JP6682021B2 - Road collapse risk evaluation device, road collapse risk evaluation method, and computer program for road collapse risk evaluation - Google Patents

Description

Cross reference

  This application claims priority based on Japanese Patent Application No. 2017-021003 filed in Japan on February 8, 2017, and the contents described in the application are incorporated herein by reference. The contents described in the patents, patent applications and literatures cited in the present application are incorporated herein.

  The present invention relates to a road collapse risk evaluation apparatus and a road collapse risk evaluation method for evaluating the risk of road collapse, and a road collapse risk evaluation computer program installed in a computer for evaluating the risk of road collapse.

  In Japan, the number of local government employees continues to decline due to the shrinking population and aging population, as well as the austerity of local government finances. Regarding the decrease in the number of staff, it is necessary to prepare for the future even in the maintenance department of infrastructure such as roads and water and sewage systems. Road collapses in Japan, irrespective of size, occur more than 4000 times each year on average. The impact of road collapse on the economy as a whole, including transportation, lifelines such as electricity and gas, is greater in urban areas than in rural areas. In order to prevent road collapse and protect roads, it is necessary to obtain road ground information (especially the presence of underground cavities) at an early stage.

  Causes of cavities are caused by sewer pipes, such as misalignment of sewer pipes and poor joints account for about 40% of the total. Such a sewer pipe defect is caused by an aging pipe, a defective construction, etc. in addition to an external force represented by an increase in traffic volume and earthquake motion. Currently, in the ordinance-designated cities, various infrastructure information such as water and sewage, which is centrally managed by the road management system of general incorporated foundations and road management centers, is mutually provided. However, even such a system does not include all the information indicating the state of the infrastructure under the road. In addition, many ordinance-designated cities in Japan have many active faults in and around the city. Such active faults are also considered to be a hazard that causes road collapse. The infrastructure information described above does not necessarily include the existence of the hazard.

  Looking at road collapses both inside and outside Japan, it is known that the occurrence of road collapses due to sewer pipelines is more frequent in Korea than in Japan. As one of the measures, Seoul City signed an administrative agreement with Tokyo in 2015 regarding technical cooperation for road collapse. In response to this, the city of Seoul decided to conduct a survey using ground-penetrating radar (GPR) and carry out exploration of major highways in the next three-year cycle. Currently, Seoul City is developing a program for cavern research and analysis. In the United States and Canada, sewer pipe breakage is detected by a method called Smoke Testing. Specifically, this method is to set one manhole, then put odorless and nontoxic smoke from the manhole into the sewer, and eject the smoke from another manhole through the manhole and the sewer pipe. . At this time, it is judged that there is a damaged sewer pipe in the sewer pipe connected to the manhole in which smoke did not spout.

  In Japan, if smoke testing is applied, it is difficult to observe on the road under the conditions of dense conduit installation in densely populated areas such as houses, and the density of the conduit itself under the road is high. It is difficult to identify the leak location. Therefore, in Japan, applying GPR is more in line with road conditions. In Japan, in recent years, the Road Law has been revised, and the Road Bureau of the Ministry of Land, Infrastructure, Transport and Tourism provides a guideline for the comprehensive inspection of roads, in which it is indicated that the radar will perform a cavity survey under the road. According to the Ministry of Land, Infrastructure, Transport and Tourism's manual, the cavity exploration vehicle equipped with radar has an exploration capacity of approximately 2.0 m, a depth of approximately 1.5 m, and a cavity with a length of 50 cm × width 50 cm × thickness of 10 cm or more. Have. By using such a vehicle, it is possible to grasp the existence and size of a cavity under the road. A cavity exploration method using such a vehicle is exemplified in Patent Document 1.

Japanese Patent Laid-Open No. 05-087945

  However, the above-mentioned conventional cavity exploration method is merely a means for grasping the existence of a cavity, and it is difficult to objectively and quantitatively evaluate the risk of road depression. In order to prevent road collapse and take appropriate measures, it is strongly required to objectively and quantitatively evaluate the risk of collapse.

  The present invention has been made to solve the above problems, and an object of the present invention is to objectively and quantitatively evaluate the risk of road collapse.

(1) A road collapse risk evaluation apparatus according to an embodiment for achieving the above object is a road collapse risk evaluation apparatus for evaluating the risk of road collapse,
It is an expression for determining the risk of road collapse and is data necessary for determining a specific judgment formula as a function formula of the factor of road collapse and the risk, and for creating the specific judgment formula Judgment formula factor data receiving means for receiving judgment formula factor data quantifying the factors of road collapse at a plurality of sampled points,
The general judgment formula shown in the following formula (A), which is the basis of the specific judgment formula, {Y is an external criterion indicating the presence or absence of a road depression, and X1 to Xn: Xi are explanatory variables that cause road depression. A1 to An are coefficients by which X1 to Xn are respectively multiplied, n is the number of the i-th explanatory variable Xi and is an integer of 1 or more, xij is the j-th categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi represent the number of categories of Xi, respectively. } General determination formula storage means for storing
First substituting means for substituting the judgment formula factor data configured by the categorical variables into the general judgment formula read from the general judgment formula storage means,
By substituting the factor data for the judgment formula into the general judgment formula, a calculation is executed by the quantification theory type II analysis so as to maximize the intergroup variation relative to the total variation, and Specific determination formula determining means for determining the specific determination formula specifying the coefficient (aij);
A specific determination formula storage means for storing the specific determination formula,
Risk factor data receiving means for receiving risk factor data quantifying the factors of road collapse at a plurality of evaluation points to be evaluated,
A second substituting means for substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination expression read from the specific determination expression storage means,
Judgment value determination means for performing a calculation based on the processing of the second substitution means, and for determining a judgment value that is a numerical value of the risk of depression at each evaluation target point,
including.

(2) A road collapse risk degree evaluation device according to another embodiment preferably classifies the determination value calculated by the specific determination expression into a predetermined range, and sets a threshold for occurrence of a depression based on the presence or absence of an actual depression. It further includes a threshold value determining means for determining.

(3) A road collapse risk degree evaluation device according to another embodiment preferably includes map information storage means for storing information on a map including each of the evaluation target points,
On the map read from the map information storage means, map display means for displaying by color or by shade based on the determination value indicating the risk of depression at each evaluation target point,
Further includes.

(4) In the road collapse risk assessment apparatus according to another embodiment, preferably, the factors of the road collapse are the presence of underground cavities, the type of backfill soil, the condition of groundwater, the number of years of pipe laying, and pipes. It is 2 or more of the overburden thickness on the road, the part of the pipe, the pipe type, the damage condition of the pipe, traffic vibration, summer temperature and active fault hazard.

(5) The road collapse risk evaluation method according to one embodiment is a method of evaluating the risk of road collapse using a device for evaluating the risk of road collapse.
It is an expression for determining the risk of road collapse and is data necessary for determining a specific judgment formula as a function formula of the factor of road collapse and the risk, and for creating the specific judgment formula A judgment formula factor data receiving step for receiving judgment formula factor data quantifying the factors of road collapse at a plurality of sampled points;
The general judgment formula shown in the following formula (A), which is the basis of the specific judgment formula, {Y is an external criterion indicating the presence or absence of a road depression, and X1 to Xn: Xi are explanatory variables that cause road depression. A1 to An are coefficients by which X1 to Xn are respectively multiplied, n is the number of the i-th explanatory variable Xi and is an integer of 1 or more, xij is the j-th categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi represent the number of categories of Xi, respectively. } The first substitution step of substituting the judgment formula factor data configured by the categorical variables into the general judgment formula read from the general judgment formula storing means for storing
By substituting the factor data for the judgment formula into the general judgment formula, a calculation is executed by the quantification theory type II analysis so as to maximize the intergroup variation relative to the total variation, and A specific determination formula determining step of determining the specific determination formula that specifies a coefficient (aij);
A risk factor data reception step for receiving risk factor data quantifying the factors of road collapse at a plurality of evaluation points to be evaluated,
A second substitution step of substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination expression read from the specific determination expression storage unit that stores the specific determination expression,
A determination value determination step of performing a calculation based on the processing of the second substitution step, and determining a determination value that is a numerical value of the risk of depression at each evaluation target point,
including.

(6) In the road collapse risk assessment method according to another embodiment, preferably, the judgment value calculated by the specific judgment formula is classified into a predetermined range, and a threshold for the occurrence of a collapse is determined based on the presence or absence of an actual collapse. The method further includes a threshold value determining step of determining

(7) In the road collapse risk assessment method according to another embodiment, preferably, each evaluation is performed on the map read from a map information storage unit that stores information on a map including each evaluation target point. The method further includes a map display step of displaying by color or by light and shade based on the determination value indicating the risk of depression at the target point.

