JP2018014063A - Sewer deterioration prediction system and sewer deterioration prediction method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a sewer detection prediction system capable of predicting the deterioration state of a sewer on a span-by-span basis.SOLUTION: A deterioration prediction unit derives life spans or deterioration levels of spans of the same pipe ages which are management objects for a municipality. It allocates the spans to total height at positions of the pipe ages on a Weibull curve figure. It regards pipe ages of intersection positions (Z5, Z6, Z7) of a horizontal line (Y2) from the center in a height direction of each of the allocated spans to a direction increasing the pipe age or a direction decreasing the pipe age and w1 to w3 as indicators indicating soundness of the respective spans, and outputs them to a span deterioration database.SELECTED DRAWING: Figure 14

Description

  The present invention relates to a system for predicting deterioration of a sewer pipe for each span and a method for predicting deterioration of a sewer pipe for each span.

  For example, it is effective to predict the deterioration of the sewage pipe when managing the sewage pipe in each local government. It is conceivable that the deterioration of the sewer pipe is predicted by using a statistically derived deterioration evaluation formula as shown in Patent Document 1, for example.

JP 2009-48384 A

  However, in the deterioration prediction of the sewer pipe using the deterioration evaluation formula as in Patent Document 1, the prediction is too rough, and for example, it is possible to grasp what state the sewage pipe is in at what point. difficult.

  Therefore, an object of the present invention is to provide a sewage pipe deterioration prediction system and a sewage pipe deterioration prediction method that can predict and grasp the state of the sewage pipe in detail.

  A sewage pipe deterioration prediction system according to the present invention for achieving this object is a sewage pipe deterioration prediction system for predicting deterioration of each span of the sewage pipe, and includes a plurality of attributes for each of the spans subject to deterioration prediction. And a magnitude or value (for example, a degree of lowness) of a risk level (priority such as repair) for each of the target spans based on the plurality of attributes stored in the attribute information storage unit. Discriminant value calculation unit that allocates the discriminant value to be shown, and sewer pipe dredgings that belong to each of multiple levels of soundness or urgency at each pipe age, estimated based on the deterioration status of each span of the sewer pipe droop being aggregated A degree-of-soundness or emergency level storage unit that stores a ratio of all spans to all spans, and ratio information of spans belonging to each degree of health or emergency level stored in the level of soundness or emergency level storage unit and the pair The soundness level to which all or a part of the target span belongs at the time of reaching the soundness level or the urgency level of all or a part of the target span based on the discriminant value of the span, for example, at a specific time in the future Alternatively, a deterioration prediction unit that predicts the degree of urgency is provided. The span of the sewage pipe rod is, for example, a sewage pipe line extending between manholes. The plurality of attributes of the target span stored in the attribute information storage unit includes, for example, a route extension (length) of the span. The soundness level or urgency level storage unit is statistically formed based on, for example, information on a wide range of sewer pipes that do not include the target span. When the discriminant value indicates a low risk level, it is predicted that the target span having a small discriminant value and a high risk level will reach a lower soundness level or a higher urgency level earlier. Here, for example, the ratio of spans belonging to each soundness level or urgency level is determined for each tube age with respect to a length (reference length) indicating, for example, 100% or 100% in the order of soundness level or urgency level. Allocate (prediction information). Then, for example, based on the allocation of survey spans having the same tube age on the prediction information for the tube age at the time when prediction is desired, it is determined which soundness or urgency each survey span belongs to. Here, if a specific survey span belongs to a specific soundness level or urgency level, it can be determined that the survey span has reached the soundness level or urgency level at the age of the tube. Further, if a specific survey span is located at the start end of a specific soundness level or urgency level, it can be determined that the survey span has reached the soundness level or urgency level at that age. Or, for example, while assigning survey spans having the same tube age on the prediction information at the tube age at the time of execution of the prediction process, curve data representing each soundness level or urgency level linking the allocation points of the prediction information Acquired and the age of the tube at the intersection of each curve and each survey span can be determined as the time when each survey span has reached each soundness or urgency.

  For example, the survey spans having the same tube age on the prediction information are allocated by allocating the survey spans in the order of the discriminating values or the entire reference length. For example, the direction in which the discrimination value decreases and the direction in which the urgency level increases are matched.

