unit that generates a decision tree model by ensemble learning of a plurality of data sets composed of the pipeline attribute data, the ground characteristic data, and the damage data associated with each pipeline, and generates the damage prediction information based on the decision tree model generated by the machine learning unit.

Effects of the Invention

[0026] As described above, according to the present invention, it has become possible to provide a pipeline damage prediction system, a pipeline damage prediction method, and a pipeline damage prediction program that can obtain appropriate damage prediction information for each pipeline by considering the influence of soft ground, ground boundaries, and the like.

Brief Description of the Drawings

[0027] [Figure 1] Diagram illustrating the computer configuration used to build the pipeline damage prediction system. [Figure 2] Diagram illustrating the functional blocks of the pipeline damage prediction system. [Figure 3] Diagram illustrating the structure of a dataset for training or evaluation. [Figure 4] (a) and (b) are explanatory diagrams for formulating ground boundary data. [Figure 5] Flowcharts (a) to (d) illustrating the procedure for predicting pipeline damage are explanatory diagrams for the automatic generation procedure of intersections and passage indication points, and explanatory diagrams for the pipe layout diagram creation device according to the present invention. [Figure 6] Diagram illustrating the prediction results from the pipeline damage prediction system. [Figure 7] (a) and (b) are explanatory diagrams of the damage prediction calculation unit showing an alternative embodiment. [Figure 8] (a) is an explanatory diagram of the damage estimation formula by JWWA, and (b) is an explanatory diagram of the damage estimation formula by JWRC. [Modes for carrying out the invention]

[0028] The pipeline damage prediction system, pipeline damage prediction method, and pipeline damage prediction program according to the present invention will be described below with reference to the drawings. [Pipeline Damage Prediction System] As shown in Figure 1, the pipeline damage prediction system 10 consists of a computer C, which is connected by a communication bus to a motherboard equipped with integrated circuits such as a CPU and chipset, a memory board equipped with semiconductor memory such as ROM and RAM, and an I / O board equipped with input / output interfaces.

[0029] Computer C is connected to storage devices such as hard disk drives and flash memory devices, portable memory devices such as USB memory sticks, input devices such as keyboards and mice, and output devices such as LCD displays and printers, and is configured to connect to a cloud computer via a communication interface such as Wi-Fi.

[0030] The pipeline damage prediction system 10 is realized when a pipeline damage prediction program, provided via a recording medium such as a cloud computer, storage device, or portable memory device, is installed in the memory on a memory board, and the pipeline damage prediction program is executed by the CPU.

[0031] Figure 2 shows the functional block configuration of the pipeline damage prediction system 10. The pipeline damage prediction system 10 comprises a basic data storage unit 11 and a damage prediction calculation unit 12. The basic data storage unit 11 consists of the aforementioned storage devices and cloud computers and stores pipeline attribute data indicating the attributes of the pipeline, ground characteristic data indicating the characteristics of the ground in which the pipeline is buried, which includes at least one of the following in GIS units: liquefaction data, ground boundary data, and terrain modification data, and damage data indicating the degree of damage caused by natural disasters that have occurred in the past.

[0032] The damage prediction calculation unit 12 is a calculation unit that generates damage prediction information predicting damage to the pipeline based on the pipeline attribute data, ground characteristic data, and damage data stored in the basic data storage unit 11.

[0033] The damage prediction calculation unit 12 includes a machine learning unit 13 that generates a decision tree model by ensemble learning of multiple datasets consisting of pipeline attribute data, ground characteristic data, and damage data associated with each pipeline, and generates damage prediction information based on the decision tree model generated by the machine learning unit 13.

[0034] A decision tree is a knowledge representation that describes the procedure for separating features using a branched tree structure. It determines the properties and classification category of a pipeline, which is the target of damage estimation, from multiple attributes that characterize it. Attributes refer to the pipeline attribute data, ground characteristic data, and each classification item that constitutes the damage data. In the decision tree, branching progresses according to the answers to questions about attributes, and ultimately, information on the estimated damage to the pipeline is obtained.

