WO2018147086A1

WO2018147086A1 - Road-subsidence risk-level evaluation device, road-subsidence risk-level evaluation method, and computer program for road-subsidence risk-level evaluation

Info

Publication number: WO2018147086A1
Application number: PCT/JP2018/002316
Authority: WO
Inventors: 康生清水; 恭悟野村
Original assignee: 株式会社日水コン
Priority date: 2017-02-08
Filing date: 2018-01-25
Publication date: 2018-08-16
Also published as: JPWO2018147086A1; JP6682021B2; CN110234811A; CN110234811B

Abstract

[Problem] To objectively and quantitatively evaluate the level of risk of the occurrence of road subsidence. [Solution] The present invention relates to a road-subsidence risk-level evaluation device 1 including: a classification-formula-factor-data accepting means 10; a general-classification-formula storage means 11; a first substitution means 13; a specific-classification-formula determining means 14; a specific-classification-formula storage means 16; a risk-level-factor-data accepting means 18; a second substitution means 19; and a classification-value determining means 20. The present invention also relates to a method for evaluating the level of risk of road subsidence by using the device 1, as well as a computer program for road-subsidence risk-level evaluation, said program causing a computer to function as the road-subsidence risk-level evaluation device 1.

Description

Road collapse risk evaluation device, road collapse risk evaluation method, and computer program for road collapse risk evaluation

Cross reference

This application claims priority based on Japanese Patent Application No. 2017-021003 filed in Japan on February 8, 2017, the contents of which are incorporated herein by reference. Moreover, the content described in the patent quoted in this application, a patent application, and literature is used for this specification.

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 computer program for road collapse risk evaluation that is installed in a computer and evaluates the risk of road collapse.

In Japan, the number of local government staff has continued to decrease due to the austerity of local government finances in addition to population decline and aging. Regarding the decrease in staff, it is necessary to prepare for the future in the maintenance department of infrastructure such as roads and water and sewage systems. In Japan, road depressions occur at an average of more than 4000 cases every year, regardless of size. The impact of road depression on the entire economy, including lifelines such as traffic, electricity and gas, is greater in urban areas than in rural areas. In order to prevent road collapse and protect the road, it is necessary to obtain road ground information (especially the presence of underground cavities) at an early stage.

The cause of the generation of cavities is caused by sewer pipes, for example, sewer pipe misalignment and poor joints account for about 40% of the total. Such defective sewer pipes are caused not only by external forces represented by increased traffic and earthquake motion, but also by aging pipes and poor construction. Currently, in the ordinance-designated city, various infrastructure information such as water and sewage systems that are centrally managed by the road management system of the general incorporated foundation and road management center are provided mutually. However, even such a system does not include all information indicating the state of the infrastructure under the road. In addition, there are many active faults in the city and its surrounding areas among Japanese government-designated cities. Such an active fault is considered to be a hazard that causes dislocation of roads. The infrastructure information described above does not necessarily include the presence of the hazard.

Here, it is known that the incidence of road cave-in caused by sewer pipes is higher in Korea than in Japan, when looking at the road cave-in and out of the country. As one of the measures, Seoul City signed an administrative agreement in 2015 with Tokyo regarding technical cooperation for road collapse. In response, Seoul City conducted a survey using ground-penetrating radar (GPR) and decided to conduct major main road exploration in the next three years. Currently, Seoul City is developing a program for investigation and analysis of cavities. In the United States and Canada, damage to sewer pipes is detected by a method called Smoke Testing. Specifically, after one manhole is determined, odorless and nontoxic smoke is put into the sewer from that manhole, and the smoke is ejected from another manhole through the manhole and the sewer pipe. . At this time, it is determined that there is a damaged sewer pipe in the sewer pipe connected to the manhole where no smoke was emitted.

In Japan, when smoke testing is applied, it is difficult to observe on the road under dense pipe laying conditions in densely populated urban areas such as residences, and the density of pipes under the road itself is high. It is difficult to identify a leaking place. For this reason, in Japan, applying GPR matches the road conditions. In Japan, the Road Law has been revised in recent years, and the Ministry of Land, Infrastructure, Transport and Tourism's Road Bureau has indicated the outline of the implementation of road inspections, and in that it will conduct a cavity survey under the radar. According to the Ministry of Land, Infrastructure, Transport and Tourism manual, the cavity exploration vehicle equipped with radar has the exploration ability to detect cavities with an exploration width of about 2.0 m, an exploration depth of about 1.5 m, a length of 50 cm x width 50 cm x thickness 10 cm or more. Have. By using such a vehicle, it is possible to grasp the existence and size of the 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 conventional cavity exploration method described above is merely a means for grasping the existence of the cavity, and it is difficult to objectively and quantitatively evaluate the risk of road collapse. In order to prevent road depression and take appropriate measures, it is strongly required to objectively and quantitatively evaluate the risk of occurrence of depression.

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

(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,
Formula for determining the risk of road collapse, and data necessary for determining a specific determination formula as a function expression of the factor of road collapse and the risk, for creating the specific determination formula Determination factor factor data receiving means for receiving determination factor factor data obtained by quantifying the factors of road depression at a plurality of sampled sampling points;
General judgment formula shown in the following formula (A) based on the specific judgment formula {Y is an external standard indicating whether or not a road is depressed, X1 to Xn: Xi are explanatory variables that cause a road depression, A1 to An are coefficients multiplied by X1 to Xn, n is the number of the i-th explanatory variable Xi, an integer of 1 or more, xij is the jth categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi respectively indicate the number of categories of Xi. }, General judgment expression storage means for storing
First substituting means for substituting the judgment formula factor data constituted by the categorical variable into the general judgment formula read from the general judgment formula storage unit;
Substituting the judgment factor factor data into the general judgment formula, and performing a calculation so as to make the variation between the two groups relatively large with respect to the total variation by quantification theory type II analysis, Specific determination formula determining means for determining the specific determination formula specifying the coefficient (aij);
Specific determination formula storage means for storing the specific determination formula;
A risk factor data receiving means for receiving risk factor data quantifying the factors of road collapse at a plurality of evaluation target points to be evaluated;
Second substitution means for substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific judgment formula read from the specific judgment formula storage means;
A determination value determining means for performing a calculation based on the processing of the second substitution means, and determining a determination value obtained by quantifying the risk of depression at each evaluation target point;
including.

(2) The road collapse risk evaluation apparatus according to another embodiment preferably classifies the determination value calculated by the specific determination formula into a predetermined range, and a threshold for occurrence of the collapse based on the presence or absence of actual depression Threshold value determining means for determining

(3) The road collapse risk evaluation device according to another embodiment is preferably a map information storage unit that stores information of a map including each evaluation target point;
On the map read from the map information storage means, a map display means for performing display by color or shade based on the determination value indicating the risk of depression at each evaluation target point;
Further included.

(4) In the road collapse risk evaluation apparatus according to another embodiment, preferably, the factors of the road collapse are the presence of an underground cavity, the type of backfill soil, the state of groundwater, the number of years of pipe laying, the pipe It is two or more of the thickness of the earth covering on the road, the part of the pipe, the pipe type, the pipe breakage, the traffic vibration, the summer temperature and the active fault hazard.