  Alternatively, the surveyed soundness level or urgency level of the target span obtained by investigating the inside of the sewer pipe with a TV camera or the like is recorded in the attribute information storage unit, and the deterioration predicting unit records the surveyed soundness level of each target span. Alternatively, the soundness or urgency to which each or all of the target span belongs at the time of reaching the soundness or urgency of each or all of the target span based on the urgency level, for example, at a specific time in the future Can be configured to predict degrees. For example, collect target spans (survey spans) with the same age at survey, survey tube age and surveyed soundness or urgency, and allocate the collected target spans on information for predicting tube age at the time of survey using a TV camera or the like. . The allocation is performed, for example, by allocating survey spans in whole or over the entire length in the order of determination values within a range of soundness or urgency corresponding to the surveyed soundness or urgency. For example, the direction in which the discrimination value decreases and the direction in which the urgency level increases are matched. The allocation of the survey span on the prediction information for the tube age at the time when the prediction is desired is performed so that the position in the length direction of the allocation on the prediction information of the tube age at the time of the survey does not change. For example, the position of soundness or urgency in the direction or length direction when the collected target spans are allocated on the information for predicting the tube age at the time of survey is specified, and the allocated survey span is aligned in the tube age direction. It is possible to predict the soundness level or urgency level to which each soundness level or urgency level or the survey span belongs by assigning it to the prediction information at the age of the tube at the time when it is desired to be moved. Determination of the degree of soundness or urgency at the time of investigation is performed depending on which stage of soundness or degree of urgency stored in the soundness or urgency level storage unit the degradation state of the investigated span. Alternatively, for example, the survey span is allocated to the range of the soundness or urgency corresponding to the investigated soundness or urgency, and the curve data representing each soundness or urgency that connects the allocation points of the prediction information is acquired, The tube age at the intersection of each curve and each survey span can be determined as the point in time when each survey span has reached each soundness or urgency.

  The deterioration predicting unit can be configured to predict a point in time at which each soundness level or urgency level of all or a part of the target span is reached based on the route extension of the target span. The allocation of the survey span on the prediction information can be performed so that the route extension of each survey span is reflected. That is, the allocation length of each survey span may correspond to the route extension of the survey span.

  The sewage pipe deterioration prediction method according to the present invention is a sewage pipe deterioration prediction method for predicting deterioration of each span of the sewage pipe, wherein the target span is based on a plurality of attributes of each deterioration prediction target span. Each is assigned a discriminant value indicating the magnitude or value of the risk (for example, the degree of lowness), and based on the total deterioration status of each sewage pipe span, multiple levels of soundness or emergency at each age The ratio of the span of the sewer pipes belonging to each of the degrees is estimated with respect to the total span, and based on the ratio information of the span belonging to each soundness level or urgency level and the discriminant value of the target span, all or a part of the target span is estimated. Predicting the degree of soundness or urgency to which all or part of the target span belongs at the time of reaching or specifying the degree of soundness or urgency, respectively. . In addition, based on the survey soundness or urgency level of the target span obtained by investigating the inside of the sewer pipe with a TV camera, etc. It is also possible to be configured to predict the soundness level or urgency level to which all or a part of the target span belongs at a specific time. The degree of soundness or emergency to which all or part of the target span belongs at the time of reaching or specifying the degree of soundness or urgency of the whole or part of the target span based also on the pipeline extension of the target span Can be configured to predict degrees.

  If the sewage pipe deterioration prediction system or the sewage pipe deterioration prediction method of the present invention is used, the management target sewage pipe deterioration can be grasped and managed for each span.

It is a lineblock diagram of a sewer pipe dredging deterioration prediction system concerning the present invention. It is a figure which shows the structure of a built-in memory | storage device. It is a figure which shows the structure of a control part. It is a figure for demonstrating the process sequence of a sewer pipe deterioration prediction system. It is a figure which shows the content of the basic information database. It is a figure which shows the content of the discrimination coefficient memory | storage part. It is a figure which shows a prediction execution screen. It is a figure which shows the content of the emergency database. It is a table | surface which shows a Weibull coefficient. It is a figure which shows a Weibull analytical formula. It is a figure which shows the content of the Weibull memory | storage part. It is a figure which shows the case where the object span selected with respect to the total height between the Weibull curves in a specific tube age position is allocated with a Weibull curve diagram. It is a figure which shows the content of the span degradation database. It is a figure which shows the case where a span is allocated with respect to the total height in a specific pipe age position in a Weibull curve diagram. It is a figure which shows the content of the span deterioration database with an asset amount. It is a figure which shows the menu of an output file. It is a figure which shows the display function of a prediction execution screen. It is a figure which shows an error information file.