[0035] Figure 3 illustrates an example of a dataset of attributes set for each pipeline Pi (i=1,2,...) that constitutes the pipeline network. Pipeline Pi includes pipeline attribute data such as pipe type (data that can identify the material and seismic resistance), diameter (data indicating the size of the pipe), pipe length (data indicating the length of the pipe), and year of installation. In addition, ground characteristic data includes values ​​that are basically in polygon units, such as the microtopographic classification where the pipeline is laid, liquefaction data indicating liquefaction characteristics, topographic modification data such as old river channels / water bodies and residential land development ground data, ground boundary data indicating whether the pipeline exists at a ground boundary, maximum ground acceleration, and maximum ground velocity. Furthermore, damage data includes the number of damages per unit pipe length (km) from past earthquakes. Microtopographic classifications are set with values ​​that reflect the degree of earthquake impact, such as velocity amplification factor, according to the microtopography. The "microtopographic classification" includes data indicating the category of "topography" and numerical data of "topography" that indicates the degree of impact, such as velocity amplification factor. This takes into account the impact of earthquakes on micro-topography.

[0036] The liquefaction data indicates whether or not liquefaction occurred due to past earthquakes as a 1 / 0; the topographic modification data, such as former river channels, former water bodies, and residential land development ground data, indicates whether or not they apply as a 1 / 0; and the ground boundary data indicates whether or not a pipeline exists at the ground boundary as a 1 / 0.

[0037] If the liquefaction data is "1", it can be predicted that liquefaction will likely occur in the event of a future earthquake, increasing the likelihood of damage. The topographic modification data can be predicted that changes in topography will increase the likelihood of damage in the event of a future earthquake. This takes into account areas where ground deformation is likely to occur, which cannot be evaluated using the current topographic classification. Former river channels and former waterways have soft ground, and residential development areas have soft ground due to embankment, so it can be predicted that ground deformation will increase in the event of a future earthquake.

[0038] If a pipeline is located at a ground boundary, that is, at the boundary of adjacent polygons, then in the event of a future earthquake, the ground will not change uniformly, resulting in different ground deformations affecting the pipeline, and consequently, pipeline damage can be predicted.

[0039] Figure 4(a) shows three adjacent ground areas represented by solid lines. The data source is a 1:200,000 seamless geological map, and assuming a boundary line width of 0.05 mm (roughly the width of a single hair), the ground boundary will have a width of approximately 10 m (= 0.05 × 200,000 × 1 / 1,000).

[0040] As shown in Figure 4(b), when ground with different characteristics is adjacent, the movement of both during an earthquake will differ, affecting the pipeline at the boundary, and this effect is thought to extend to the adjacent joint. Since the length of a single pipe is 4m to 6m, the range of influence of ground changes on the pipeline is ±6m of the ground boundary, considering the conservative side. If we consider the effect of positional accuracy to be ±5m and the influence of ground changes on the pipeline to be 6m on the conservative side, the range of the ground boundary can be defined as ±11m. Specifically, ground boundary data can be defined as the overlapping area when adjacent boundaries are expanded by a predetermined width based on the pipe length, using polygons as the unit. In this embodiment, we have exemplified the case where the width of the boundary line on the geological map can be estimated to be 10m, but it goes without saying that this value is not limiting and depends on the data source.

[0041] A decision tree is generated by using the following basic learning algorithm to train the machine learning unit 13 with the dataset shown in Figure 3 as training data. The training dataset is classified into subsets using appropriate attributes. If no attributes are available for classification, the algorithm terminates without completing the classification knowledge. For each subset, this algorithm is applied to find the difference between the obtained damage prediction information and the actual damage information, and the process of creating a new decision tree (classification rule) to reduce the difference is repeated. At this time, instead of creating a single decision tree, multiple decision trees are created and ensemble learning is used, where the whole set of these is used as a single piece of knowledge, thereby generating a more appropriate decision tree.