(8) A road depression risk evaluation computer program according to an embodiment is a computer program that is installed in a computer and causes the computer to function as a road depression risk evaluation device for evaluating the risk of road depression. There
The computer
It is an expression for determining the risk of road collapse and is data necessary for determining a specific judgment formula as a function formula of the factor of road collapse and the risk, and for creating the specific judgment formula Judgment factor data reception means for receiving judgment factor data quantifying the factors of road collapse at a plurality of sampled points,
The general judgment formula shown in the following formula (A), which is the basis of the specific judgment formula, {Y is an external criterion indicating the presence or absence of a road depression, and X1 to Xn: Xi are explanatory variables that cause road depression. A1 to An are coefficients by which X1 to Xn are respectively multiplied, n is the number of the i-th explanatory variable Xi and is an integer of 1 or more, xij is the j-th categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi represent the number of categories of Xi, respectively. } The first substituting means for substituting the judgment formula factor data constituted by the categorical variables into the general judgment formula read from the general judgment formula storage means for storing
By substituting the factor data for the judgment formula into the general judgment formula, a calculation is executed by the quantification theory type II analysis so as to maximize the intergroup variation relative to the total variation, and Specific determination formula determining means for determining the specific determination formula that specifies the coefficient (aij),
Risk factor factor data receiving means for receiving risk factor factor data quantifying the factors of road collapse at a plurality of evaluation target points,
Second substitution means for substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination expression read from the specific determination expression storage means for storing the specific determination expression, and A calculation is performed based on the processing of the second substituting means to function as a judgment value deciding means for deciding a judgment value which is a numerical value of the risk of depression at each evaluation target point.

(9) A computer program for evaluating a road collapse risk according to another embodiment preferably uses the above computer,
The judgment value calculated by the specific judgment formula is classified into a predetermined range, and further functions as a threshold value determining unit that determines a threshold value for occurrence of a depression based on the presence or absence of an actual depression.

(10) A computer program for evaluating a road collapse risk according to another embodiment preferably includes the computer,
On the map read from the map information storage unit that stores the information of the map including each of the evaluation target points, based on the determination value indicating the risk of depression at each evaluation target point It further functions as a map display means for displaying.

  According to the present invention, it is possible to objectively and quantitatively evaluate the risk of road collapse.

FIG. 1 shows a conceptual diagram for obtaining a road collapse risk degree in the embodiment of the present invention. The figure for demonstrating the general formula (general determination formula) of the determination formula demonstrated based on FIG. 1 is shown. The details of the general determination formula shown in FIG. 1A are shown. An example of various data used to create a road collapse sign determination formula is shown. An example of various data used for creation of a road depression sign determination expression different from the example shown in FIG. 1C is shown. FIG. 2 shows an example of various data used to create a road depression sign determination formula different from FIGS. 1C and 1D. FIG. 3 shows a functional configuration of the road collapse risk assessment apparatus according to the embodiment of the present invention. FIG. 4 shows a flow of main steps of the road collapse risk assessment method according to the embodiment of the present invention. FIG. 5 is a table in which the external reference Y and various explanatory variables X1 to X5 at 100 points for determination expression creation are quantified, and show data at each of the 1st to 50th points. FIG. 6 is a table in which the external reference Y and various explanatory variables X1 to X5 at 100 points for determination expression creation are quantified, and shows data at the 51st to 100th points. FIG. 7 is a diagram (7A) for explaining a general determination formula, a diagram (7B) for explaining factorized data, a diagram (7C) for explaining a specific determination formula, and a threshold value. The drawings (7D, 7E) are shown respectively. FIG. 8 shows a table in which various explanatory variables X1 to X5 at 30 evaluation target points for which the degree of risk is evaluated are digitized. FIG. 9 shows a map (9A) of an area including points to be evaluated and a risk evaluation map (9B) in which unit blocks including each point are displayed on the map in different colors or shades. FIG. 10 is a diagram for explaining the specific determination formula (10A), a diagram for explaining the threshold value (10B, 10C), and a risk evaluation in which unit blocks including each point are displayed on the map in color or shade. Maps (10D) are shown respectively. FIG. 11 is a table in which various explanatory variables X6 to X11 at 100 points for determination expression creation are quantified, and shows data of each of the 1st to 50th points. FIG. 12 is a table in which various explanatory variables X6 to X11 at 100 points for determination expression creation are quantified, and shows data of 51st to 100th points. FIG. 13 is a diagram for explaining the specific determination formula (13A), a diagram for explaining the threshold value (13B, 13C), and the magnitude of the range of the formula 3 of (13A) for 11 road collapse factors. FIG. 13D is shown respectively. FIG. 14 shows a table in which various explanatory variables X6 to X11 at 30 evaluation target points where the risk is evaluated are digitized. FIG. 15 shows a risk evaluation map in which unit blocks including respective points are displayed on the map in different colors or in different shades.

1: Road collapse risk assessment device (also simply referred to as "device")
10: Judgment factor data reception unit (judgment factor data reception means)
11: General judgment formula storage unit (general judgment formula storage means)
13: First substitution unit (first substitution means)
14: Specific determination formula determination unit (specific determination formula determination means)
15: Threshold value determination unit (threshold value determination means)
16: Specific determination formula storage unit (specific determination formula storage means)
18: Risk factor data reception unit (risk factor data reception means)
19: Second substitution unit (second substitution means)
20: Judgment value determination unit (judgment value determination means)
21: Map information storage unit (map information storage means)
23: Map display section (map display means)

  Next, an embodiment of the present invention will be described with reference to the drawings. It should be noted that the embodiments described below do not limit the invention according to the claims, and all of the elements and combinations described in the embodiments are essential to the solution means of the invention. Not necessarily.

<First embodiment>
First, a road collapse risk evaluation apparatus, a road collapse risk evaluation method, and a road collapse risk evaluation computer program according to a first embodiment of the present invention will be described.

<1. Road Depression Risk Assessment Concept>
FIG. 1 shows a conceptual diagram for obtaining a road collapse risk degree in the embodiment of the present invention.

(1) Perill Data and Various Hazard Data In this embodiment, various data represented by peril data, hazard data, fieldworker hazard data, and site MH management hazard data are required to obtain the road collapse risk level. Is. All of these data contribute to road collapse. Peril data means the data that causes a hazard. The perill data in this embodiment is collected by the road administrator and the sewer administrator in cooperation with preventive maintenance against road collapse. Specific examples of the peril data include a depression history, a road improvement history, and a cavity investigation history (follow-up observation, cause investigation, repair, etc.).

  Hazard data means a dangerous situation (risk factor) when road collapse is considered as a dangerous event. Hazard data can include pipe type, diameter, aged value, deterioration damage, construction history, etc. as attributes of sewer pipes. The same applies to water pipes. These data are available from the ledger. The hazards that cause road collapse include other infrastructure such as gas pipes, and the various data described above are required. In addition, the backfill material for various buried objects, groundwater level conditions, traffic volume, and road surface temperature may also be included in the hazard data.

  On-site survey engineering hazard data (also referred to as on-site survey engineering road survey hazard data) is survey data relating to roads and refers to the risk situation of road collapse. This data requires road surface information by MMS (Mobile Mapping System) and its position information. This data is obtained, for example, by surveying the survey vehicle at a traveling speed of 40 km / h or more. No traffic restrictions are required for data collection. Using a survey vehicle, it is possible to measure a three-dimensional terrain model with an onboard laser scanner and camera. The MMS information obtained in this way makes it possible to know the depression situation on the road. Furthermore, the survey vehicle can be equipped with a GPR (Ground Penetrating Rader). GPR extracts abnormal signals (including signals such as cavities and looseness, as well as information on buried pipes, etc.) from the data measured by the survey vehicle, and identifies abnormal signals having the characteristics of cavities from them. . The abnormal signal is obtained by surveying the survey vehicle at a traveling speed of about 40 km / h. No traffic restrictions are required for data collection. As the exploration accuracy, it is possible to explore a cavity having a depth of about 1.5 m and a length of 50 cm × width 50 cm × thickness of 10 cm or more. If a more accurate method is adopted, it is possible to estimate the state of void formation in the soil layer up to about 3.0 m from the data up to the exploration depth of 1.5 m.

  The base MH management hazard data is data relating to the real-time environment inside and outside the pipeline, and refers to a risk situation of road collapse. For example, a sewer pipe is easily deteriorated by corrosion of concrete or the like caused by hydrogen sulfide. Therefore, it is extremely important to inspect and investigate the hydrogen sulfide concentration. In addition, the sewerage pipeline, which has deteriorated in the pipeline, is likely to be damaged by road load. From such a point, real-time information such as hydrogen sulfide in the sewer pipeline can be hazard data for road depression. In addition, water temperature, water quality, and water level in the sewer pipeline can also be a hazard data of this type.

(2) Conversion of data into coordinate values The above-mentioned perill data and various types of hazard data are obtained from a ledger or by a survey and provided for coordinate conversion. An exploration vehicle equipped with MMS and GPR can constantly record the traveling position by GPS (Global Positioning System). Therefore, it is possible to convert the underground cavity position into coordinates. Further, roads and various underground buried objects can be converted into coordinate values in a ledger or after being extracted from the ledger. In this way, by converting the above-mentioned perill data and various hazard data into coordinate values, it is possible to efficiently manage the risk of road collapse by combining data surveys and field surveys. Further, it is possible to automate the calculation of the risk of sinking based on the road sinking sign determination formula (simply referred to as a sign determination formula). Such coordinate value conversion will be described later in detail.

(3) Creation of Road Depression Prediction Judgment Formulation of the road depression Prediction judgment expression means that the information that has been converted into the coordinate values is input to the general expression of the judgment expression, and the road depression occurs by applying the quantification type II. Determining the sign judgment formula. In this work, the objective variable to be estimated is represented as peril data (F) to represent the level of the situation of road collapse in about two to three levels, and various types of hazard data are considered as information regarding the hazard data (Xi). That is, this step is to output an expected collapse condition level from Xi data qualitatively given.