(5) The road collapse risk evaluation method according to an embodiment is a method of evaluating the risk of road collapse using an apparatus for evaluating the risk of road collapse,
Formula for determining the risk of road collapse, and data necessary for determining a specific determination formula as a function expression of the factor of road collapse and the risk, for creating the specific determination formula A determination formula factor data reception step for receiving determination formula factor data quantifying the factors of road depression at a plurality of sampled sampling points;
General judgment formula shown in the following formula (A) based on the specific judgment formula {Y is an external standard indicating whether or not a road is depressed, X1 to Xn: Xi are explanatory variables that cause a road depression, A1 to An are coefficients multiplied by X1 to Xn, n is the number of the i-th explanatory variable Xi, an integer of 1 or more, xij is the jth categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi respectively indicate the number of categories of Xi. }, The first substituting step of substituting the determination formula factor data constituted by the categorical variable into the general determination formula read from the general determination formula storage means for storing
Substituting the judgment factor factor data into the general judgment formula, and performing a calculation so as to make the variation between the two groups relatively large with respect to the total variation by quantification theory type II analysis, A specific determination formula determining step for determining the specific determination formula specifying the coefficient (aij);
A risk factor data reception step for accepting risk factor data that quantifies the factors of road collapse at a plurality of evaluation target points to be evaluated;
A second substituting step of substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination formula read from the specific determination formula storage means for storing the specific determination formula;
A determination value determination step for performing a calculation based on the processing of the second substitution step and determining a determination value obtained by quantifying the risk of depression at each evaluation target point;
including.

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

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

(8) A computer program for evaluating the risk of road collapse according to an embodiment is a computer program that is installed in a computer and that causes the computer to function as a road collapse risk evaluation apparatus for evaluating the risk of road collapse. There,
The computer
Formula for determining the risk of road collapse, and data necessary for determining a specific determination formula as a function expression of the factor of road collapse and the risk, for creating the specific determination formula Determination factor factor data receiving means for receiving determination factor factor data quantifying the factors of road depression at a plurality of sampled sampling points;
General judgment formula shown in the following formula (A) based on the specific judgment formula {Y is an external standard indicating whether or not a road is depressed, X1 to Xn: Xi are explanatory variables that cause a road depression, A1 to An are coefficients multiplied by X1 to Xn, n is the number of the i-th explanatory variable Xi, an integer of 1 or more, xij is the jth categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi respectively indicate the number of categories of Xi. }, The first substituting means for substituting the judgment formula factor data composed of the categorical variables into the general judgment formula read from the general judgment formula storage means.
Substituting the judgment factor factor data into the general judgment formula, and performing a calculation so as to make the variation between the two groups relatively large with respect to the total variation by quantification theory type II analysis, Specific determination formula determining means for determining the specific determination formula specifying the coefficient (aij);
Risk factor data receiving means for receiving risk factor data quantifying the factors of road collapse at a plurality of evaluation target points to be evaluated;
A second substituting unit for substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination formula read from the specific determination formula storage unit that stores the specific determination formula; and An operation is performed based on the processing of the second substitution means, and functions as a determination value determination means for determining a determination value obtained by quantifying the risk of depression at each evaluation target point.

(9) The computer program for risk assessment of road collapse according to another embodiment preferably includes the computer,
The determination value calculated by the specific determination formula is classified into a predetermined range, and further functions as threshold determination means for determining a depression occurrence threshold based on the actual presence or absence of depression.

(10) The computer program for risk assessment of road collapse according to another embodiment preferably includes the computer,
On the map read out from the map information storage means for storing the information of the map including each evaluation target point, the color or gray level is classified according to the judgment value indicating the risk of depression at each evaluation target point. It further functions as a map display means for performing display.

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

FIG. 1 is a conceptual diagram for determining a road collapse risk according to an 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 the various data used for preparation of the road depression sign judgment formula is shown. An example of the various data used for preparation of the road depression precursor determination formula different from the example shown to FIG. 1C is shown. FIG. 2 shows an example of various data used to create a road collapse predictor determination formula different from those shown in FIGS. 1C and 1D. FIG. 3 shows the configuration of each function of the road collapse risk evaluation apparatus according to the embodiment of the present invention. FIG. 4 shows a flow of main steps of the road collapse risk evaluation method according to the embodiment of the present invention. FIG. 5 is a table in which the external reference Y and the various explanatory variables X1 to X5 at the 100 points for creating the judgment formula are quantified, and shows data at the 1st to 50th points. FIG. 6 is a table in which the external reference Y and the various explanatory variables X1 to X5 at the 100 points for creating the judgment formula are quantified, and shows data at the 51st to 100th points. FIG. 7 is a diagram (7A) for explaining the general judgment formula, a diagram (7B) for explaining the digitized factor data, a diagram (7C) for explaining the specific judgment formula, and the threshold value. Figures (7D, 7E) are respectively shown. FIG. 8 shows a table in which various explanatory variables X1 to X5 are digitized at 30 evaluation target points to be evaluated for risk. FIG. 9 shows a map (9A) of an area including an evaluation target point and a risk evaluation map (9B) in which unit blocks including each point are displayed on the map by color classification or light / dark classification. FIG. 10 is a diagram (10A) for explaining a specific determination formula, diagrams (10B, 10C) for explaining a threshold value, and a risk evaluation in which a unit block including each point is displayed on the map by color coding or gray coding. Each map (10D) is shown. FIG. 11 is a table in which various explanatory variables X6 to X11 at 100 points for determination formula creation are quantified, and shows data of the 1st to 50th points. FIG. 12 is a table in which various explanatory variables X6 to X11 at 100 points for determination formula creation are quantified, and shows data of the 51st to 100th points. FIG. 13 compares the size of the range of Expression 3 in FIG. 13A for explaining the specific determination formula, the figures 13B and 13C for explaining the threshold values, and 11 road crush factors (13A). Each figure (13D) is shown. FIG. 14 shows a table in which various explanatory variables X6 to X11 at 30 evaluation target points that are targets for risk evaluation are digitized. FIG. 15 shows a risk evaluation map in which unit blocks including each point are displayed on the map by color classification or light / dark classification.

1: Road collapse risk evaluation device (also simply called “device”)
10: Judgment formula factor data receiving unit (judgment formula factor data receiving means)
11: General judgment formula storage unit (general judgment formula storage means)
13: 1st substitution part (1st substitution means)
14: Specific determination formula determination unit (specific determination formula determination means)
15: Threshold determination unit (threshold 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: Determination value determination unit (determination 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. The embodiments described below do not limit the invention according to the claims, and all the elements and combinations described in the embodiments are essential for the solution of the invention. Is not limited.

<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 the first embodiment of the present invention will be described.

<1. Concept of Road Crash Risk Assessment>
FIG. 1 is a conceptual diagram for determining a road collapse risk according to an embodiment of the present invention.

(1) Peril data and various hazard data In this embodiment, in order to obtain the road collapse risk, various data represented by, for example, peril data, hazard data, field survey hazard data, and base MH management hazard data are required. It is. All of these data cause road collapse. Peril data means data that causes a danger. The peril data in this embodiment is collected by the road manager and the sewer manager in cooperation with preventive maintenance against road depression. Specific examples of the peril data include depression history, road improvement history, and cavity investigation history (follow-up observation, cause investigation, repair, etc.).

Hazard data refers to the danger situation (risk factor) when road depression is considered a dangerous event. Examples of hazard data include sewer pipe attributes such as pipe type, caliber, aging value, deterioration damage, construction history, and the like. The same applies to water pipes. These data are available from the ledger. There are other infrastructures such as gas pipes for hazards that cause road depression, and the various data are required. Furthermore, the backfill material of various buried objects, the groundwater level status, traffic volume, and road surface temperature can also be included in the hazard data.