[0042] Parallel ensemble methods such as XGBoost and Random Forest are suitable for ensemble learning. For example, in the Random Forest method, multiple training datasets are created by randomly extracting data from a dataset as shown in Figure 3. A corresponding decision tree is created using each training dataset, and the overall result, i.e., damage estimation information, can be obtained by using the average of the outputs of the multiple decision trees.

[0043] Figure 4 illustrates the series of steps described above. In other words, the pipeline damage prediction method, which uses a computer to predict damage to pipelines caused by natural disasters, is configured to perform the following steps: a basic data storage step (SA1) in which pipeline attribute data indicating the attributes of the pipeline, ground characteristic data indicating the characteristics of the ground in which the pipeline is buried, which includes at least one of liquefaction data, ground boundary data, and terrain modification data in the form of polygons in a GIS, and damage data indicating the degree of damage caused by natural disasters that have occurred in the past, is stored in a basic data storage unit; and damage prediction calculation steps (SA2 to SA7) in which a damage prediction calculation unit generates damage prediction information predicting the damage that will occur to the pipeline based on the pipeline attribute data, ground characteristic data, and damage data stored in the basic data storage unit in the basic data storage step.

[0044] The damage prediction calculation step includes a machine learning step (SA2-SA5) in which a decision tree model is generated by ensemble learning of multiple datasets consisting of pipeline attribute data, ground characteristic data, and damage data associated with each pipeline, and a step (SA7) in which damage prediction information is generated based on the decision tree model generated by the machine learning step.

[0045] Figure 6 shows the recall rate for actual earthquake damage caused by past earthquakes in a certain region, when any of the six features mentioned above—presence or absence of liquefaction, presence or absence of residential land development ground, presence or absence of ground boundaries, presence or absence of old river channels / old water bodies, categorical topographic data, and numerical topographic data—are removed or combined, as well as the ratio of the recall rate to Model 0 (M0), which does not take any of the features into account. Here, recall rate refers to the proportion of actual damaged pipelines for which the predicted damage was accurate. A doubling of the recall rate means a doubling of the accuracy rate, and a larger number indicates better prediction accuracy.

[0046] By comparing the prediction results for six features (including both categorical and numerical data for "topography") that are thought to have a high correlation with actual pipeline damage, under specific seismic motions, the following was found: Models that use at least one of each feature (M1-M13) have a higher recall rate than M0 models that use none of the features. Models that use only one feature exhibit high recall rates for liquefaction (M1) and ground boundary (M3), which are 2.4 times and 1.8 times higher than M0, respectively. Among the models that do not use any one of the features, the model that does not use only the presence or absence of liquefaction (M7) has the lowest recall rate, followed by the model that does not use the presence or absence of residential land development ground (M8). The model that does not use only the ground boundary (M10) has a higher accuracy of reproduction than the model that does not use only the old river / old waterway (M9), and the model that uses both (M11) has an even higher accuracy of reproduction. 5. By using numerical data on "topography" in addition to the presence or absence of liquefaction, the presence or absence of residential land development ground, ground boundaries, and former rivers / water bodies, the recall rate is highest at 3.1 times that of M0.

[0047] The dataset shown in Figure 3 illustrates an example where polygon data is assigned to pipelines as ground attribute data. However, for attribute data other than liquefaction, old river channels / water areas, and ground boundaries, it is also permissible to use conventional mesh data.

[0048] The application program that embodies the pipeline damage prediction system 10 described above becomes the pipeline damage prediction program of the present invention, and is installed on a computer via the various recording media and communication interfaces described above.

[0049] In other words, the pipeline damage prediction program is a pipeline damage prediction program that causes a computer to function as a basic data storage unit that stores pipeline attribute data indicating the attributes of the pipeline, ground characteristic data indicating the characteristics of the ground in which the pipeline is buried, which includes at least one of liquefaction data, ground boundary data, and topographic modification data in the form of polygons in a GIS, and damage data indicating the degree of damage caused by natural disasters that have occurred in the past, and a damage prediction calculation unit that generates damage prediction information predicting damage that will occur to the pipeline based on the pipeline attribute data, ground characteristic data, and damage data stored in the basic data storage unit.