(4) Quantifying road collapse risk by unit block In this quantification, the area to be surveyed is divided into multiple blocks, and the coordinate values of the points in each block are substituted into the previously determined determination formula, The risk level is quantified in units of blocks. As a result, it is possible to quantitatively and objectively grasp how much each block has a risk of road collapse when the road is divided into unit blocks in the area having the road.

  As described above, the creation of the above-described road depression sign determination formula is an operation of specifying the determination formula using perill data and hazard data of a plurality of points for the determination formula creation. On the other hand, the digitization of the road collapse risk for each unit block is based on the peril data and various hazard data of the target point for which the collapse risk is to be calculated (may be partially overlapped with the point for creating the above judgment formula). This is a work of substituting the above-mentioned specified judgment formula and expressing the risk of depression by a numerical value.

  1A and 1B are diagrams for explaining a general formula (general judgment formula) of the judgment formula described based on FIG. 1. FIG. 1C and FIG. 1D show an example of various data used for creating a road depression sign determination formula. FIG. 2 shows data different from those in FIGS. 1C and 1D and shows an example of various data used for creating a road depression sign determination formula.

  The information shown in FIGS. 1A-1D and FIG. 2 is largely divided into external criteria and explanatory variables. In the most typical example, the external standard is the presence or absence of a road depression, and means the result of whether or not the phenomenon of depression occurs. Further, the external standard may include not only the presence or absence of road collapse, but also the situation of each stage where there is a risk of road collapse. In FIG. 1A, when there are only two types of external reference (Y), that is, the presence or absence of a road depression, k = 2, and Y is B1 or B2. Here, B1 means no road collapse, and B2 means road collapse. On the other hand, if the external criterion (Y) includes the presence or absence of a road depression and another condition of “indication of depression”, k = 3, and Y becomes B1, B2, and B3. . B3 means that there is a sign of road collapse. The explanatory variable (X) means an investigation result of a situation assumed as a cause of road collapse. The explanatory variable (X) is a factor considered to be the cause of the road collapse, and preferably includes at least two (X1 and X2) in Y = f (Xi). However, the number of X is not limited as long as it is 1 or more. X can be represented by X1, X2, X3, ..., Xn (n is the number of i-th explanatory variable Xi and is an integer of 1 or more), and X1, X2, X3, etc. are generalized, It can be referred to as "Xi". The relational expression between the external reference (Y) and the explanatory variable (X) is the expression shown in FIG. 1A, more specifically the expression shown in FIG. 1B. In these equations, A is a coefficient by which the explanatory variable Xi is multiplied. The coefficient by which X1 is multiplied is represented by A1, the coefficient by which X2 is multiplied is represented by A2, and the coefficient by which Xn is multiplied is represented by An. A1, A2, A3, etc. can be generalized and referred to as "Ai".

  The equation Y = f (Xi) can be expressed as the following equation (A). In Expression (A), xij indicates the j-th categorical variable of Xi, aij indicates the category coefficient of xij, and mi indicates the number of categories of Xi. In the formula (A), the comma (,) existing between the category coefficients aij may be omitted in the notation.

  The categorical variable (xij) is a variable that constitutes the explanatory variable Xi, but various numbers can be taken depending on the type of Xi. For example, in the example shown in FIG. 1C, X1 has three categorical variables x11, x12, and x13. X2 has two categorical variables x21 and x22. X3 has three categorical variables x31, x32, and x33. X4 has three categorical variables x41, x42, and x43. However, in the example shown in FIG. 1D, partly different from FIG. 1C, for example, X4 has four categorical variables x41, x42, x43, and x44. In this way, the number of categorical variables can be variously changed according to the explanatory variables. Each categorical variable xij that constitutes the explanatory variable Xi is all zero, or only one is 1 and the others are zero. This will be described in detail later. The criteria for classifying categorical variables into a plurality and the number of categorical variables can also be changed variously. In this embodiment, three types of explanatory variables, FIG. 1C, FIG. 1D, and FIG. 2 are illustrated.

  Here, as an example of a factor of road collapse (that is, an example of an explanatory variable), the presence of a cavity (X1), backfill soil (X2), the status of groundwater (X3), the number of years (X4) Thickness), soil cover thickness (X5), pipe section (X6), pipe type (X7), sewer pipe damage status (X8), traffic vibration (X9), summer season The temperature (X10) and the active fault hazard (X11) are listed. With respect to X1 to X11 that appear later, the above explanatory variables are meant.

  As shown in FIG. 2, the explanatory variable (X1) indicating the presence of a cavity is that there is no cavity, there is a cavity at a position less than 1/2 of the soil cover, and there is a cavity at a position of 1/2 or more of the soil cover. There are three types of cases. The position where the soil cover is less than 1/2 is a position which is lower than 1/2 of the thickness of the earth and sand covering the pipe such as a sewer pipe. The position of the overburden ½ or more is a position ½ of the thickness of the earth and sand covering a pipe such as a sewer pipe or a position shallower than that. The explanatory variable (X2) of backfilled soil is classified into two types, that is, the type of the soil is gravel soil and the type of the soil is sandy soil. The explanatory variable (X3), which is the state of groundwater, is divided into three types: the case where there is no groundwater, the case where there is groundwater, and the case where there is groundwater and fluctuations. When there is groundwater, it is divided into cases where there is fluctuation in groundwater and cases where there is no fluctuation in groundwater. The risk of sinking is higher when there is groundwater fluctuation. Therefore, the case where there is groundwater is divided into two cases where there is groundwater from the viewpoint of whether or not there is fluctuation in groundwater. The explanatory variable (X4), which is the number of elapsed years (the number of years since the pipeline was installed), is divided into three types: less than 20 years, 20 years or more and less than 40 years, and 40 years or more. The explanatory variable (X5), which is the soil cover thickness, is classified into three types, that is, 2 m or more, 1 m or more and less than 2 m, and 1 m or less. The explanatory variable (X6), which is the part of the conduit, is divided into three types, that is, the mounting pipe, the main pipe, and the other. The explanatory variable (X7), which is a pipe type, is divided into three types, namely, a ceramic pipe, reinforced concrete, and other types. The explanatory variable (X8), which is the sewer pipe damage status, is divided into two types: one that is not damaged and one that is damaged. The explanatory variable (X9) called traffic vibration is divided into two types, namely, a large number and a small number with respect to a predetermined standard. The explanatory variable (X10) called summer temperature is divided into two types, namely, a case where the temperature is higher than a predetermined temperature (for example, the temperature can be set to a temperature in the range of 25 to 35 ° C. of the monthly average temperature) and a case where the temperature is low. The explanatory variable (X11), which is an active fault hazard, is divided into two types, one with an active fault and the other with no active fault. In addition, the variable which comprises each said explanatory variable can be called a categorical variable. In the above example, the number of categorical variables is two or three in each explanatory variable, but it may be four or more. Further, the number of categorical variables in each explanatory variable is not limited to the above example. For example, X1 has three categorical variables, but may have only two or four or more. The same applies to other explanatory variables. However, the explanatory variables for creating the judgment formula and the explanatory variables for evaluating the degree of risk need to match the types and the criteria and the number of each categorical variable.

<2. Road collapse risk evaluation device and road collapse risk evaluation method>
FIG. 3 shows a functional configuration of the road collapse risk assessment apparatus according to the embodiment of the present invention.

  The road collapse risk evaluation device 1 according to this embodiment is a device for evaluating the risk of road collapse. The road collapse risk degree evaluation device (hereinafter, also simply referred to as “device”) 1 includes a judgment formula factor data reception unit 10, a general judgment formula storage unit 11, a general judgment formula reading unit 12, a first substitution unit 13, and a specific judgment. Formula determination unit 14, threshold value determination unit 15, specific determination formula storage unit 16, specific determination formula reading unit 17, risk factor data reception unit 18, second substitution unit 19, determination value determination unit 20, map information storage unit 21. , A map information reading unit 22 and a map display unit 23.

  Judgment formula factor data receiving unit 10, general judgment formula reading unit 12, first assigning unit 13, specific judgment formula determining unit 14, threshold value determining unit 15, specific judgment formula reading unit 17, risk factor data receiving unit 18, In the second substituting unit 19, the determination value determining unit 20, the map information reading unit 22, and the map displaying unit 23, the central processing unit (CPU) mounted on the electronic circuit board in the computer is a computer program (for evaluating road collapse risk). Each process is performed by executing a computer program). The general judgment formula storage unit 11, the specific judgment formula storage unit 16, and the map information storage unit 21 are memories such as a RAM or a hard disk capable of reading and writing data. The general determination formula storage unit 11 and / or the map information storage unit 21 is not limited to a RAM or a hard disk, and may be, for example, a ROM in which data is unidirectionally read. Further, the judgment formula factor data accepting unit 10 and / or the risk factor factor data accepting unit 18 receive respective data from various devices (server, keyboard, pointing device, touch panel, etc.) connected to the device 1 by wire or wirelessly. It is possible to accept. For example, even if the user inputs the numerical values shown in FIG. 7 (7B) described below from the keyboard and the judgment formula factor data receiving unit 10 and / or the risk factor data receiving unit 18 receive the input numerical values. good. As described above, the judgment formula factor data receiving unit 10 and / or the risk factor data receiving unit 18 may be connected to an input device (not shown in FIG. 3), another computer, or a communication device.