”Field Survey Hazard Data (also called Field Survey Road Survey Hazard Data) is survey data related to roads and refers to the danger 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. Traffic regulation is not required for data collection. When the survey vehicle is used, it is possible to measure the 3D terrain model using the laser scanner and camera. From the MMS information obtained in this way, the depression situation on the road can be known. Furthermore, the investigation vehicle can be equipped with GPR (Ground Penetrating Radar). GPR extracts abnormal signals (including cavities and looseness signals 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 among them. . The abnormality signal is obtained by surveying the survey vehicle at a traveling speed of about 40 km / h. Traffic regulation is not required for data collection. As exploration accuracy, a exploration depth of about 1.5 m, a cavity having a length of 50 cm × width 50 cm × thickness 10 cm or more can be explored. If a more accurate method is adopted, it is also possible to estimate the state of cavities in the soil layer up to about 3.0 m from data up to an exploration depth of 1.5 m.

The site MH management hazard data is data related to the real-time environment inside and outside the pipeline, and refers to the risk of road collapse. For example, sewer pipes are likely to deteriorate due to corrosion of concrete or the like caused by hydrogen sulfide. For this reason, inspections and surveys such as hydrogen sulfide concentration are extremely important. In addition, sewerage pipes that have deteriorated in the pipes are easily damaged by road loads. From such points, real-time information such as hydrogen sulfide in the sewer pipe can be hazard data for road depression. In addition, water temperature, water quality, and water level in sewer pipes can also be this type of hazard data.

(2) Coordinate value conversion of data The above-mentioned peril data and various hazard data are obtained from a ledger or by survey and used for coordinate value conversion. An exploration vehicle equipped with MMS and GPR can always record a traveling position by GPS (Global Positioning System). For this reason, the coordinate value of the underground cavity position can be obtained. In addition, roads and various underground objects can be coordinated in the ledger or after being extracted from the ledger. As described above, by converting the above-described peril data and various hazard data into coordinate values, it is possible to efficiently manage the risk of road collapse by combining the data survey and the field survey. In addition, it is possible to automate the calculation of the depression risk based on the road depression sign determination formula (also simply referred to as a sign determination formula). Such coordinate value conversion will be described in detail later.

(3) Creation of road depression sign judgment formula Road depression sign judgment formula creation is a road depression by inputting the above coordinated information into the general formula of the judgment formula and applying quantification type II It means to determine the sign judgment formula. In this work, the target variable to be estimated is the peril data (F), the level of the road depression is expressed in about two to three levels, and various hazard data are considered as information related to the hazard data (Xi). That is, this step outputs an assumed depression state level from qualitatively given Xi data.

(4) Quantification of road collapse risk by unit block The quantification is to divide the area to be investigated into a plurality of blocks and substitute the coordinate value of the point in each block into the judgment formula obtained earlier, The risk level is quantified in units of each block. As a result, when the road is divided into unit blocks in the survey target area having the road, it is possible to quantitatively and objectively grasp how much the block has a risk of road collapse.

As described above, the creation of the road collapse predictive judgment formula described above is an operation for specifying the judgment formula using the peril data and hazard data at a plurality of points for creating the judgment formula. On the other hand, the numerical value of the road collapse risk by unit block is based on the peril data and various hazard data of the target point for which the collapse risk is calculated (which may partially overlap with the point for creating the above judgment formula). This is an operation for substituting into the above-identified judgment formula and expressing the depression risk level as a numerical value.

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

1A-1D and the information shown in FIG. 2 are broadly divided into external criteria and explanatory variables. The external standard is, in the most typical example, the presence or absence of a road depression, which means a result of whether or not a phenomenon of depression has occurred. Further, the external standard may include not only the presence or absence of a road depression, but also the status of each stage at the risk of road depression. In FIG. 1A, when there are only two types of external criteria (Y), that is, whether or not there is a road depression, k = 2, and Y is B1 and B2. Here, B1 means no road depression, and B2 means a road depression. On the other hand, if the external standard (Y) includes another situation of “there is a sign of depression” in addition to the presence or absence of road depression, k = 3, and Y is 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 the road depression. The explanatory variable (X) is a factor that is considered to be the cause of the road depression, and preferably includes at least two (X1 and X2) when Y = f (Xi). However, there is no restriction as long as the number of X is 1 or more. X can be represented by X1, X2, X3,..., Xn (n is the number of the 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 criterion (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. A coefficient to be multiplied by X1 is represented by A1, a coefficient to be multiplied by X2 is represented by A2, and a coefficient to be multiplied by Xn 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 represents the jth categorical variable of Xi, aij represents the category coefficient of xij, and mi represents 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 constituting the explanatory variable Xi, but can take various numbers 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. Thus, the number of categorical variables can be variously changed according to the explanatory variables. Each categorical variable xij constituting the explanatory variable Xi is all zero, or only one is 1 and the others are zero. This will be described in detail later. Various changes can be made to the criteria for dividing a categorical variable into a plurality of categories and the number of categorical variables. In this embodiment, three types of explanatory variables of FIG. 1C, FIG. 1D, and FIG. 2 are illustrated.

Here, as an example of the cause of the road collapse (that is, an example of explanatory variables), the presence of a cavity (X1), backfill soil (X2), groundwater status (X3), elapsed years (X4) ), Cover thickness (X5), pipe part (X6), pipe type (X7), sewer damage (X8), traffic vibration (X9), summer Let us list the temperature (denoted as X10) and the active fault hazard (denoted as X11). Subsequent X1 to X11 mean the above explanatory variables.

As shown in FIG. 2, the explanatory variable (X1) of the existence of a cavity has a cavity at a position less than 1/2 of the earth covering, and at a position of 1/2 or more of the earth covering when there is no cavity. There are three types of cases. The position less than 1/2 of the earth covering is a position below 1/2 of the thickness of the earth and sand covering the pipe such as the sewage pipe. The position of 1/2 or more of the earth covering is a position that is 1/2 or less than the thickness of the earth and sand covering a pipe such as a sewer pipe. The explanatory variable (X2) of backfilling soil is divided into two types: a case where the soil type is gravel soil and a case where the soil type is sandy soil. The explanatory variable (X3) of the state of groundwater is divided into three types: when there is no groundwater, when there is groundwater, and when there is groundwater and there is fluctuation. When there is groundwater, it is divided into cases where there is no change in groundwater and cases where there is no change. The risk of depression is higher when there is groundwater fluctuation. Therefore, the case where there is groundwater is divided into two cases from the viewpoint of the presence or absence of fluctuations in groundwater. The explanatory variable (X4) of elapsed years (years since pipe installation) is divided into three types: less than 20 years, more than 20 years and less than 40 years, and more than 40 years. The explanatory variable (X5) of the earth covering thickness is divided into three types of 2 m or more, 1 m or more and less than 2 m, and less than 1 m. The explanatory variable (X6) of the part of the pipe line is divided into the case of the attachment pipe, the case of the main pipe, and the other three types. The explanatory variable (X7), which is a pipe type, is divided into three types: ceramic pipe, reinforced concrete, and others. The explanatory variable (X8) of the sewer pipe breakage state is divided into two types, that is, the case where it is not broken and the case where it is broken. The explanatory variable (X9) of traffic vibration is divided into two types, a case where it is large and a case where it is small relative to a predetermined standard. The explanatory variable (X10) of summer temperature is classified into two types: a case where the temperature is higher than a predetermined temperature (for example, a temperature within the range of 25 to 35 ° C. monthly average temperature) and a case where the temperature is lower. The explanatory variable (X11), which is an active fault hazard, is classified into two types when there is an active fault and when there is 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 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 only two or four or more may be used. The same applies to other explanatory variables. However, the explanatory variable for creating the judgment formula and the explanatory variable for risk assessment need to have the same type and criteria / number of categorical variables.

<2. Road Crash Risk Assessment Device and Road Crash Risk Assessment Method>
FIG. 3 shows the configuration of each function of the road collapse risk evaluation apparatus according to the embodiment of the present invention.