  FIG. 1 is a configuration diagram of a sewage pipe deterioration prediction system according to the present invention.

  The sewer pipe deterioration prediction system 1 is constituted by a personal computer, and includes a control unit 3, a storage medium connection unit 5 that reads and writes a storage medium, a built-in storage device 7 (hard disk), and an input unit such as a keyboard and a mouse. 9, an output unit 11 such as a printer, and a liquid crystal display 13, for predicting the deterioration of sewer pipes managed by a specific local government (for example, a municipality) and supporting the local sewer pipe management plan. belongs to. A storage medium 15 storing information on sewer facilities managed by the local government is connected to the storage medium connection unit 5.

  FIG. 2 is a diagram showing the configuration of the internal storage device 7.

  The built-in storage device 7 includes a basic information database 17 that stores basic information about sewer pipes, and a multivariate analysis parameter (discriminant coefficient) derived from the basic information of the sewer pipes recorded in the basic information database 17. And receiving the necessary pipe attributes from the storage medium 15 connected to the storage medium connection section 5 and storing the information of the sewerage facilities managed by the local government. Attribute information storage unit 21 for storing an actual survey tabulation table including the urgency level input from 9 and Weibull storage for storing Weibull curve data indicating a plurality of urgency levels relating to repair or deterioration input from the input unit 9 Construction at the time of new construction that records construction costs at the time of construction for each part 23 (soundness or urgency storage part) and the span of each sewer pipe managed by the local government It has a storage unit 25, a. The built-in storage device 7 includes a prediction time database created during the prediction process, and the prediction time database is recorded in the attribute and discrimination coefficient storage unit 19 for each span of the sewer pipes recorded in the attribute information storage unit 21. The urgency database 27 that stores the discriminant value for each span of the sewage pipes managed by the local government, and the discriminant value recorded in the urgency database 27 and the Weibull memory By adding to the span deterioration database 29, the span deterioration database 29 that records the deterioration prediction of each sewer pipe span that is derived based on the Weibull curve data stored in 23, and the information from the new construction cost storage unit 25. And a span degradation database 31 with a trial calculation amount.

  FIG. 3 is a diagram illustrating a configuration of the control unit 3.

  The control unit 3 controls the operation of the entire sewer pipe deterioration prediction system 1, and in particular has a multivariate analysis unit 33 for deriving multivariate analysis parameters (discriminant coefficients) based on information in the basic information database 17. . The control unit 3 also includes a prediction execution screen display unit 35 and includes a prediction execution unit that is executed during the prediction process. The prediction execution unit is recorded in the attribute information storage unit 21 using the derived multivariate analysis parameter based on the prediction start command input from the prediction execution screen displayed on the liquid crystal display 13 by the prediction execution screen display unit 35. A discriminant value calculation unit 37 that calculates and assigns a discriminant value to each span of the sewage pipes that are being operated, and the discriminant value calculated by the discriminant value calculator 37 and the Weibull curve data input to the Weibull storage unit 23 A deterioration predicting unit 39 that derives a predicted urgency level for each span of the water pipe dredger, and an asset evaluating unit 41 that performs asset evaluation for each span are provided.

  FIG. 4 is a diagram for explaining the processing procedure of the sewage pipe deterioration prediction system 1.