  The determination formula factor data reception unit 10 is data required to determine a specific determination formula as a function formula for determining the risk of road collapse and the function of the factor of road collapse and the risk. In addition, it is a component functioning as a judgment formula factor data receiving unit that receives judgment formula factor data that quantifies the factors of road collapse at a plurality of sampling points sampled to create a specific judgment formula. Here, as the factor of the road collapse, preferably, the presence of underground cavities, the type of backfill soil, the condition of groundwater, the number of years of pipeline construction, the overburden thickness on the pipeline, the site of the pipeline, the pipe type, It is 2 or more of the condition of pipeline damage, traffic vibration, summer temperature, and active fault hazard. Further, the judgment formula factor data is data that is digitized for each factor, and is data that is “1” when it is applicable and is “0” when it is not applicable. Further, the specific determination formula refers to a formula exemplified in FIG. 7 (7C). These will be described later in detail with reference to FIG. 7.

  The general judgment formula storage unit 11 is a component unit that functions as a general judgment formula storage unit that stores a general judgment formula that is a basis of the specific judgment formula. Here, the general determination formula refers to a formula exemplified in FIG. 7 (7A) described later. The general judgment formula storage unit 11 is described as a constituent unit that stores the general judgment formula, but a constituent unit that can store other data, for example, a judgment formula factor data accepting unit 10 and a general judgment formula reading unit. 12, first assignment unit 13, specific determination formula determination unit 14, threshold determination unit 15, specific determination formula reading unit 17, risk factor data reception unit 18, second substitution unit 19, determination value determination unit 20, map information. It may be a configuration unit capable of storing one or more computer programs (computer program for evaluating a road collapse risk) necessary for performing each processing of the reading unit 22 and the map display unit 23.

  The general determination formula reading unit 12 is a component that reads the data of the general determination formula from the general determination formula storage unit 11.

  The first substituting unit 13 is a component that functions as a first substituting unit that substitutes the judgment formula factor data into the general judgment formula read from the general judgment formula storage unit 11.

  The specific determination formula determination unit 14 is a component that functions as a specific determination formula determination unit that substitutes the determination formula factor data into the general determination formula to perform an operation to determine the specific determination formula.

  The threshold value determination unit 15 is a component unit that functions as a threshold value determination unit that classifies the determination values calculated by the specific determination expression into a predetermined range and determines the threshold value for the occurrence of the depression based on the presence or absence of the actual depression. The threshold value is a judgment value that means a boundary at which the risk of occurrence of depression is high. The determination value is the value of Y in the specific determination expression illustrated in FIG. 7 (7C). In the example of FIG. 7 (7D), the threshold value is determined to be −0.2 based on the fact that the road depression is recognized when the determination value is −0.2 or less. However, the presence or absence of the depression is not always clearly separated at the judgment value = −0.2. For example, even if the judgment value = 0, there is a point where the depression has occurred, while even if the judgment value = −0.3, there is a point that the depression does not occur. Despite this situation, the reason why the threshold is determined to be −0.2 is that when the threshold is set to −0.2, the correct answer rate for the presence or absence of a depression is high and the false determination rate is low. If the threshold value is set to 0 (zero), a point where no depression occurs within the range of the judgment value ≦ 0 is likely to be included as an error. On the other hand, if the threshold value is -0.4, the point where the depression occurs within the range of the judgment value ≥ -0.4 is likely to be included as an error. Considering the magnitude of such an error, the determination value (-0.2) that minimizes the determination error is used as the threshold value. This point will be described later in detail with reference to FIGS. 7D and 7E. The threshold value determination unit 15 is not an essential component and may not be provided in the device 1.

  The specific determination formula storage unit 16 is a component functioning as a specific determination formula storage unit that stores at least the specific determination formula. The specific determination formula storage unit 16 may further store a threshold value.

  The specific determination formula reading unit 17 is a component that reads out the specific determination formula from the specific determination formula storage unit 16. The specific determination formula reading unit 17 may further read the threshold value.

  The risk factor data reception unit 18 is a component unit that functions as a risk factor data reception unit that receives risk factor data quantifying the factors of road collapse at a plurality of evaluation target points to be evaluated. The judgment formula factor data accepting unit 10 and / or the risk factor factor data accepting unit 18 are described as components that can accept unique data, respectively, but can also accept other data. It may be a constituent unit, for example, a constituent unit that also accepts data arbitrarily input by the user.

  The second substituting unit 19 is a component that functions as a second substituting unit that substitutes the risk factor data into the specific determination formula read from the specific determination formula storage unit 16.

  The determination value determination unit 20 is a component unit that functions as a determination value determination unit that performs a calculation based on the processing of the second substitution unit 19 and determines a determination value that is a numerical value of the risk of depression at each evaluation target point.

  The map information storage unit 21 is a component unit that functions as a map information storage unit that stores information about a map including each evaluation target point.

  The map information reading unit 22 is a component that reads out predetermined map information from the map information storage unit 21.

  The map display unit 23 functions as a map display unit that performs color-based or shade-based display on the map read from the map information storage unit 21 based on the determination value indicating the risk of sinking at each evaluation target point. It is a component. The color or shade information can be readably stored in the map information storage unit 21 or another storage unit (not shown in FIG. 3). The above-mentioned map information reading unit 22 can read out the color information or the shade information together with the map information from the map information storage unit 21.

  The map display unit 23 may be a component unit that displays only the determination value indicating the risk level without displaying the color. In addition, the map display unit 23 may be a configuration unit that displays both the color and the determination value. Further, the map display unit 23 may display monochrome shades instead of colors. In that case, the judgment value may be displayed together with the contrast of monochrome, or may not be displayed. Further, when the device 1 includes the threshold value determination unit 15, the map display unit 23 may display the color and shading largely before and after the threshold value.

  As a modified example of the above-described embodiment, the device 1 may include the first collapse factor numerical value conversion unit 30, the first storage unit 31, the second collapse factor numerical value conversion unit 40, and the second storage unit 41. The first subsidence factor numerical conversion unit 30 converts the road subsidence factor into a numerical value shown in FIG. 7 (7B) described below, and transmits the numerical value data to the determination formula factor data reception unit 10. It is a component functioning as a numerical conversion means. The first storage unit 31 is a first readable / writable first storage unit that stores information in a data table for converting the road collapse factor into the numerical value. The first sinking factor numerical value conversion unit 30 refers to the data table stored in the first storage unit 31, digitizes each road factor, and transmits the digitized data to the determination formula factor data reception unit 10. To do. Similarly, the second collapse factor numerical value conversion unit 40 functions as a second collapse factor numerical value conversion unit that converts the road collapse factor into a numerical value and transmits the numerical value data to the risk factor data reception unit 18. It is a department. The second storage section 41 is a second readable / writable second storage means for storing information that stores a data table for converting the road collapse factor into the numerical value. The second collapse factor numerical value conversion unit 40 refers to the data table stored in the second storage unit 41, digitizes each road factor, and transmits the digitized data to the risk factor data reception unit 18. To do. The first subsidence factor numerical value conversion unit 30 and the second subsidence factor numerical value conversion unit 40 are units that perform respective processes by the CPU executing a computer program (road subsidence risk evaluation computer program). The first storage unit 31 and the second storage unit 41 are memories typified by RAMs and hard disks that enable reading and writing of information.

  More specifically, the device 1 is a road collapse risk evaluation device for evaluating the risk of road collapse, and is a formula for determining the risk of road collapse, which is a factor and risk of road collapse. It is necessary to determine the specific judgment formula as a function formula with, and accepts the judgment formula factor data quantifying the factors of road collapse at a plurality of sampling points sampled to create the specific judgment formula. Judgment formula factor data receiving means (corresponding to the judgment formula factor data receiving unit 10) and a general judgment formula shown in the formula (A) which is the basis of the specific judgment formula (Y is an external criterion indicating the presence or absence of a road depression) Where X1 to Xn: Xi are explanatory variables that cause road collapse, A1 to An are coefficients by which X1 to Xn are respectively multiplied, and n is the number of i-th explanatory variable Xi and is an integer of 1 or more, xij is the j-th category of Xi Lee variable, aij is the category coefficient of xij, mi is the number of category Xi, respectively. } Is stored in the general judgment formula storage means (corresponding to the general judgment formula storage unit 11), and the judgment formula factor data constituted by the categorical variables is substituted into the general judgment formula read from the general judgment formula storage means. The first substitution means (corresponding to the first substitution unit 13) and the factor data for the determination formula are substituted into the general determination formula to quantify the theory of group II to analyze the variation between the two groups relative to the total variation. Specific determination formula determining means (corresponding to the specific determination formula determination unit 14) that determines the specific determination formula that specifies the category coefficient (aij) by performing an operation so as to maximize it, and a specific storage formula that stores the specific determination formula. Judgment expression storage means (corresponding to the specific judgment expression storage part 16) and risk factor data reception means for accepting risk factor data quantifying the factors of road collapse at a plurality of evaluation points to be evaluated (danger) Factor data And a second substituting unit (second substituting unit) for substituting the risk factor data configured by the categorical variables at the evaluation target point into the specific determination formula read from the specific determination formula storage unit. (Corresponding to 19) and a judgment value deciding means (corresponding to the judgment value deciding portion 20) for deciding a judgment value which is calculated based on the processing of the second substituting means and quantifies the risk of depression at each evaluation target point. ,including.