The road collapse risk evaluation apparatus 1 according to this embodiment is an apparatus for evaluating the risk of road collapse. A road collapse risk evaluation apparatus (hereinafter also simply referred to as “apparatus”) 1 includes a determination formula factor data receiving unit 10, a general determination formula storage unit 11, a general determination formula reading unit 12, a first substitution unit 13, and a specific determination. Formula determination unit 14, threshold determination unit 15, specific determination formula storage unit 16, specific determination formula read 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 are provided.

Determination formula factor data receiving unit 10, general determination formula reading unit 12, first substitution unit 13, specific determination formula determining unit 14, threshold determining unit 15, specific determination formula reading unit 17, risk factor data receiving unit 18, The second substitution unit 19, the determination value determination unit 20, the map information reading unit 22, and the map display unit 23 are configured so that a central processing unit (CPU) mounted on an electronic circuit board in a computer can execute a computer program (for road collapse risk evaluation). Each process is performed by executing a computer program. The general determination formula storage unit 11, the specific determination 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 are not limited to a RAM or a hard disk, and may be a ROM from which data is unilaterally read. Further, the judgment factor factor data receiving unit 10 and / or the risk factor data receiving unit 18 receives each data from various devices (server, keyboard, pointing device, touch panel, etc.) connected to the apparatus 1 by wire or wirelessly. It is possible to accept. For example, even if the user inputs a numerical value shown in FIG. 7 (7B), which will be described later, from the keyboard, the determination factor factor data reception unit 10 and / or the risk factor data reception unit 18 receives the input numerical value. good. In this way, the determination formula factor data reception unit 10 and / or the risk factor data reception unit 18 may be connected to an input device, another computer, or a communication device not shown in FIG.

The determination factor data receiving unit 10 is an equation for determining the risk of road collapse, and is data necessary for determining a specific determination equation as a function expression of the cause of road depression and the risk. This is a component functioning as determination formula factor data receiving means for receiving determination formula factor data in which the factors of road depression at a plurality of sampling points sampled for creating a specific determination formula are quantified. Here, as the factors of the road collapse, preferably, the presence of underground cavities, the type of backfill soil, the situation of groundwater, the age of pipe laying, the thickness of the overburden on the pipe, the part of the pipe, the pipe type, Two or more of pipe breakage, traffic vibration, summer temperature and active fault hazard. The determination formula factor data is data digitized for each factor, and is “1” when applicable and “0” when not applicable. Further, the specific determination formula refers to a formula illustrated in FIG. 7 (7C). These will be described in detail later with reference to FIG.

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

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

The first substitution unit 13 is a component that functions as first substitution means for substituting the factor data for judgment formula 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 determines the specific determination formula by substituting the determination formula factor data into the general determination formula and performs an operation.

The threshold determination unit 15 is a component that functions as a threshold determination unit that classifies the determination values calculated by the specific determination formula into a predetermined range and determines a threshold for occurrence of depression based on the actual presence or absence of depression. The threshold value is a determination value that means a boundary that increases the risk of occurrence of depression. The determination value is a value of Y in the specific determination expression exemplified in FIG. 7 (7C). In the example of FIG. 7 (7D), the threshold = −0.2 is determined based on the fact that road depression is recognized when the determination value is −0.2 or less. However, the presence / absence of depression is not always clearly divided at the judgment value = −0.2. For example, there is a spot where a depression occurs even when the judgment value = 0, and there may be a spot where no depression occurs even when the judgment value = −0.3. Despite this situation, the threshold is determined to be −0.2 because, when the threshold is set to −0.2, the correct answer rate for the presence or absence of depression is high and the erroneous determination rate is low. If the threshold value is set to 0 (zero), a point where no depression has occurred within the range of determination value ≦ 0 is likely to be included as an error. On the other hand, if the threshold value is −0.4, a point where depression occurs within the range of determination value ≧ −0.4 is likely to be included as an error. Considering the magnitude of such an error, the determination value (−0.2) having the smallest determination error is set as the threshold value. This point will be described in detail later with reference to FIGS. 7 (7D) and (7E). The threshold 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 that functions as a specific determination formula storage unit that stores at least a 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 a 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 receiving unit 18 functions as a risk factor data receiving unit that receives risk factor data obtained by quantifying the factors of road collapse at a plurality of evaluation target points to be evaluated. The determination formula factor data receiving unit 10 and / or the risk factor data receiving unit 18 are described as components capable of receiving specific data, but can also receive other data. A configuration unit, for example, a configuration unit that accepts data arbitrarily input by the user may also be used.

The second substitution unit 19 is a component that functions as a second substitution unit that substitutes 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 functioning 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 obtained by quantifying the risk of depression at each evaluation target point.

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

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

The map display unit 23 functions as a map display unit that displays colors or shades on the map read from the map information storage unit 21 based on a determination value indicating the risk of depression at each evaluation target point. It is a component. The color or shading information can be stored in the map information storage unit 21 or in another storage unit (not shown in FIG. 3) so as to be read out. The map information reading unit 22 described above can also read color or shade information from the map information storage unit 21 together with the map information.

The map display unit 23 may be a component that displays only the determination value indicating the degree of risk without displaying the color. The map display unit 23 may be a component that displays both the color and the determination value. Further, the map display unit 23 may display monochrome shades instead of colors. In this case, the determination value can be displayed together with monochrome shading, or can be hidden. Further, when the apparatus 1 is provided with the threshold value determination unit 15, the map display unit 23 may display the color and shades largely changed before and after the threshold value.

As a modification of the above embodiment, the apparatus 1 may include a first depression factor value conversion unit 30, a first storage unit 31, a second depression factor value conversion unit 40, and a second storage unit 41. The first depression factor numerical value conversion unit 30 converts the road depression factor into a numerical value shown in FIG. 7 (7B) described later, and transmits the numerical value data to the determination formula factor data reception unit 10. It is a component that functions as numerical value conversion means. The 1st memory | storage part 31 is a 1st memory | storage means with which the information which memorize | stores the data table for converting a road depression factor into the said numerical value can be read and written. The first depression factor numerical value conversion unit 30 refers to the data table stored in the first storage unit 31 and digitizes each road factor and transmits the digitized data to the determination formula factor data reception unit 10. To do. Similarly, the second depression factor numerical value conversion unit 40 functions as a second depression factor numerical value conversion unit that converts a road depression factor into a numerical value and transmits the numerical value data to the risk factor data receiving unit 18. Part. The 2nd memory | storage part 41 is a 2nd memory | storage means with which the information which memorize | stores the data table for converting a road depression factor into the said numerical value can be read and written. The second depression 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 depression factor numerical value conversion unit 30 and the second depression factor numerical value conversion unit 40 are parts that perform each process by the CPU executing a computer program (a computer program for evaluating the risk of road depression). The first storage unit 31 and the second storage unit 41 are memories represented by a RAM, a hard disk, and the like that can read and write information.