1. Processing of Parameter Calculation Unit 33 of Control Unit 3 The multivariate analysis unit 33 derives multivariate analysis parameters (discriminant coefficients) from the basic information of the sewer pipes recorded in the basic information database 17. As shown in FIG. 5, the basic information database 17 includes, for each span of sewage pipes managed by the basic information local government, “pipe type”, “route extension”, “pipe diameter”, “aged years”, “soil cover” ”,“ Total extension ”,“ corrosion of pipe ”,“ sagging in the vertical direction ”,“ damage of pipe ”,“ crack of pipe ”,“ displacement of pipe joint ”and“ intrusion water ”. The recorded sewage pipes preferably include those managed by a plurality of local governments, and it is a good idea to record sewage pipe information managed by a plurality of main local governments. Here, it is possible to arbitrarily select a basic information local government, and actually investigate and record the sewer pipe information managed by the local government in the basic information database 17. Alternatively, it is possible to use information described in an existing database such as the “pipe deterioration database” of the National Institute for Land and Infrastructure Management. Further, both the information in the existing database and the actual survey result on the arbitrarily selected basic information local government can be stored in the basic information database 17 together. “Corrosion of pipe”, “sagging in the vertical direction”, “breakage of pipe”, “crack of pipe”, “displacement of joint of pipe” and “intrusion water” are C, B in order from light to moderate. , A ranks are displayed, and the ranks from 0 to 5 are assigned in order from the highest urgency level for each rank (only the defect of rank C is detected for the span displayed in FIG. 5). ). The information in the basic information database 17 can be updated as appropriate, for example, once a year.

  The multivariate analysis unit 33 calculates, for each pipe type, a parameter (discriminant coefficient) used for calculating a discriminant value for each span in the sewer pipe managed by the local government subject to the prediction of degradation, and a discriminant coefficient storage unit 19 (see FIG. 6). The multivariate analysis unit 33 also calculates a parameter for the entire span that does not distinguish the tube type, and outputs the parameter to the discrimination coefficient storage unit 19. The multivariate analysis unit 33 uses “route extension”, “pipe diameter”, “elapsed years”, and “soil cover” as explanatory variables, and is a management target priority prediction formula using the priority of management target or repair as a target variable. The discrimination coefficient to be used is calculated. The discrimination coefficient is derived using a discrimination coefficient calculation formula derived from the basic information of the sewer pipes recorded in the basic information database 17. This discriminant coefficient calculation formula is obtained by statistical analysis by multivariate analysis, specifically by discriminant analysis.

    2. Processing of the prediction execution screen display unit 35 of the control unit 3

  The prediction execution screen display unit 35 displays a prediction execution screen on the liquid crystal display 13 when the sewage pipe deterioration prediction system 1 is activated, for example. For example, as shown in FIG. 7, the prediction execution screen includes a “target tube type” selection field, a “prediction year” setting field, a “prediction start” button, an “output file” selection field, and an output file display button. Can be used. When the “prediction start” button is pressed, the prediction execution unit starts the prediction process based on the tube age in the year entered in the “forecast year” setting field.

3. Processing of Discriminant Value Calculation Unit 37 of Control Unit 3 The discriminant value calculation unit 37 uses the parameter calculated by the multivariate analysis unit 33 and sets the discriminant value for each span in the sewage pipe managed by the local government for which the deterioration is predicted. As shown in FIG. 8, the data are output to the urgency database 27 in the order of low discriminant values (in order of high priority of management such as repair). The discriminant value is used as an indication of the risk of collapse, and the lower the value, the higher the risk. Specifically, the discriminant value is calculated using “route extension” × X1 + “tube diameter” × X2 + “elapsed years” × X3 + “soil cover” × X4 + “constant”, which is a priority prediction formula for management such as repair. (X1, X2, X3, X4 and “constant” are read from the discrimination coefficient storage unit 19), “route extension”, “tube diameter”, “elapsed years” or “tube age” and “soil cover” Other explanatory variables can be selected. Note that the values of explanatory variables such as route extensions relating to the span of sewer pipes managed by the municipality subject to deterioration prediction are read from the municipal sewerage facility information storage medium 15 and stored in the attribute information storage unit 21 in advance. ing. When the information in the sewer facility information storage medium 15 is updated, the discriminant value calculation unit 37 allocates a new discriminant value for each span based on the updated information, and updates the urgency database 27.