  FIG. 4 shows a flow of main steps of the road collapse risk assessment method according to the embodiment of the present invention.

  The road collapse risk evaluation method according to this embodiment is a method for evaluating the risk of road collapse using a device for evaluating the risk of road collapse. As shown in FIG. 4, this evaluation method is a source of a specific judgment formula, which is a judgment formula factor data reception step (S100) for receiving data of each factor (decision formula factor data) at a point for making a judgment formula. A first substitution step (S200) of substituting the determination formula factor data into the general determination formula, a specific determination formula determination step (S300) of determining a specific determination formula by calculation, and a threshold determination step (S400) of determining a depression occurrence threshold value. ), A risk factor data receiving step (S500) for receiving risk factor data quantifying the factors of road collapse at a plurality of evaluation target points, and substituting the risk factor data for the specific determination formula Second substitution step (S600), decision value decision step (S) of deciding a decision value based on the risk of depression at each evaluation target point by calculation 00), a method of evaluating a road collapse risk by performing a map display step (step S800) for displaying by color or shade based on a judgment value indicating a risk of collapse at each evaluation target point on the map. is there. Note that step S400 and step S800 are not essential steps. Further, as in the case of the device 1, the map display step may be a step of displaying only the determination value indicating the risk level without displaying the color and the light and shade. Further, the map display step may be a step of displaying both the color and shade and the judgment value. Furthermore, the map display step may be a step of displaying monochrome shading instead of the color. In that case, the judgment value may be displayed together with the contrast of monochrome, or may not be displayed. Further, when the threshold value determining step (S400) is performed, the map displaying step may display the color and the lightness and shade before and after the threshold value. The above steps will be described below with reference to FIGS.

(1) Step for receiving judgment factor data (S100)
This step is a step of receiving determination formula factor data quantifying the factors of road collapse at a plurality of (100 in this embodiment) sampling points sampled to create a specific determination formula. This step can be performed by the determination formula factor data reception unit 10 of the device 1. The judgment formula factor data is data necessary for determining the specific judgment formula. The specific determination formula is a formula for determining the risk of road collapse and is a functional formula of the factor of road collapse (corresponding to explanatory variable Xi) and the risk (corresponding to external criterion Y).

  FIG. 5 and FIG. 6 respectively show tables in which the external reference Y and various explanatory variables X1 to X5 at 100 points for making a judgment formula are digitized. FIG. 5 shows data at each of the 1st to 50th points, and FIG. 6 shows data at each of the 51st to 100th points.

  In the tables of FIGS. 5 and 6, the presence or absence of a depression at the 1st to 100th points on the road in a specific area in a certain city and the five explanatory variables that can be the factors are represented by the numbers 1 to 3. . These points 1 to 100 are sampling points sampled for creating the specific determination formula. In the tables of FIGS. 5 and 6, for example, regarding the presence or absence (Y) of a depression, “1” means that there is no depression, and “2” means that there is a depression. Regarding the presence of cavities (X1), "1" means that there are no cavities, "2" means that there are cavities at a position less than 1/2 of the earth cover thickness, and "3". Means that there is a cavity at a position of ½ or more of the earth cover thickness. Regarding the backfill soil (X2), "1" means that the soil type is gravel soil, and "2" means that the soil type is sandy soil. Regarding the groundwater situation (X3), "1" means no, "2" means yes, "3" means yes / variable. Regarding the elapsed years (X4), "1" means less than 20 years, "2" means 20 years or more and less than 40 years, and "3" means 40 years or more. Regarding the overburden (X5), "1" means 2 m or more, "2" means 1 m or more and less than 2 m, and "3" means less than 1 m.

  Therefore, for example, at the first sampling point in the table of FIG. 5, since Y and X1 to X5 are all “1”, there is no depression, there is no cavity, and the backfill soil is gravel soil and groundwater. There is no such thing, the number of years elapsed is less than 20 years, and the soil cover is a point of 2 m or more. Other sampling points are similarly interpreted by the numerical values of 1 to 3. What is important here is that the numerical values 1 to 3 in FIGS. 5 and 6 are merely for classifying the external reference and each explanatory variable by type, and have an meaning in the absolute value itself of the numerical value. That is not the case. Therefore, the alphabets a, b, and c may be used instead of the numerical values 1, 2, and 3. It is also important that the numbers 1 to 3 in the tables of FIGS. 5 and 6 are not directly substituted into the general judgment formula. This point will be described later.

  FIG. 7 is a diagram (7A) for explaining a general determination formula, a diagram (7B) for explaining factorized data, a diagram (7C) for explaining a specific determination formula, and a threshold value. The drawings (7D, 7E) are shown respectively.

  In the examples of FIG. 5 and FIG. 6, the general judgment formula which is the basis of the specific judgment formula is represented by Y = f (Xi) = A1 * X1 + A2 * X2 + A3 * X3 + ... + An * Xn. Here, n is the number of explanatory variables (a positive integer). A1, A2, A3, ..., An (generalizing them are “Ai”) are coefficients of X1, X2, X3, ..., Xn (generalizing these, “Xi”), respectively. . In the example of FIGS. 5 and 6, since there are five explanatory variables, n = 5. Here, X1 has three variables x11, x12, and x13 that can take 1 or zero (this is referred to as a categorical variable, but may simply be referred to as a variable hereinafter). X2 has two variables that can be 1 or zero, x21 and x22. X3 has three variables that can be 1 or zero, x31, x32, and x33. X4 has three variables, x41, x42, and x43, which can be 1 or zero. X5 has three variables that can be 1 or zero, x51, x52, and x53.

  In (7A), only X1 to X3 are displayed, but if Y = f (Xi) is accurately described, Y = f (Xi) = A1 * X1 + A2 * X2 + A3 * X3 + A4 * X4 + A5 * X5 = (a11 * X11 + a12 * x12 + a13 * x13) + (a21 * x21 + a22 * x22) + (a31 * x31 + a32 * x32 + a33 * x33) + (a41 * x41 + a42 * x42 + a43 * x43) + (a51 * x51 + a52 * x52 + a53 * x53).

  Here, when X1 is described as a representative, any one of x11, x12, and x13 is 1, and the other two are zero. Therefore, when there is no cavity, x11 = 1, x12 = 0, and x13 = 0. Similarly, when there is a cavity at a position less than 1/2 of the soil cover, x11 = 0, x12 = 1, and x13 = 0. When there is a cavity at a position of 1/2 or more of the soil cover, x11 = 0, x12 = 0, and x13 = 1. That is, as shown in (7B), there are three types of combinations of x11, x12, and x13, in which only one of x11, x12, and x13 is 1 and the other two are 0. This also applies to X2 to X5. In the case of X2, one of x21 and x22 is 1, and the other is zero. In the case of X3, any one of x31, x32, and x33 is 1, and the other two are zero. In the case of X4, any one of x41, x42, and x43 is 1, and the other two are zero. In the case of X5, any one of x51, x52, and x53 is 1, and the other two are zero.

  Step S100 is (x11, x12, x13), (x21, x22), (x21, x22), as determination formula factor data quantifying the factors of road collapse and the presence / absence of depression at each sampling point sampled for creating the specific determination formula, This is a step of receiving (x31, x32, x33), (x41, x42, x43), (x51, x52, x53) and Y. For example, the sampling point No. shown in FIG. 1, in the group “no depression” of the two possible classifications (2 groups) of the objective variable Y, (1,0,0), (1,0), (1,0,0), ( The data of (1, 0, 0) and (1, 0, 0) are accepted by the determination formula factor data accepting unit 10. Similarly, for example, the sampling point No. shown in FIG. In 43, in the group “with a depression” of the two possible classifications (2 groups) of the objective variable Y, (0,1,0), (0,1), (0,1,0), ( The judgment formula factor data of 0, 1, 0) and (0, 1, 0) is received by the judgment formula factor data reception unit 10. The reception of such data is similarly performed at 98 other sampling points.

(2) First substitution step (S200)
In this step, the general judgment formula (see 7A) is added to the judgment formula factor data, for example, the sampling point No. In the case of 1, in the step of substituting the data of (1,0,0), (1,0), (1,0,0), (1,0,0), (1,0,0) is there. This step can be performed by the first substituting unit 13 of the device 1. In the example of 100 sampling points in total shown in FIG. 5 and FIG. 6, there are 45 points without depression and 55 points with depression. Therefore, in the group without depression (referred to as Y1 group), 45 Y = f (Xi) obtained by substituting the determination formula factor data can be obtained. Similarly, in the group with depressions (referred to as Y2 group), 55 Y = f (Xi) values obtained by substituting the determination formula factor data are obtained.