More specifically, the device 1 is a road collapse risk evaluation device for evaluating the risk of road collapse, and is an expression for determining the risk of road collapse, and causes and risks of road collapse. Data necessary for determining a specific judgment formula as a function formula with and accepting judgment formula factor data obtained by quantifying factors of road depression at a plurality of sampling points sampled for creating a specific judgment formula Determination formula factor data receiving means (corresponding to the determination formula factor data receiving unit 10) and a general determination formula {Y is an external criterion indicating whether or not a road has collapsed. X1 to Xn: Xi is an explanatory variable that causes a road collapse, A1 to An are coefficients to be multiplied by X1 to Xn, n is the number of the i-th explanatory variable Xi, and an integer of 1 or more, xij is the jth character of Xi The Gori variables, aij is the category coefficient xij, mi is the number of categories Xi, respectively. } Is substituted into the general judgment formula storage means (corresponding to the general judgment formula storage section 11) and the general judgment formula read from the general judgment formula storage means. The first substituting means (corresponding to the first substituting unit 13) and the factor data for the judgment formula are substituted into the general judgment formula, and the variation between the two groups is relative to the total variation by the quantification theory type II analysis. Specific determination formula determining means (corresponding to the specific determination formula determination unit 14) for determining the specific determination formula specifying the category coefficient (aij) by executing the calculation so as to maximize the value, and the specification for storing the specific determination formula Determination factor storage means (corresponding to the specific determination expression storage unit 16) and risk factor data reception means (dangerous) that receives risk factor data obtained by quantifying the factors of road collapse at a plurality of evaluation target points to be evaluated Factor data And a second substituting unit (second substituting unit) for substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination formula read from the specific determination formula storage unit. 19), and determination value determination means (corresponding to the determination value determination unit 20) that performs a calculation based on the processing of the second substitution means and determines a determination value that quantifies the risk of depression at each evaluation target point. ,including.

FIG. 4 shows a flow of main steps of the road collapse risk evaluation 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 road collapse risk using an apparatus for evaluating the road collapse risk. As shown in FIG. 4, this evaluation method is based on a determination formula factor data receiving step (S100) for receiving data of each factor (determination formula factor data) at a determination formula creation point, and a specific determination formula. A first substitution step (S200) for substituting judgment formula factor data into a general judgment formula, a specific judgment formula decision step (S300) for deciding a specific judgment formula by calculation, and a threshold decision step (S400) for deciding a depression occurrence threshold ), A risk factor data receiving step (S500) for receiving risk factor data obtained by quantifying the factors of road collapse at a plurality of evaluation target points to be evaluated, and substituting the risk factor data into the specific determination formula Second substitution step (S600), determination value determination step (S600) for determining a determination value based on the risk of depression at each evaluation target point by calculation 00), a method for evaluating the risk of road collapse by performing a map display step (step S800) for displaying by color or shade based on a determination value indicating the risk of depression at each evaluation target point on the map. is there. Steps S400 and S800 are not essential steps. In addition, as described in the apparatus 1, the map display step may be a step of displaying only the determination value indicating the degree of risk without performing display of color and shading. Further, the map display step may be a step of displaying both the color, the shading, and the determination value. Further, the map display step may be a step of displaying monochrome shades instead of colors. In this case, the determination value can be displayed together with monochrome shading, or can be hidden. Further, when the threshold value determining step (S400) is performed, the map display step may be displayed by changing the color or shade largely before and after the threshold value. The above steps will be described below with reference to FIGS.

(1) Determination formula factor data receiving step (S100)
This step is a step of receiving determination formula factor data obtained by quantifying the factors of road depression at a plurality (100 in this embodiment) of sampling points sampled for creating a specific determination formula. This step can be performed by the determination formula factor data receiving unit 10 of the apparatus 1. Judgment formula factor data is data necessary to determine a specific judgment formula. The specific determination expression is an expression for determining the risk of road depression, and is a function expression of the cause of road depression (corresponding to the explanatory variable Xi) and the risk (corresponding to the external criterion Y).

FIG. 5 and FIG. 6 show tables quantifying the external reference Y and various explanatory variables X1 to X5 at 100 points for creating a judgment formula. FIG. 5 shows data at each of the first 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 depression at the 1st to 100th points of the road in a specific area in Sakai City and the five explanatory variables that can be the cause are represented by 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 presence / absence (Y) of depression, “1” means that there is no depression, and “2” means that there is depression. Regarding the existence of the cavity (X1), “1” means that there is no cavity, “2” means that there is a cavity at a position less than 1/2 of the soil covering thickness, and “3”. Means that there is a cavity at a position of 1/2 or more of the covering thickness. Regarding 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 status (X3), “1” means no, “2” means yes, “3” means yes / no fluctuation. 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 earth covering (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.

Thus, for example, the first sampling point in the table of FIG. 5 is that Y and X1 to X5 are all “1”, so there is no depression, no cavities, the backfilling soil is gravel, groundwater There is no, the elapsed years are less than 20 years, and the earth covering is a point of 2 m or more. Other sampling points are similarly interpreted by numerical values of 1 to 3. What is important here is that the numerical values 1 to 3 in FIG. 5 and FIG. 6 are merely for categorizing the external criteria and each explanatory variable by type, and the absolute values of the numerical values themselves have meaning. It 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 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 the general judgment formula, a diagram (7B) for explaining the digitized factor data, a diagram (7C) for explaining the specific judgment formula, and the threshold value. Figures (7D, 7E) are respectively shown.

5 and 6, the general judgment formula that 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 (when these are generalized, “Ai”) are coefficients of X1, X2, X3,..., Xn (when these are generalized, “Xi”), respectively. . In the example of FIGS. 5 and 6, since there are five explanatory variables, n = 5. Here, X1 has three variables of x11, x12, and x13 that can take 1 or zero (this is referred to as a categorical variable, but may be simply referred to as a variable hereinafter). X2 has two variables of x21 and x22 that can take 1 or zero. X3 has three variables that can be 1 or zero, x31, x32, and x33. X4 has three variables that can be 1 or zero, x41, x42, and x43. 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, X1 will be described as a representative, and 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 covering, x11 = 0, x12 = 1, and x13 = 0. When there is a cavity at a position of 1/2 or more of the covering, x11 = 0, x12 = 0, and x13 = 1. That is, as shown in (7B), there are three types of combinations of x11, x12, and x13, where only one of x11, x12, and x13 is 1 and the other two are 0. The same applies to X2 to X5. In the case of X2, one of x21 and x22 is 1, and the others are 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 includes (x11, x12, x13), (x21, x22), (x11, x12, x13), as factorial data for judgment formulas that quantify the factors of the road depression and the presence or absence of depression at each sampling point sampled for creating the specific judgment formula. In this step, (x31, x32, x33), (x41, x42, x43), (x51, x52, x53) and Y are received. For example, the sampling point No. 1 shown in FIG. 1, in the group of “no depression” out of two possible classifications (group 2) of the objective variable Y, (1, 0, 0), (1, 0), (1, 0, 0), ( Data of (1, 0, 0) and (1, 0, 0) is received by the determination factor factor data receiving unit 10. Similarly, for example, sampling point No. 1 shown in FIG. 43, in the group of “with depression” out of the two classifications (group 2) that the objective variable Y can take, (0, 1, 0), (0, 1), (0, 1, 0), ( The determination formula factor data of 0, 1, 0) and (0, 1, 0) is received by the determination formula factor data receiving 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, sampling point No. In the case of 1, in the step of substituting the data (1, 0, 0), (1, 0), (1, 0, 0), (1, 0, 0), (1, 0, 0) is there. This step can be performed by the first substitution unit 13 of the device 1. In the example of a total of 100 sampling points shown in FIGS. 5 and 6, there are 45 points without depressions and 55 points with depressions. Therefore, in the group without depression (referred to as Y1 group), 45 Y = f (Xi) into which the determination formula factor data is substituted are obtained. Similarly, 55 Y = f (Xi) are obtained by substituting the factor data for the judgment formula in the depressed group (referred to as Y2 group).