4). Processing of Deterioration Prediction Unit 39 of Control Unit 3 The deterioration prediction unit 39 predicts the degree of soundness of each target span with respect to each tube age using the Weibull curve data or urgency information input to the Weibull storage unit 23. The Weibull curve includes four types of curves w1, w2, w3, and w4 according to the Weibull coefficient (m: shape parameter, η: scale parameter) shown in FIG. A calculation of each Weibull curve data (height data) for each year of 120 years of age is performed in advance in the Weibull storage unit 23 (see FIG. 11). ). The Weibull coefficients m and η are statistically obtained based on information recorded in the basic information database 17, for example. Here, w1 is a life curve, and the ratio of the upper height (1-w1 data) from w1 to the total height (= 1) at each tube age is the lifetime within the entire span of the sewer pipe at the age of the tube. Shows the percentage of span that is expected to be damaged. For example, the total height indicates the total line length of the entire span of the sewer pipe, and the height above w1 indicates the total line length of the span of the sewer pipe that is predicted to reach the end of its life at the age of the pipe. The ratio of the height above w1 to the total height indicates the ratio of the total line extension of the span of the sewer pipe that is predicted to have reached the end of its life at the age of the pipe with respect to the total line extension of the entire span. w4 is a healthy curve, and the ratio of the height below w4 (w4 data) to the total height (= 1) at each tube age is healthy within the entire span of the sewer pipe at the age of the tube, and the urgency level is 4 The ratio of pipe span expected to be For example, the total height indicates the total extension of the entire span of the sewer pipe, and the height below w4 indicates the total extension of the span of the sewer pipe that is predicted to be healthy at the age of the pipe. The ratio of the lower height from w4 indicates the ratio of the total line extension of the span of the sewer pipe expected to be healthy at the age of the pipe with respect to the total line extension of the entire span. w3 is a caution curve, and the ratio of the height below w3 and the height above w4 (w3 data-w4 data) to the total height (= 1) at each age is within the entire span of the sewer pipe at the age of the tube. Shows the ratio of the span of pipes estimated to be urgency 3. For example, the total height indicates the total line length of the entire span of the sewer pipe, and the height below the w3 and the upper side from the w4 indicates the total length of the span of the sewer pipe that is predicted to be urgency 3 at the age of the pipe. The ratio of the height from w3 to the lower side and from w4 to the upper side to the total height indicates the ratio of the total line extension of the span of the sewer pipe predicted to be urgency level 3 at the age of the pipe relative to the total line extension of the entire span. . w2 is a risk curve, and the ratio of the height below w2 and the height above w3 (w2 data-w3 data) to the total height (= 1) at each tube age is within the total span of the sewer pipe at the age of the tube. The ratio of the span of the fistula predicted to be urgency level 2 is shown. For example, the total height indicates the total length of the entire span of the sewer pipe, and the height below w2 and above the top of w3 indicates the total length of the span of the sewer pipe that is predicted to have an urgency level of 2 at the age of the pipe. The ratio of the height from w2 to the lower side and from w3 to the upper side to the total height indicates the ratio of the total line extension of the span of the sewer pipe predicted to be urgency level 2 at the age of the pipe to the total line extension of the entire span. . In addition, the ratio of the height from w1 to the upper side (w1 data-w2 data) from w1 to the total height (= 1) at each tube age is urgency level 1 within the entire span of the sewer pipe at the age of the tube. Shows the percentage of pipe span expected. For example, the total height indicates the total line length of the entire span of the sewer pipe, and the height below w1 and above the height from w2 indicates the total line length of the span of the sewer pipe that is predicted to be urgency 1 at the age of the pipe. The ratio of the height from w1 to the lower side and from w2 to the upper side to the total height indicates the ratio of the total line extension of the span of the sewer pipe predicted to be urgency level 1 at the age of the pipe to the total line extension of the entire span. . In FIG. 12 and the like, w4 and w3 intersect each other at a tube age of nearly 100 years. This is a phenomenon due to a lack of deterioration information at a high tube age. In such a case, w4 is It can be processed as having ended at the intersection position.

  The deterioration predicting unit 39 searches the urgency level database 27 and selects or groups spans having the same tube age, survey time and actual survey urgency level (including no urgency level). The actual survey tabulation table of the attribute information storage unit 21 is referred to for each span survey and the surveyed urgency level. In addition, spans that have the same tube age but are not actually investigated for urgency are selected or grouped. The deterioration prediction unit 39 assigns the grouped spans to the Weibull curve data.