(3) Specific determination formula determination step (S300)
This step is a step of substituting the judgment formula factor data into the general judgment formula and performing a calculation to determine the specific judgment formula. Specifically, in this step, the specific judgment formula is obtained by the quantification theory type II analysis. This step can be performed by the specific determination formula determination unit 14 of the device 1. Now, let the coefficients (a11, a21, etc.) in A1 to A5 be aij. i is the order of A1-A5, and is a positive integer in any one of 1-5. j is the order of the coefficients in one Ai. Since A1 has three coefficients, j is a positive integer of any one of 1 to 3. In order to best discriminate the classification (2 groups) of the objective variable Y, aij is set so as to maximize the inter-group variation of the two groups relative to the total variation. That is, aij is determined so as to maximize the correlation ratio (η ² ) = (inter-group dispersion SB / total dispersion ST). At this time, generally, the total dispersion ST = the intra-group dispersion SW + the inter-group dispersion SB. If the equation is solved under such conditions so that the correlation ratio becomes maximum, this problem results in an eigenvalue problem, and the coefficient aij to be obtained is obtained as a component of the eigenvector corresponding to the maximum eigenvalue (η ² ). As a result, in the present calculation example, the specific determination formula as shown in Formula 1 of (7C) is obtained. Note that such an analysis method is disclosed in, for example, “Multivariate statistical analysis method” (author: Yutaka Tanaka / Kazumasa Wakimoto, publisher: Hyundai Mathematics Co., Ltd.). The specific determination expression is a relational expression between the explanatory variable Xi and the objective variable Y in the state where the coefficient aij is specified. Y means a “judgment value” indicating the risk of road collapse in the specific judgment formula. That is, Y, which means the presence or absence of a road depression or a sign in the general judgment formula, becomes a “judgment value” indicating the road collapse risk in the specific judgment formula. For this reason, Y in the general determination formula may be distinguished from “Y1” and Y in the specific determination formula may be distinguished from “Y2”.

(4) Threshold value determination step (S400)
This step is a step of determining a threshold value for occurrence of depression. The threshold value means a judgment value for judging the risk of occurrence of depression. This step can be performed by the threshold value determining unit 15 of the device 1. In this step, the judgment value calculated by the specific judgment formula is classified into a predetermined range, and the threshold value for the occurrence of the depression is determined based on the presence or absence of the actual depression. As shown in (7D, 7E), in this embodiment, the determination values are classified into 16 within a predetermined range of 0.2. In addition, No. Road collapse occurs from 1 to 7 (that is, the determination value is −0.2 or less). However, the depression of the road does not mean that 100% occurs when the determination value is −0.2 or less and does not occur 100% when the determination value is greater than −0.2. When the judgment value is -0.2 or less, the road collapse may not occur, and when the judgment value is larger than -0.2, the road collapse may occur. Under such a situation that the set of road depressions and the set of non-depressions of roads overlap each other, the judgment value is set to No. It is appropriate to determine the position where the determination error becomes the smallest as the threshold value when the level is set to each of 1 to 16. In the example shown in (7C, 7E), if the threshold value is set to a value smaller than −0.2, the determination error becomes larger than that when the threshold value is set to −0.2 (in this case, the range equal to or greater than the threshold value). In, there are many cases where collapse occurs). Further, even if the threshold value is set to a value larger than -0.2, the determination error becomes larger than that when the threshold value is set to -0.2 (in this case, the depression below the threshold value does not occur. Many are included). When the threshold value is set to −0.2, it is possible to separate with high probability the case where the depression occurs and the case where the depression does not occur. In addition, the determination predictive value of the presence or absence of depression is 96% when the threshold value is set to −0.2 at 100 sample points. This means that the error is 4%. Steps S100 to S400 are the processes from the general judgment formula to the specific judgment formula based on the information of the sample points.

(5) Step for accepting risk factor data (S500)
This step is a step of receiving risk factor data in which the factors of road collapse are quantified at a plurality of evaluation points to be evaluated. This step can be performed by the risk factor data reception unit 18 of the device 1. In this embodiment, risk factor data for 30 evaluation points are accepted. It is preferable that the evaluation target points are geographically close to each other, such as the same municipality as the sampling point or the same district within the same municipality.

  FIG. 8 shows a table in which various explanatory variables X1 to X5 at 30 evaluation target points for which the degree of risk is evaluated are digitized.

  Numerical values 1 to 3 of X1 to X5 in FIG. 8 have the same meaning as the numerical values in FIGS. 5 and 6. For example, the evaluation target point No. In No. 1, "1" is entered in X1 to X4, and only X5 is "2". This is the No. At the point 1, it means that there is no cavity, the backfill soil is gravel soil, there is no groundwater, the number of elapsed years is less than 20 years, and the overburden is 1 m or more and less than 2 m. The other 29 locations are similarly interpreted based on the numbers in the table.

  What is important here is that the numerical values 1 to 3 in FIG. 8 are merely for classifying each explanatory variable by type, as in FIGS. 5 and 6, and the absolute value of the numerical value itself has meaning. Is not. Therefore, the alphabets a, b, and c may be used instead of the numerical values 1, 2, and 3. It is also important that the numbers 1 to 3 in the table of FIG. 8 are not directly substituted into the specific determination formula.

  The step S500 accepts (x11, x12, x13), (x21, x22), (x31, x32, x33), (x41, x42, x43), (x51, x52, x53) as risk factor data. It is a step. For example, the evaluation target point No. shown in FIG. In 1, the data of (1,0,0), (1,0), (1,0,0), (1,0,0), (0,1,0) is the risk factor data reception unit. Accepted by 18. Similarly, for example, the evaluation target point No. shown in FIG. 7, the risk factor data for (1,0,0), (0,1), (0,1,0), (0,1,0), and (0,0,1) are for risk. It is accepted by the factor data accepting unit 18. The reception of such data is similarly performed at the other 28 sampling points.

(6) Second substitution step (S600)
This step is a step of substituting risk factor data into the specific determination formula. This step can be performed by the second substitution unit 19 of the device 1. This step uses the risk factor data, for example, the evaluation target point No., in the specific determination formula (see 7C). In the case of 1, in the step of substituting the data of (1,0,0), (1,0), (1,0,0), (1,0,0), (0,1,0) is there. In this step, similar substitution is performed for the other 29 evaluation target points.

(7) Determination value determination step (S700)
This step is a step of determining a judgment value based on the risk of sinking at each evaluation target point by calculation using a specific judgment formula. This step can be performed by the determination value determination unit 20 of the device 1. As a result of this calculation, Y (determination value meaning risk) of the specific determination formula is obtained for each evaluation target point.

(8) Map display step (step S800)
This step is a step of displaying by color or by light and shade based on the determination value indicating the risk of depression at each evaluation target point on the map. This step can be performed by the map display unit 23 of the device 1.

  FIG. 9 shows a map (9A) of an area including points to be evaluated and a risk evaluation map (9B) in which unit blocks including each point are displayed on the map in different colors or shades.

  As shown in (9A), the points to be evaluated with the symbols 1 to 30 are clearly indicated on the map. The map of (9B) is obtained in S700 in a unit block centered on each evaluation target point on the map shown in (9A) (for example, a length of 50 m on the left and right, a radius of 50 m, etc. is arbitrarily determined). The displayed judgment value (which may be called a risk level) and the color based on the judgment value are displayed. The map of (9B) may be referred to as a risk evaluation map. The numerical value of 30 points in the risk evaluation map means the risk of road collapse, and the smaller the numerical value, the higher the risk of collapse. It is preferable that a block having a high risk of depression is displayed in, for example, a deep red color, and is displayed in different colors such as light red, orange, yellow, light blue, and dark blue as the risk becomes lower. Further, instead of using color, the degree of danger may be displayed in monochrome shading. The risk evaluation map of (9B) is an example in which the risk is visually represented by the shades of monochrome, and the closer to black, the higher the risk. Note that the risk evaluation map may be displayed with only the judgment value, only the shade of color or monochrome, or any combination of the judgment value and the shade of color or monochrome.

  Note that in the flow of FIG. 4, prior to step S100, the first sinking that converts the road sinking factor into the numerical value shown in FIG. 7 (7B) and transmits the numerical value data to the determination formula factor data accepting unit 10. The factor value conversion step (step S50) may be performed. This step is a step executed by the first collapse factor numerical value conversion unit 30 with reference to the data table of the first storage unit 31. Further, prior to step S500, a second collapse factor numerical value conversion step (step S450) of converting the road collapse factor into a numerical value and transmitting the numerical value data to the risk factor data reception unit 18 may be performed. This step is a step executed by the second collapse factor numerical value conversion unit 40 with reference to the data table of the second storage unit 41.

  The above-mentioned evaluation of the road collapse risk is based on the five factors of the road collapse. Next, the evaluation when the factors causing the road collapse are narrowed down to two will be described with reference to FIG.

  FIG. 10 is a diagram for explaining the specific determination formula (10A), a diagram for explaining the threshold value (10B, 10C), and a risk evaluation in which unit blocks including each point are displayed on the map in color or shade. Maps (10D) are shown respectively.

  It is convenient to reduce the number of explanatory variables in the general judgment formula as much as possible so as to save the trouble of collecting data. However, the accuracy for judgment must be ensured. Here, an example will be described in which the number of explanatory variables is narrowed down to two, based on the above-described analysis example in which the number of explanatory variables that cause the road collapse is set to five. Even if the number of explanatory variables is different, the configuration in the device 1 and the flow of the evaluation method are the same as those in the above example.

  In this narrowing down, explanatory variables having a large degree of influence on the determination result (factors having a large range) are preferentially left and the other explanatory variables are discarded. Here, the range is a numerical width of the category quantity of the explanatory variable Xi, and indicates the degree of influence on Y. In the above-mentioned example, the range of three types of road collapse factors such as the presence of a cavity (X1), the number of years elapsed (X4) and the condition of groundwater (X3) is large in this order, and the others are smaller than these. Therefore, two factors are selected from the one with the larger range, and X1 and X4 are adopted as explanatory variables. When the calculation is performed by applying the quantification theory type II analysis to the two factors, the specific judgment formula (Formula 2) of (10A) is obtained.