(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 an operation to determine the specific judgment formula. Specifically, in this step, a specific determination formula is obtained by quantification theory type II analysis. This step can be performed by the specific determination formula determination unit 14 of the device 1. Now, the coefficients (a11, a21, etc.) in A1 to A5 are assumed to be aij. i is an order of A1 to A5, and is a positive integer of 1 to 5. j is the order of the coefficients in one Ai. Since A1 has three coefficients, j is any positive integer from 1 to 3. In order to best discriminate the classification (group 2) of the objective variable Y, aij is determined so as to maximize the intergroup 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 variance SB / total variance ST). At this time, generally, the total variance ST = intra-group variance SW + inter-group variance SB. In such conditions, when solving equation as correlation ratio is maximized, this problem results in an eigenvalue problem, the coefficient aij seeking is obtained as a component of the eigenvector corresponding to the largest eigenvalue (eta ^2). As a result, in the present calculation example, a specific determination formula as shown in Formula 1 of (7C) is obtained. Such an analysis method is disclosed in, for example, “Multivariate Statistical Analysis Method” (author: Yutaka Tanaka / Kazumasa Wakimoto, publisher: Hyundai Mathematics). The specific determination expression is a relational expression between the explanatory variable Xi and the objective variable Y in a state where the coefficient aij is specified. Y means “determination value” indicating the risk of road collapse in the specific determination formula. That is, Y, which means the presence or absence of a road depression or a sign in the general determination formula, is a “determination value” indicating the risk of road collapse in the specific determination formula. Therefore, Y in the general determination formula may be distinguished from “Y1” and Y in the specific determination formula may be described as “Y2”.

(4) Threshold determination step (S400)
This step is a step of determining a threshold value for occurrence of depression. The threshold value means a determination value for determining the risk of occurrence of depression. This step can be performed by the threshold value determination unit 15 of the device 1. In this step, the determination value calculated by the specific determination formula is classified into a predetermined range, and a threshold value for occurrence of depression is determined based on the presence or absence of actual depression. As shown in (7D, 7E), in this embodiment, the determination values are classified into 16 in a predetermined range of 0.2. No. A depression of the road occurs from 1 to 7 (that is, the judgment value is −0.2 or less). However, road depression does not occur 100% when the determination value is −0.2 or less, and does not occur 100% when the determination value is greater than −0.2. There may be no road depression even when the determination value is −0.2 or less, or there may be road depression even when the determination value is greater than −0.2. In such a situation where the set of road depressions and the set of road depressions overlap each other, the judgment value is No. It is appropriate to determine the position where the determination error is the smallest as the threshold value when setting in each stage from 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 when the threshold value is set to −0.2 (in this case, a range equal to or greater than the threshold value). In many cases, depressions occur.) Even if the threshold value is set to a value larger than −0.2, the determination error becomes larger than when the threshold value is set to −0.2 (in this case, there is no depression in the range below the threshold value). Are included). When the threshold is set to −0.2, it is possible to separate the case where the depression has occurred and the case where the depression has not occurred with a high probability. It should be noted that the critical rate of the presence or absence of depression when the threshold value is −0.2 at 100 sample points is 96%. This means that the error is 4%. Steps S100 to S400 are processing until a specific determination formula is obtained from a general determination formula based on information on sample points.

(5) Risk factor data reception step (S500)
This step is a step of accepting risk factor data obtained by quantifying the factors of road depression at a plurality of evaluation target points to be evaluated. This step can be performed by the risk factor data receiving unit 18 of the device 1. In this embodiment, risk factor data for 30 evaluation target points are accepted. The evaluation target point is preferably located in a geographically close position such as the same municipality as the sampling point or the same district in the same municipality.

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

Numerals 1 to 3 of X1 to X5 in FIG. 8 have the same meaning as the numerical values in FIGS. For example, the evaluation target point No. In “1”, “1” is entered in X1 to X4, and only X5 is “2”. This is because the No. At point 1, it means that there is no cavity, the backfilling soil is gravel soil, there is no groundwater, the elapsed time is less than 20 years, and the earth covering 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 used to classify the explanatory variables by type, as in FIGS. 5 and 6, and the absolute values of the numerical values themselves have meaning. That 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.

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. 1 shown in FIG. 1, the data of (1, 0, 0), (1, 0), (1, 0, 0), (1, 0, 0), (0, 1, 0) is the risk factor data receiving unit. 18 is accepted. Similarly, for example, the evaluation target point No. 1 shown in FIG. 7, the risk factor data of (1, 0, 0), (0, 1), (0, 1, 0), (0, 1, 0), (0, 0, 1) is used for the risk level. It is received by the factor data receiving unit 18. Such data reception is performed in a similar manner for the other 28 sampling points.

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

(7) Determination value determination step (S700)
This step is a step of determining a determination value based on the risk of depression at each evaluation target point by calculation using a specific determination 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 performing display by color or shade based on a 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 an evaluation target point and a risk evaluation map (9B) in which a unit block including each point is displayed on the map by color classification or gray scale classification.

(9A) As shown in (9A), the evaluation target points with 1 to 30 are clearly indicated on the map. The map of (9B) is obtained in S700 in a unit block (for example, a length of 50 m on the left and right, a range of radius of 50 m, etc. is arbitrarily determined) around each evaluation target point on the map shown in (9A). The judgment value (which may be referred to as a risk level) and a color based on the judgment value are displayed. The map (9B) may be referred to as a risk evaluation map. The numerical values at 30 locations in the risk evaluation map mean the risk of road depression, and the smaller the numerical value, the higher the risk of depression. Blocks with a high risk of depression are preferably displayed in dark red, for example, and are displayed in different colors from light red, orange, yellow, light blue, and dark blue as the risk decreases. Also, the degree of danger may be displayed in monochrome shades without using color. The risk evaluation map (9B) is an example in which the risk is visually represented by monochrome shading, and the risk is higher as it is closer to black. The risk evaluation map may be displayed with only the determination value, only the color or monochrome shading, or any combination of the determination value and the color or monochrome shading.

In the flow of FIG. 4, prior to step S100, the road collapse factor is converted into the numerical value shown in FIG. 7 (7B), and the first depression is transmitted to the determination formula factor data receiving unit 10 You may perform a factor numerical value conversion step (step S50). This step is a step executed by the first depression factor value conversion unit 30 with reference to the data table of the first storage unit 31. Prior to step S500, a second depression factor value conversion step (step S450) may be performed in which the road depression factor is converted into a numerical value and the numerical value data is transmitted to the risk factor data receiving unit 18. This step is a step executed by the second depression factor value conversion unit 40 with reference to the data table in the second storage unit 41.

The above evaluation of the risk of road collapse is based on five factors for road collapse. Next, the evaluation when the cause of the road depression is limited to two will be described with reference to FIG.

FIG. 10 is a diagram (10A) for explaining a specific determination formula, diagrams (10B, 10C) for explaining a threshold value, and a risk evaluation in which a unit block including each point is displayed on the map by color coding or gray coding. Each map (10D) is shown.

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

In this narrowing down, explanatory variables that have a large degree of influence on the judgment result (factors with a large range) are left with priority, and other explanatory variables are discarded. Here, the range is a numerical range 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 the three types of road collapse factors of the presence of the cavity (X1), the number of years elapsed (X4), and the situation of the groundwater (X3) is large in this order, and the others are smaller than these. Therefore, two factors are selected from the larger range, and X1 and X4 are adopted as explanatory variables. When calculation is performed by applying the quantification theory type II analysis to the two factors, the specific determination formula (Formula 2) of (10A) is obtained.

(10B) shows the distribution of judgment values (sample scores). It is the same as the above-mentioned result that the risk of depression increases as the determination value decreases. 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 out of 14 cases are misidentified (no depression). Result. For this reason, the threshold value is -0.2. In this case, the discriminatory probability is 94%. From this result, it is considered that a sufficiently high evaluation can be performed depending on the selection of the depression factor even when the depression factor is limited to two.