[0050] Furthermore, the damage prediction calculation unit includes a machine learning unit that generates a decision tree model by ensemble learning of multiple datasets consisting of pipeline attribute data, ground characteristic data, and damage data associated with each pipeline, and is a program that generates damage prediction information based on the decision tree model generated by the machine learning unit.

[0051] It is preferable to have a pipeline renewal plan formulation device that formulates a renewal plan for existing pipelines based on the damage prediction information obtained by the pipeline damage prediction system 10 described above. For example, the renewal plan formulation device can be realized by comprising: a data storage unit capable of storing pipeline attribute data linked to pipeline IDs; a deterioration value unit that performs deterioration evaluation due to aging from the pipeline attribute data; a pipeline damage estimation unit that performs pipeline damage estimation due to natural disasters from the pipeline attribute data; a pipeline attribute data update unit that updates the pipeline attribute data by combining the deterioration evaluation result calculated by the deterioration evaluation unit and the pipeline damage estimation result calculated by the pipeline damage estimation unit based on the pipeline ID and storing them in the data storage unit; and a pipeline renewal plan calculation unit that calculates a pipeline renewal plan based on the updated pipeline attribute data. The pipeline damage prediction system of the present invention is applied to the pipeline damage estimation unit.

[0052] In the embodiments described above, the pipeline damage prediction system is equipped with a machine learning unit that generates a decision tree model by ensemble learning of multiple datasets consisting of pipeline attribute data, ground characteristic data, and damage data associated with each pipeline, and a damage prediction calculation unit that generates damage prediction information based on the decision tree model generated by the machine learning unit. However, the damage prediction calculation unit may be configured to generate damage prediction information by employing the Markov chain Monte Carlo method (MCMC method).

[0053] The Markov chain Monte Carlo method is a technique that defines a chain of probability distributions and estimates the parameters of those distributions based on the data. The number of damage cases within a mesh follows a Poisson distribution, and the distribution parameter λi in mesh i can be obtained from the formula shown in Figure 7(a). The first term on the right-hand side represents the influence of each factor, and the second term Li on the right-hand side represents the length of the pipeline in mesh i. As shown in Figure 7(b), each coefficient β of the first term on the right-hand side 〇 These are calculated by fitting a normal distribution. Each coefficient β 〇 By calculating this, we can derive a damage prediction formula. Damage prediction model: Number of incidents = Explanatory variable 1 × Explanatory variable 2 × ... × Pipeline length log(number of incidents) = log(explanatory variable 1) + log(explanatory variable 2) + ... + log(pipe length) + error

[0054] Furthermore, instead of including a machine learning unit as the damage prediction calculation unit, the terrain correction coefficient Cg included in the conventional damage estimation formula Rm(v), as shown in Figures 8(a) and (b), may be adapted to ground characteristic data that includes either ground boundary data or terrain modification data in polygon units in a GIS, and the liquefaction correction coefficient may be adapted to liquefaction data in polygon units.

[0055] Furthermore, ground boundary data, terrain modification data, and liquefaction data may be positioned not as values ​​of 1 / 0 as in the example above, but as weighting coefficients that further correct the correction coefficient set for the micro-topographic division corresponding to the polygon where the pipeline is laid.

[0056] In the embodiments described above, a pipeline damage prediction system due to earthquakes was explained, but the pipeline damage prediction system according to the present invention can also be applied to predicting pipeline damage due to natural disasters such as ground collapse, volcanic eruptions, and floods.