  The table (10B) shows the distribution of the judgment value (sample score). The smaller the judgment value, the higher the risk of sinking, as in the above result. In this case, when the judgment value is -0.2 or less (NO.7), 100% road collapse occurs, and when the judgment value is 0 or less (NO.8), 6 of 14 misjudgments (no collapse). Will result in including. Therefore, the threshold value is -0.2. In this case, the discriminant predictive value is 94%. From this result, it is considered possible to perform a sufficiently high evaluation depending on the selection of the collapse factor even if the collapse factor is narrowed down to two.

  By substituting each data of X1 and X4 in the table shown in FIG. 8 into the specific determination formula of (10A), the collapse risk degree of each evaluation target point is determined, and subsequently, the determination value and the basis thereof are displayed on the map. As a result of displaying the shading, the risk evaluation map shown in (10D) is obtained. In this map as well, similar to the risk evaluation map of FIG. 9 (9B), the smaller the numerical value, the higher the risk of depression. The evaluation target point No. Numerical values of “boundary part” and “intersection part” other than 1 to 30 are the same as (9B) in notation. From (10D), the evaluation target point No. 13 and No. It is highly possible that a depression will occur at each of the 15 points, and the same results as in the risk evaluation map of (9B) are obtained. For other evaluation points, the difference in the judgment values (shading difference) is small because the factors for collapse are narrowed down. In addition, the evaluation target point No. 10 and No. In No. 16, the judgment value changes from minus to 0 or plus. These are considered to be the effects of using a specific judgment formula that narrows down the factor of depression. However, it can be read that it is a relatively high-risk place.

  Next, the evaluation will be described when there are 11 causes of road collapse.

  FIG. 11 and FIG. 12 respectively show tables in which various explanatory variables X6 to X11 at 100 points for making a judgment formula are digitized. FIG. 11 shows data at each of the 1st to 50th points, and FIG. 12 shows data at each of the 51st to 100th points. Note that Y and X1 to X5 are as shown in FIGS. FIG. 13 is a diagram for explaining the specific determination formula (13A), a diagram for explaining the threshold value (13B, 13C), and the magnitude of the range of the formula 3 of (13A) for 11 road collapse factors. FIG. 13D is shown respectively.

  If an explanatory variable having a high explanatory power with respect to the objective variable (Y) is selected as the explanatory variable of the judgment formula, the explanatory power of the formula will be high, but it is not sufficient if the number of items is simply large. Even if many factors with relatively low explanation power are taken in, the contribution to improve the determination accuracy will be small, and the difficulty of obtaining data will increase. Furthermore, it should be noted that if the number of variables is increased, the coefficient of the identified judgment formula may be a coefficient code that does not properly describe the actual influence content. Therefore, it is necessary to select an appropriate explanatory variable from the eleven sinking factors listed in FIG. 2 in consideration of the road characteristics, the sewer pipeline characteristics, etc. of the target city. Here, a calculation example will be described in the case where all the 11 depression factors listed as candidates are captured. The additional data shown in FIG. 11 and FIG. 12 are the pipeline part (X6), pipe type (X7), sewer pipe damage status (X8), traffic vibration (X9), summer temperature (X10), and active fault hazard (X11). ) Related data.

  In the tables of FIGS. 11 and 12, regarding the site (X6) of the conduit, "1" means the attachment pipe, "2" means the main pipe, and "3" means the others. Regarding the pipe type (X7), "1" means ceramic pipe, "2" means reinforced concrete, and "3" means others. Regarding the status of sewer pipe damage (X8), "1" means yes, and "2" means no. Regarding the traffic vibration (X9), "1" means more than the predetermined standard, and "2" means less than the predetermined standard. Regarding the summer temperature (X10), "1" means that the temperature is higher than the predetermined temperature, and "2" means that the temperature is not higher than the predetermined temperature. Regarding the active fault hazard (X11), "1" means yes, and "2" means no. These numerical values 1 to 3 are merely for classifying the explanatory variables X6 to X11 by type, and the absolute value of the numerical value itself has no meaning. Therefore, the alphabets a, b, and c may be used instead of the numerical values 1, 2, and 3. In addition, the numbers 1 to 3 in the tables of FIGS. 11 and 12 are not directly substituted into the general determination formula. This point is as described above for Y and X1 to X5.

  The data received by the judgment formula factor data receiving unit 10 includes the existence of underground cavities, the type of backfilled soil, the status of groundwater, the number of years of pipeline construction, the soil cover thickness on pipelines, the location of pipelines, Data on species, pipeline damage, traffic vibration, summer temperature and active fault hazard. As described with reference to FIG. 7 (7B), such data (determination factor data) is data quantified for each factor, and is “1” when applicable and is not applicable. Indicates data that is “0”.

  After the numerical data relating to 11 types of factors are received by the determination formula factor data reception unit 10, the respective configuration units 11 to 23 in the device 1 perform the respective processes in the flow of FIG. 4 as described above. As a result of the processing of the specific determination formula determination unit 14, Formula 3 shown in (13A) is determined. In the distribution of the judgment values (sample scores) of (13B, 13C), the smaller the judgment value is, the higher the risk of road collapse is, as described above. Also in this example, the threshold value is -0.2. Using 11 depression factors, the discriminant predictive value was about 97%. It can be seen that the hit rate is higher than that when there are two or five collapse factors (94% in each case). The category quantity of some explanatory variables, such as the number of years elapsed (X4), shows the result that the actual content of the impact is not properly described (for example, in X4, even a new pipe with a low degree of deterioration has a negative category. Quantity has occurred).

  According to the diagram (13D), the presence of cavities (X1), the number of years elapsed (X4), and the status of groundwater (X3) are at the top. From this, it is considered preferable that these three types of factors are taken into consideration when evaluating the risk of road collapse.

  FIG. 14 shows a table in which various explanatory variables X6 to X11 at 30 evaluation target points where the risk is evaluated are digitized. The explanatory variables X1 to X5 are as shown in FIG. FIG. 15 shows a risk evaluation map in which unit blocks including respective points are displayed on the map in different colors or in different shades.

  When the processing after step S500 (as a modification, step S450) in the flow of FIG. 4 is performed, the map of FIG. 15 is displayed. It can be seen that a similar map can be obtained, although the risk and the color or shade of each block in the map do not match those of FIG. 9 (9B) or FIG. 10 (10D).

The road collapse risk evaluation method according to this embodiment as described above is, in detail, a method of evaluating a road collapse risk using the device 1 for evaluating a road collapse risk,
This is an expression for determining the risk of road collapse and is the data necessary for determining the specific judgment equation as a functional expression of the factors of road collapse and the risk, and is sampled for creating the specific judgment equation. A judgment formula factor data receiving step for receiving judgment formula factor data quantifying the factors of road collapse at a plurality of sampling points,
The general judgment formula shown in the formula (A) which is the basis of the specific judgment formula {Y is an external criterion indicating the presence or absence of a road depression, X1 to Xn: Xi are explanatory variables that cause the road depression, and A1 to An Are coefficients for multiplying X1 to Xn, n is an integer of 1 or more and is the number of i-th explanatory variables Xi, xij is a j-th categorical variable of Xi, aij is a category coefficient of xij, and mi is The number of categories of Xi is shown respectively. } The first substitution step (S200) of substituting the judgment formula factor data constituted by the categorical variables into the general judgment formula read from the general judgment formula storage means for storing
By substituting the factor data for the judgment formula into the general judgment formula, the calculation is performed by the quantification theory II class analysis so as to maximize the inter-group variation relative to the total variation, and the category coefficient (aij ) Determining a specific determination formula (S300),
A risk level factor data reception step (S500) of receiving risk level factor data quantifying the factors of road collapse at a plurality of evaluation target points to be evaluated,
A second substitution step (S600) of substituting risk factor data constituted by categorical variables at the evaluation target point into the specific determination formula read from the specific determination formula storing means for storing the specific determination formula;
A determination value determination step (S700) of performing a calculation based on the processing of the second substitution step and determining a determination value that is a numerical value of the risk of depression at each evaluation target point;
including.

<3. Computer program for road collapse risk assessment>

  The road collapse risk evaluation computer program is a computer program that is installed in a computer and causes the computer to function as the road collapse risk evaluation device 1 for evaluating the risk of road collapse. The computer program for road collapse risk evaluation uses a computer typified by the device 1, a judgment formula factor data reception unit 10, a first substitution unit 13, a specific judgment formula determination unit 14, a risk factor data reception unit 18, The second assigning unit 19 and the determination value determining unit 20 are caused to function. Further, the road collapse risk evaluation computer program uses a computer represented by the device 1 as a general judgment formula reading unit 12, a threshold value determining unit 15, a specific judgment formula reading unit 17, a map information reading unit 22, and a first collapse factor. It is preferable to function as at least one of the numerical conversion unit 30 and the second collapse factor numerical conversion unit 40.

  A computer program represented by the device 1 is used as a computer program for evaluating a road collapse risk, and a determination formula factor data reception step (S100), a first substitution step (S200), a specific determination formula determination step (S300), The risk factor data reception step (S500), the second substitution step (S600), and the determination value determination step (S700) are executed. Further, the road collapse risk evaluation computer program causes a computer represented by the device 1 to perform a first collapse factor numerical value conversion step (S50), a general determination expression reading step (here, S150), and a threshold value determination step. (S400), second depression factor numerical value conversion step (S450), specific determination formula reading step (herein S550), map information reading step (herein S750) and map display step (S800). Preferably, one step is performed.