Substituting each data of X1 and X4 in the table shown in FIG. 8 into the specific determination formula of (10A), determination of the depression risk level of each evaluation target point is performed, and subsequently, the determination value and its basis on the map As a result of displaying the shading, a risk evaluation map shown in (10D) is obtained. Also in this map, as in the risk evaluation map of FIG. 9 (9B), the smaller the numerical value, the higher the risk of depression. Evaluation point No. The numerical values of “boundary part” and “intersection part” other than 1 to 30 are the same as (9B) in terms of notation. (10D), the evaluation target point No. 13 and no. There is a high possibility that depressions will occur at each of the 15 points, and the same result as the risk evaluation map of (9B) is obtained. As for other evaluation target points, since the depression factor is narrowed down, the difference in the judgment values (shading difference) is small. In addition, the evaluation target point No. 10 and no. In 16, the determination value changes from minus to 0 or plus. These are considered to be effects using a specific judgment formula that narrows down the cause of depression. However, it can be read that the place has a relatively high risk level.

Next, the evaluation when the number of road cave-in factors is 11 will be described.

FIG. 11 and FIG. 12 show tables in which various explanatory variables X6 to X11 are digitized at 100 points for determination formula creation. FIG. 11 shows the data of each of the 1st to 50th points, and FIG. 12 shows the data of each of the 51st to 100th points. Y and X1 to X5 are as shown in FIGS. FIG. 13 compares the size of the range of Expression 3 in FIG. 13A for explaining the specific determination formula, the figures 13B and 13C for explaining the threshold values, and 11 road crush factors (13A). Each figure (13D) is shown.

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

11 and 12, in the table of the pipe line (X6), “1” means the mounting pipe, “2” means the main pipe, and “3” means the other. Regarding the pipe type (X7), “1” means porcelain pipe, “2” means reinforced concrete, and “3” means others. Regarding the sewer pipe breakage situation (X8), “1” means “present” and “2” means “not present”. Regarding traffic vibration (X9), “1” means more than a predetermined standard, and “2” means below a predetermined standard. Regarding summer temperature (X10), “1” means higher than a predetermined temperature, and “2” means lower than a 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 values of the numerical values themselves have no meaning. Therefore, the alphabets a, b, and c may be used instead of the

numerical values

1, 2, and 3. Further, 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 with respect to Y and X1 to X5.

The data received by the judgment formula factor data receiving unit 10 includes the presence of underground cavities, the type of backfill soil, the status of groundwater, the number of years the pipe has been laid, the thickness of the covering over the pipe, the part of the pipe, the pipe Data on species, pipeline breakage, traffic vibration, summer temperature and active fault hazard. As described with reference to FIG. 7 (7B), such data (factorial data for judgment formula) is data that is quantified for each factor, and is “1” when applicable, and is not applicable. Represents data that is “0”.

After receiving the numerical data related to the eleven types of factors in the determination formula factor data receiving unit 10, each of the component units 11 to 23 in the apparatus 1 performs each process 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 determination values (sample scores) of (13B, 13C), the risk of road collapse increases as the determination value decreases, as described above. Also in this example, the threshold is −0.2. Using 11 depression factors, the discriminatory probability was about 97%. It can be seen that the hit ratio is higher than when there are two or five depression factors (94% for both). It should be noted that the category quantities of some explanatory variables such as the number of years elapsed (X4) show results that do not adequately describe the actual impact (for example, X4 is a negative category even for new tubes with a low degree of aging) Quantity has arisen).

(13D) It can be seen that the presence of the cavity (X1), the number of years elapsed (X4), and the status of the groundwater (X3) are at the top. From this, it is considered that these three types of factors are preferably considered in evaluating the risk of road collapse.

FIG. 14 shows a table in which various explanatory variables X6 to X11 at 30 evaluation target points that are targets for risk assessment are quantified. The explanatory variables X1 to X5 are as shown in FIG. FIG. 15 shows a risk evaluation map in which unit blocks including each point are displayed on the map by color classification or light / dark classification.

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

The road collapse risk evaluation method according to this embodiment as described above is specifically a method for evaluating the risk of road collapse using the device 1 for evaluating the risk of road collapse,
This is an equation for determining the risk of road collapse, and is necessary for determining the specific determination formula as a function expression of the factors of road collapse and the risk, and is sampled for creating the specific determination formula A determination formula factor data receiving step for receiving determination formula factor data quantifying the factors of road depression at a plurality of sampling points;
General judgment formula (Y) as the basis of the specific judgment formula {Y is an external standard indicating the presence or absence of a road depression, X1 to Xn: Xi is an explanatory variable that causes a road depression, and A1 to An Is a coefficient to be multiplied by X1 to Xn, 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, aij is the categorical coefficient of xij, and mi is The number of categories of Xi is shown respectively. }, The first substituting step (S200) of substituting the factor data for the judgment formula constituted by the categorical variable into the general judgment formula read from the general judgment formula storage means for storing
Substituting the factor data for the judgment formula into the general judgment formula and performing an operation so that the variation between the two groups is maximized relative to the total variation by quantification theory type II analysis, and the category coefficient (aij Specific determination formula determination step (S300) for determining a specific determination formula specifying
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 to be evaluated;
A second substituting step (S600) of substituting the factor data for risk composed of categorical variables at the evaluation target point into the specific determination formula read from the specific determination formula storage means for storing the specific determination formula;
A determination value determination step (S700) for performing a calculation based on the processing of the second substitution step and determining a determination value obtained by quantifying the risk of depression at each evaluation target point;
including.

<3. Computer program for assessing the risk of road collapse>

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 evaluating the risk of road collapse is a computer represented by the device 1, which includes a determination formula factor data reception unit 10, a first substitution unit 13, a specific determination formula determination unit 14, a risk factor data reception unit 18, It functions as the second substitution unit 19 and the determination value determination unit 20. Further, the computer program for evaluating the risk of road collapse is a computer that is represented by the apparatus 1 and includes a general determination formula reading unit 12, a threshold value determination unit 15, a specific determination formula reading unit 17, a map information reading unit 22, a first collapse factor. It is preferable to function as at least one of the numerical value conversion unit 30 and the second depression factor numerical value conversion unit 40.

The road collapse risk evaluation computer program is a computer typified by the device 1, and includes 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 is a computer typified by the apparatus 1, and includes a first depression factor numerical value conversion step (S50), a general judgment formula reading step (herein referred to as S150), and a threshold value determination step. (S400), at least one of a second depression factor numerical value conversion step (S450), a specific determination formula reading step (referred to here as S550), a map information reading step (referred to here as S750), and a map display step (S800). Preferably, one step is executed.

Further, the road collapse risk evaluation computer program is executed after being installed in a memory in the apparatus 1 from another computer (server) that is physically separated from the apparatus 1, or an information recording medium (for example, Once stored in a compact disk, portable flash memory, FD, MD, etc.) and installed in the memory in the device 1 through insertion or connection of the information recording medium to the device 1, it is executed or not read as it is installed. It may be issued and executed. The information recording medium means a non-temporary tangible recording medium. The computer program for evaluating the risk of road collapse may be in the form of one program or two or more programs. Similarly, the information recording medium storing the computer program for evaluating the risk of road collapse may be 1 or 2 or more.