  In the case of a span with a surveyed urgency level, each group within the group is compared to the urgency level (height) corresponding to the surveyed urgency level at the age of the tube for each survey group. Execute processing to allocate the target span. Allocation is the total length of the target span in the group: the length of each target span = the size of the urgency for the age of the tube in the Weibull curve data: the allocation size of each target span, and The lower the span discrimination value is, the higher the value is assigned to the upper side (see FIG. 12, FIG. 12 is a graph of this process of the deterioration prediction unit 39). The intersection positions (for example, z1, z2, z3, and the like) of the horizontal lines (for example, y1) and w1 to w4 extending in the direction in which the tube age increases or decreases in the tube age from the center in the height direction of each target span thus allocated. The span degradation database in which the tube age of z4) is the tube age corresponding to or expected to correspond to each urgent level (including the entire life) indicated by w1 to w4, respectively. 29 (the span reference displayed on the upper side of FIG. 13 and the specific numerical values do not match). For example, in the fifth span from the bottom of FIG. 12 (No. 5 span), the age of the tube at the time of the survey is 19 years, and the age of w3 (corresponding to urgency 2) is 35 years of age (z1), w2 (Applicable to urgency 1) is tube age 68 years (z2), w1 (corresponds to total life, damage), that is, it is tube age 75 years (z3) that reaches collapse It is shown. In addition, the age of 12 years at the intersection position (z4) with w4 indicates that the emergency level is 3 7 years before the survey. In the span deterioration database 29, the age of the year entered in the “predicted year” setting field of the prediction execution screen is recorded in the “age of aging” field, and “emergency level 3” is stored in each of the “adjusted emergency level” fields. The number of years obtained by subtracting “tube age” from each of “2 or 1” is recorded. In addition, the number of years obtained by subtracting “age” from “total life” is recorded in the “remaining life” column, and the ratio of “remaining life” to “total life” is recorded in the “remaining rate” column. . Note that a span in which the column “(predicted) urgency” is not applicable indicates that the urgency level 3 was not reached at the time of the survey (no urgency level or urgency level 4) (intermediate in FIG. 13). (See the span displayed in.)

  In the case of a span that does not have a surveyed urgency level, for each group, the total height (1.0) at the tube age position in the year entered in the “forecast year” setting field on the forecast execution screen is within the group. The process of allocating each target span is executed. The allocation is such that the total route extension of the target spans in the group: the route extension of each target span = the possible size of the Weibull curve data (total height): the allocation size (height) of each span, and The lower the span discrimination value is, the higher the value is assigned to the upper side (see FIG. 14, FIG. 14 is a graph of this process of the deterioration predicting unit 39). The age of the intersections (for example, z5, z6, z7, z8) of the horizontal lines extending in the direction in which the tube age increases or the direction in which the tube age decreases from the center in the height direction of each span thus allocated. Are output to the span deterioration database 29 as indicating the age of the tube that is or is expected to correspond to each urgent level (including the entire life) of w1 to w4 ( (See the lower span reference in FIG. 13, and specific numerical values do not match). That is, in the fourth span from the top of FIG. 14 (the span of No4), the urgency level of w3 (emergency level 2) is 34 years old (z5) and the urgency level of w2 (emergency level 1). It is shown that the age of the tube is 75 years (z6), the urgency of w1 (full life), that is, the collapse is reached at 79 years (z7) (see horizontal line y2). Moreover, it has shown that 15 years of tube age of the intersection position (z8) with w4 has reached the emergency 3 for 13 years before the prediction year. Here, in the column of “(predicted) urgency”, the urgency of the area where the center of each span in the height direction when each span is allocated is recorded.

5. Process of Asset Evaluation Unit 41 of Control Unit 3 The new construction cost storage unit 25 records the construction cost at the time of new construction for each target span. The asset evaluation unit 41 records the total construction cost for the new construction and the construction cost for the new construction for each span in the span deterioration database 29, and sets the "remaining life" to a value obtained by dividing the construction cost for the new construction by the total life. Multiply to determine the “substantial present asset amount”, divide the “substantial present asset amount” by the “remaining life” to obtain the “substantial cost amortization expense”, and record it in the contents of the span deterioration database 29 Thus, the “span deterioration database with asset amount” 31 is constructed (see FIG. 15).