  In addition, the road collapse risk evaluation computer program is installed in a memory in the device 1 from another computer (server) physically separated from the device 1 and then executed, or an information recording medium (for example, It is stored in a compact disc, a portable flash memory, an FD, an MD, etc.) and is installed in the memory in the device 1 by inserting or connecting the information recording medium into the device 1 and then executed or read as it is without being installed. It may be issued and executed. The information recording medium means a non-transitory tangible recording medium. The computer program for road collapse risk assessment may be in the form of one program or in the form of two or more programs. Similarly, the information recording medium storing the computer program for evaluating the road collapse risk may be one or two or more.

More specifically, the computer program for road collapse risk evaluation according to this embodiment is installed in a computer and causes the computer to function as a road collapse risk evaluation device 1 for evaluating the risk of road collapse. A program,
The computer
This is an expression for determining the risk of road collapse and is the data necessary for determining the specific judgment equation as a functional expression of the factors of road collapse and the risk, and is sampled for creating the specific judgment equation. Determination formula factor data receiving means (corresponding to the determination formula factor data receiving unit 10) that receives determination formula factor data quantifying the factors of road collapse at a plurality of sampling points.
The general judgment formula shown in the formula (A) which is the basis of the specific judgment formula {Y is an external criterion indicating the presence or absence of a road depression, X1 to Xn: Xi are explanatory variables that cause the road depression, and A1 to An Are coefficients for multiplying X1 to Xn, n is an integer of 1 or more and is the number of i-th explanatory variables Xi, xij is a j-th categorical variable of Xi, aij is a category coefficient of xij, and mi is The number of categories of Xi is shown respectively. } The first substituting means (corresponding to the first substituting section 13) for substituting the factor data for the decision expression constituted by the categorical variables into the general decision expression read from the general decision expression storing means
By substituting the factor data for the judgment formula into the general judgment formula, the calculation is performed by the quantification theory II class analysis so as to maximize the inter-group variation relative to the total variation, and the category coefficient (aij ) A specific determination formula determining means for determining a specific determination formula (corresponding to the specific determination formula determining unit 14),
Risk factor factor data receiving means (corresponding to the risk factor factor data accepting unit 18) that accepts risk factor factor data quantifying factors of road collapse at a plurality of evaluation target points to be evaluated,
Second substitution means (in the second substitution unit 19) for substituting risk factor data constituted by categorical variables at the evaluation target point into the specific determination expression read from the specific determination expression storage means for storing the specific determination expression. (Corresponding to the judgment value deciding unit 20) which performs a calculation based on the processing of the second substituting means and decides a judgment value which is a numerical value of the risk of depression at each evaluation target point. .

  INDUSTRIAL APPLICABILITY The present invention can be applied to technologies and industries that predict the risk of road collapse.

Claims (10)

A road collapse risk evaluation device for evaluating the risk of road collapse,
It is an expression for determining the risk of road collapse and is data necessary for determining a specific judgment formula as a function formula of the factor of road collapse and the risk, and for creating the specific judgment formula Judgment formula factor data receiving means for receiving judgment formula factor data quantifying the factors of road collapse at a plurality of sampled points,
The general judgment formula shown in the following formula (A), which is the basis of the specific judgment formula, {Y is an external criterion indicating the presence or absence of a road depression, and X1 to Xn: Xi are explanatory variables that cause road depression. A1 to An are coefficients by which X1 to Xn are respectively multiplied, n is the number of the i-th explanatory variable Xi and is an integer of 1 or more, xij is the j-th categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi represent the number of categories of Xi, respectively. } General determination formula storage means for storing
First substituting means for substituting the judgment formula factor data configured by the categorical variables into the general judgment formula read from the general judgment formula storage means,
By substituting the factor data for the judgment formula into the general judgment formula, a calculation is executed by the quantification theory type II analysis so as to maximize the intergroup variation relative to the total variation, and Specific determination formula determining means for determining the specific determination formula specifying the coefficient (aij);
A specific determination formula storage means for storing the specific determination formula,
Risk factor data receiving means for receiving risk factor data quantifying the factors of road collapse at a plurality of evaluation points to be evaluated,
A second substituting means for substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination expression read from the specific determination expression storage means,
Judgment value determination means for performing a calculation based on the processing of the second substitution means, and for determining a judgment value that is a numerical value of the risk of depression at each evaluation target point,
Road collapse risk assessment device including.
  The road collapse risk degree according to claim 1, further comprising threshold determining means that classifies the determination values calculated by the specific determination expression into a predetermined range and determines a threshold for occurrence of a depression based on the presence or absence of an actual depression. Evaluation device. Map information storage means for storing information of a map including each evaluation target point,
On the map read from the map information storage means, map display means for displaying by color or by shade based on the determination value indicating the risk of depression at each evaluation target point,
The road collapse risk assessment apparatus according to claim 1, further comprising: The cause of the road collapse is
Presence of underground cavities, type of backfill soil, condition of groundwater, age of pipeline installation, overburden thickness of pipeline, pipeline site, pipeline type, pipeline damage status, traffic vibration, summer temperature and activity The road collapse risk assessment device according to any one of claims 1 to 3, wherein the device has two or more fault hazards. A method for evaluating the risk of road collapse using a device for evaluating the risk of road collapse,
It is an expression for determining the risk of road collapse and is data necessary for determining a specific judgment formula as a function formula of the factor of road collapse and the risk, and for creating the specific judgment formula A judgment formula factor data receiving step for receiving judgment formula factor data quantifying the factors of road collapse at a plurality of sampled points;
The general judgment formula shown in the following formula (A), which is the basis of the specific judgment formula, {Y is an external criterion indicating the presence or absence of a road depression, and X1 to Xn: Xi are explanatory variables that cause road depression. A1 to An are coefficients by which X1 to Xn are respectively multiplied, n is the number of the i-th explanatory variable Xi and is an integer of 1 or more, xij is the j-th categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi represent the number of categories of Xi, respectively. } The first substitution step of substituting the judgment formula factor data configured by the categorical variables into the general judgment formula read from the general judgment formula storing means for storing
By substituting the factor data for the judgment formula into the general judgment formula, a calculation is executed by the quantification theory type II analysis so as to maximize the intergroup variation relative to the total variation, and A specific determination formula determining step of determining the specific determination formula that specifies a coefficient (aij);
A risk factor data reception step for receiving risk factor data quantifying the factors of road collapse at a plurality of evaluation points to be evaluated,
A second substitution step of substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination expression read from the specific determination expression storage unit that stores the specific determination expression,
A determination value determination step of performing a calculation based on the processing of the second substitution step, and determining a determination value that is a numerical value of the risk of depression at each evaluation target point,
Road collapse risk assessment method including.
  The road collapse risk degree according to claim 5, further comprising a threshold determination step of classifying the determination value calculated by the specific determination expression into a predetermined range and determining a threshold for occurrence of a depression based on the presence or absence of an actual depression. Evaluation methods.   On the map read from the map information storage unit that stores the information of the map including each of the evaluation target points, based on the determination value indicating the risk of depression at each evaluation target point The road collapse risk assessment method according to claim 5, further comprising a map display step of displaying. A computer program that is installed in a computer and causes the computer to function as a road collapse risk evaluation apparatus for evaluating the risk of road collapse.
The computer
It is an expression for determining the risk of road collapse and is data necessary for determining a specific judgment formula as a function formula of the factor of road collapse and the risk, and for creating the specific judgment formula Judgment factor data reception means for receiving judgment factor data quantifying the factors of road collapse at a plurality of sampled points,
The general judgment formula shown in the following formula (A), which is the basis of the specific judgment formula, {Y is an external criterion indicating the presence or absence of a road depression, and X1 to Xn: Xi are explanatory variables that cause road depression. A1 to An are coefficients by which X1 to Xn are respectively multiplied, n is the number of the i-th explanatory variable Xi and is an integer of 1 or more, xij is the j-th categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi represent the number of categories of Xi, respectively. } The first substituting means for substituting the judgment formula factor data constituted by the categorical variables into the general judgment formula read from the general judgment formula storage means for storing
By substituting the factor data for the judgment formula into the general judgment formula, a calculation is executed by the quantification theory type II analysis so as to maximize the intergroup variation relative to the total variation, and Specific determination formula determining means for determining the specific determination formula that specifies the coefficient (aij),
Risk factor factor data receiving means for receiving risk factor factor data quantifying the factors of road collapse at a plurality of evaluation target points,
Second substitution means for substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination expression read from the specific determination expression storage means for storing the specific determination expression, and A computer program for evaluating a road collapse risk, which performs a calculation based on the process of the second substituting device and functions as a judgment value determining device that determines a judgment value that is a numerical value of the risk of collapse at each evaluation target point.
The computer,
9. The road collapse risk degree according to claim 8, wherein the judgment value calculated by the specific judgment formula is classified into a predetermined range, and is further caused to function as a threshold value determining unit that determines a threshold value for occurrence of a depression based on the presence or absence of an actual depression. Evaluation computer program. The computer,
On the map read from the map information storage unit that stores the information of the map including each of the evaluation target points, based on the determination value indicating the risk of depression at each evaluation target point The road collapse risk degree computer program according to claim 8 or 9, further functioning as map display means for displaying.