The road collapse risk evaluation computer program according to this embodiment is specifically installed in a computer, and causes the computer to function as the road collapse risk evaluation apparatus 1 for evaluating the risk of road collapse. A program,
The computer
This is an equation for determining the risk of road collapse, and is necessary for determining the specific determination formula as a function expression of the factors of road collapse and the risk, and is sampled for creating the specific determination formula Determination formula factor data receiving means (corresponding to the determination formula factor data receiving unit 10) for receiving determination formula factor data quantifying the factors of road depression at a plurality of sampling points;
General judgment formula (Y) as the basis of the specific judgment formula {Y is an external standard indicating the presence or absence of a road depression, X1 to Xn: Xi is an explanatory variable that causes a road depression, and A1 to An Is a coefficient to be multiplied by X1 to Xn, 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, aij is the categorical coefficient of xij, and mi is The number of categories of Xi is shown respectively. }, The first substituting unit (corresponding to the first substituting unit 13) for substituting the judgment formula factor data constituted by the categorical variables into the general judgment formula read out from the general judgment formula storage unit.
Substituting the factor data for the judgment formula into the general judgment formula and performing an operation so that the variation between the two groups is maximized relative to the total variation by quantification theory type II analysis, and the category coefficient (aij ) Specific determination formula determination means (corresponding to the specific determination formula determination unit 14) for determining the specific determination formula specifying
A risk factor data receiving means (corresponding to the risk factor data receiving unit 18) for receiving risk factor data obtained by quantifying the factors of road collapse at a plurality of evaluation target points to be evaluated;
Second substitution means (into the second substitution section 19) substitutes risk factor data composed of categorical variables at the evaluation target point into the specific judgment formula read from the special judgment formula storage means for storing the specific judgment formula. Equivalent), and a calculation process based on the processing of the second assigning means to function as a judgment value determining means (corresponding to the judgment value determining unit 20) for determining a judgment value obtained by quantifying the risk of depression at each evaluation target point. .

The present invention can be used for technologies and industries that predict the danger of road cave-in.

Claims

A road collapse risk evaluation device for evaluating the risk of road collapse,
Formula for determining the risk of road collapse, and data necessary for determining a specific determination formula as a function expression of the factor of road collapse and the risk, for creating the specific determination formula Determination factor factor data receiving means for receiving determination factor factor data obtained by quantifying the factors of road depression at a plurality of sampled sampling points;
General judgment formula shown in the following formula (A) based on the specific judgment formula {Y is an external standard indicating whether or not a road is depressed, X1 to Xn: Xi are explanatory variables that cause a road depression, A1 to An are coefficients multiplied by X1 to Xn, n is the number of the i-th explanatory variable Xi, an integer of 1 or more, xij is the jth categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi respectively indicate the number of categories of Xi. }, General judgment expression storage means for storing
First substituting means for substituting the judgment formula factor data constituted by the categorical variable into the general judgment formula read from the general judgment formula storage unit;
Substituting the judgment factor factor data into the general judgment formula, and performing a calculation so as to make the variation between the two groups relatively large with respect to the total variation by quantification theory type II analysis, Specific determination formula determining means for determining the specific determination formula specifying the coefficient (aij);
Specific determination formula storage means for storing the specific determination formula;
A risk factor data receiving means for receiving risk factor data quantifying the factors of road collapse at a plurality of evaluation target points to be evaluated;
Second substitution means for substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific judgment formula read from the specific judgment formula storage means;
A determination value determining means for performing a calculation based on the processing of the second substitution means, and determining a determination value obtained by quantifying the risk of depression at each evaluation target point;
Road cave risk assessment device including
2. The road collapse risk according to claim 1, further comprising threshold determination means for classifying the determination value calculated by the specific determination formula into a predetermined range and determining a threshold for occurrence of depression based on the presence or absence of 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, a map display means for performing display by color or shade based on the determination value indicating the risk of depression at each evaluation target point;
The road collapse risk evaluation apparatus according to claim 1 or 2, further comprising:
The cause of the road collapse is
Existence of underground cavities, type of backfill soil, groundwater condition, age of pipe laying, thickness of earth covering on pipe, pipe part, pipe type, pipe breakage, traffic vibration, summer temperature and activity The road collapse risk evaluation apparatus according to any one of claims 1 to 3, wherein there are two or more of the fault hazards.
A method for evaluating the risk of road depression using a device for evaluating the risk of road depression,
Formula for determining the risk of road collapse, and data necessary for determining a specific determination formula as a function expression of the factor of road collapse and the risk, for creating the specific determination formula A determination formula factor data reception step for receiving determination formula factor data quantifying the factors of road depression at a plurality of sampled sampling points;
General judgment formula shown in the following formula (A) based on the specific judgment formula {Y is an external standard indicating whether or not a road is depressed, X1 to Xn: Xi are explanatory variables that cause a road depression, A1 to An are coefficients multiplied by X1 to Xn, n is the number of the i-th explanatory variable Xi, an integer of 1 or more, xij is the jth categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi respectively indicate the number of categories of Xi. }, The first substituting step of substituting the determination formula factor data constituted by the categorical variable into the general determination formula read from the general determination formula storage means for storing
Substituting the judgment factor factor data into the general judgment formula, and performing a calculation so as to make the variation between the two groups relatively large with respect to the total variation by quantification theory type II analysis, A specific determination formula determining step for determining the specific determination formula specifying the coefficient (aij);
A risk factor data reception step for accepting risk factor data that quantifies the factors of road collapse at a plurality of evaluation target points to be evaluated;
A second substituting step of substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination formula read from the specific determination formula storage means for storing the specific determination formula;
A determination value determination step for performing a calculation based on the processing of the second substitution step and determining a determination value obtained by quantifying the risk of depression at each evaluation target point;
Road collapse risk evaluation method including
The road collapse risk according to claim 5, further comprising a threshold determination step of classifying the determination value calculated by the specific determination formula into a predetermined range and determining a threshold for occurrence of depression based on the presence or absence of actual depression. Evaluation methods.
On the map read out from the map information storage means for storing the information of the map including each evaluation target point, the color or gray level is classified according to the judgment value indicating the risk of depression at each evaluation target point. The road collapse risk evaluation method according to claim 5 or 6, further comprising a map display step for performing display.
A computer program that is installed in a computer and causes the computer to function as a road collapse risk evaluation device for evaluating the risk of road collapse,
The computer
Formula for determining the risk of road collapse, and data necessary for determining a specific determination formula as a function expression of the factor of road collapse and the risk, for creating the specific determination formula Determination factor factor data receiving means for receiving determination factor factor data quantifying the factors of road depression at a plurality of sampled sampling points;
General judgment formula shown in the following formula (A) based on the specific judgment formula {Y is an external standard indicating whether or not a road is depressed, X1 to Xn: Xi are explanatory variables that cause a road depression, A1 to An are coefficients multiplied by X1 to Xn, n is the number of the i-th explanatory variable Xi, an integer of 1 or more, xij is the jth categorical variable of Xi, and aij is the categorical coefficient of xij. , Mi respectively indicate the number of categories of Xi. }, The first substituting means for substituting the judgment formula factor data composed of the categorical variables into the general judgment formula read from the general judgment formula storage means.
Substituting the judgment factor factor data into the general judgment formula, and performing a calculation so as to make the variation between the two groups relatively large with respect to the total variation by quantification theory type II analysis, Specific determination formula determining means for determining the specific determination formula specifying the coefficient (aij);
Risk factor data receiving means for receiving risk factor data quantifying the factors of road collapse at a plurality of evaluation target points to be evaluated;
A second substituting unit for substituting the risk factor data constituted by the categorical variable at the evaluation target point into the specific determination formula read from the specific determination formula storage unit that stores the specific determination formula; and A road collapse risk evaluation computer program that performs calculation based on the processing of the second substitution means and functions as a determination value determination means that determines a determination value obtained by quantifying the risk of depression at each evaluation target point.
The computer,
9. The road collapse risk according to claim 8, further comprising a threshold determination unit that classifies the determination value calculated by the specific determination formula into a predetermined range and determines a threshold for occurrence of depression based on the presence or absence of actual depression. Computer program for evaluation.
The computer,
On the map read out from the map information storage means for storing the information of the map including each evaluation target point, the color or gray level is classified according to the judgment value indicating the risk of depression at each evaluation target point. The computer program for evaluating the risk of road collapse according to claim 8 or 9, further functioning as map display means for performing display.