6). Prediction Execution Screen Function When the “Prediction Start” button is pressed, prediction processing is performed, and information on all spans (excluding spans subject to errors) in the “forecast year” is displayed on the upper left side of the prediction execution screen. The file names that can be selected in the “output file” selection field are as shown in FIG. 16, and when “deterioration prediction” is selected, the contents of the span deterioration database 29 for the tube type selected in the “target tube type” selection field are predicted. When displayed on the lower side of the execution screen (see FIG. 17) and selecting “Asset amount prediction”, the contents of the span deterioration database 31 with asset amount for the tube type selected in the “Target tube type” selection column are predicted execution screen When “Pipeline health priority” is selected, the contents (see FIG. 8) of the urgency database 27 for the pipe type selected in the “Target pipe type” selection field are displayed at the bottom of the prediction execution screen. When “Weibull curve” is selected, a Weibull curve graph is generated from the Weibull curve data in the Weibull storage unit 23 and displayed on the upper right side of the prediction execution screen (see FIG. 17). Note that when “error information” is selected in the “output file” selection field, for example, a span (see FIG. 18) that is not subject to prediction because “age age” is not recorded in the attribute information storage unit 21 is predicted. Is displayed below

DESCRIPTION OF SYMBOLS 1 Sewage pipe deterioration prediction system 3 Control part 7 Built-in storage device 9 Input part 17 Basic information database 27 Urgency database 29 Span deterioration database 37 Discriminant value calculation part 39 Deterioration prediction part

Claims (6)

A sewage pipe deterioration prediction system that predicts deterioration of each span of sewage pipe dredging,
An attribute information storage unit that stores a plurality of attributes of each of the target spans for deterioration prediction;
A discriminant value calculating unit that allocates a discriminant value indicating the degree of risk to each of the target spans based on the plurality of attributes stored in the attribute information storage unit;
Soundness level that memorizes the ratio of the span of sewerage pipes belonging to each of multiple levels of soundness or urgency at each pipe age to the total span, estimated based on the deterioration status of each sewage pipe span Or the emergency level storage
Based on the ratio information of spans belonging to each soundness or urgency stored in the soundness or urgency storage unit and the discrimination value of the target span, the soundness of each or all of the target spans Or a deterioration prediction unit that predicts the soundness or urgency to which all or a part of the target span belongs at the time of reaching the urgency level or at a specific time in the future. Prediction system. The attribute information storage unit stores the surveyed soundness level or urgency level of the target span obtained by investigating the inside of the sewer pipe with a TV camera or the like,
The deterioration predicting unit is based on the surveyed soundness level or urgency level of each of the target spans, and when all or a part of the target spans reach the soundness level or urgency level, or when all of the target spans are specified. The sewage pipe deterioration prediction system according to claim 1, wherein the soundness level or urgency level to which each of a part belongs is predicted.   The deterioration predicting unit is configured to determine whether all or a part of the target span is reached at the time when the soundness or urgency of the whole or a part of the target span is reached or specified based on the pipeline extension of the target span. The sewer pipe drooping deterioration prediction system according to claim 1 or 2, wherein the soundness level or urgency level to which it belongs is predicted. A method for predicting deterioration of sewer pipe dredging, which predicts deterioration of each span of the sewer pipe dredging,
Based on a plurality of attributes of each of the target spans of deterioration prediction, assigning a discrimination value indicating the degree of risk to each of the target spans
Estimate the ratio of the span of sewer pipes belonging to each of multiple levels of soundness or urgency at each pipe age to the total span based on the degradation status of each span of the sewer pipes being tabulated.
Based on the ratio information of spans belonging to each soundness level or urgency level and the discriminant value of the target span, the target span is reached when the soundness level or the urgency level is reached or specified for all or a part of the target span. Predicting the soundness level or urgency level to which all or a part of each sewage pipe belongs.   The target span is reached at the time when the soundness or urgency of the target span is reached or specified based on the investigated soundness or urgency of the target span obtained by investigating the inside of the sewer pipe with a TV camera or the like. The sewage pipe deterioration prediction method according to claim 4, wherein the soundness level or the urgency level to which all or a part of each belongs is predicted.   Based on the pipeline extension of the target span, the soundness level to which all or a part of the target span belongs at the time when the soundness or urgency of the whole or a part of the target span is reached or specified. 6. The sewage pipe deterioration prediction method according to claim 4, wherein the degree of urgency is predicted.

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