[0057] The embodiments described above represent one aspect of the present invention, and the technical scope of the present invention is not limited based on this description. It goes without saying that the design can be modified as appropriate within the scope in which the effects of the present invention are achieved. [Explanation of Symbols]

[0058] 10: Pipeline damage prediction system 11: Basic data storage unit 12: Damage prediction calculation section

Claims

1. A pipeline damage prediction system that uses a computer to predict damage to pipelines caused by natural disasters, A basic data storage unit that stores pipeline attribute data indicating the attributes of the pipeline, ground characteristic data indicating the characteristics of the ground in which the pipeline is buried, which includes at least one of liquefaction data, ground boundary data, and topographic modification data in the form of polygons in a GIS, and damage data indicating the extent of damage caused by natural disasters that have occurred in the past. A damage prediction calculation unit generates damage prediction information predicting damage to the pipeline based on the pipeline attribute data, ground characteristics data, and damage data stored in the basic data storage unit, A pipeline damage prediction system equipped with this feature.

2. The pipeline damage prediction system according to claim 1, wherein the ground boundary data is defined as the overlapping area when adjacent boundaries are expanded by a predetermined width based on the length of the pipe, using the polygon as the unit.

3. The pipeline damage prediction system according to claim 1, wherein the terrain modification data includes any of the former river channel, former water body, and residential land development ground.

4. The pipeline damage prediction system according to claim 1, wherein the damage prediction calculation unit comprises a machine learning unit that generates a decision tree model by ensemble learning of a plurality of datasets consisting of pipeline attribute data, ground characteristic data, and damage data associated with each pipeline, and generates the damage prediction information based on the decision tree model generated by the machine learning unit.

5. A method for predicting damage to pipelines caused by natural disasters using a computer, A basic data storage step involves storing pipeline attribute data indicating the attributes of the pipeline, ground characteristic data indicating the characteristics of the ground in which the pipeline is buried, which includes at least one of liquefaction data, ground boundary data, and topographic modification data in units of polygons in a GIS, and damage data indicating the degree of damage caused by natural disasters that have occurred in the past, in a basic data storage unit. A damage prediction calculation step in which a damage prediction calculation unit generates damage prediction information predicting damage occurring to the pipeline based on the pipeline attribute data, ground characteristics data, and damage data stored in the basic data storage unit in the basic data storage step, A method for predicting pipeline damage.

6. The pipeline damage prediction method according to claim 5, wherein the ground boundary data is defined as the overlapping area when adjacent boundaries are expanded by a predetermined width based on the length of the pipe, using the polygon as the unit.

7. The pipeline damage prediction method according to claim 5, wherein the topographic modification data includes any of the former river channel, former water body, and residential land development ground.

8. The pipeline damage prediction method according to claim 5, wherein the damage prediction calculation step involves executing a machine learning step to generate a decision tree model by ensemble learning of a plurality of datasets consisting of pipeline attribute data, ground characteristic data, and damage data associated with each pipeline, and generating the damage prediction information based on the decision tree model generated by the machine learning step.

9. Computers, A basic data storage processing unit stores pipeline attribute data indicating the attributes of the pipeline, ground characteristic data indicating the characteristics of the ground in which the pipeline is buried, which includes at least one of liquefaction data, ground boundary data, and topographic modification data in units of polygons in a GIS, and damage data indicating the degree of damage caused by natural disasters that have occurred in the past in the basic data storage unit. A damage prediction calculation unit generates damage prediction information predicting damage to the pipeline based on the pipeline attribute data, ground characteristics data, and damage data stored in the basic data storage unit, A pipeline damage prediction program designed to function in this way.

10. The pipeline damage prediction program according to claim 7, wherein the ground boundary data is defined as the overlapping area when adjacent boundaries are expanded by a predetermined width based on the length of the pipe, using the polygon as the unit.

11. The pipeline damage prediction program according to claim 7, wherein the terrain modification data includes any of the former river channel, former water body, and residential land development ground.

12. The pipeline damage prediction program according to claim 7, wherein the damage prediction calculation unit comprises a machine learning unit that generates a decision tree model by ensemble learning of a plurality of datasets consisting of pipeline attribute data, ground characteristic data, and damage data associated with each pipeline, and generates the damage prediction information based on the decision tree model generated by the machine learning unit.

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