JP2007003538A - System and method for pipe line damage prediction - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a prediction system for a pipe line damage by an earthquake capable of quickly finding the damage in a pipe line by the earthquake and rapidly restoring. <P>SOLUTION: An acceleration estimation process 202 estimates a magnitude of acceleration of earthquake motion from earthquake intensity data input via an earthquake data input process 101. A damage prediction process 103 contains relations between the magnitude of the acceleration of the earthquake motion, specifications of the pipe line, geographical conditions and the pipe line damage as functions, predicts the damage of the pipe line in a region whose damage is to be predicted based on the functions, the estimated magnitude of the acceleration, a pipe line data file 111 and a geographical condition data file 112, and displays predicted damage results by a predicted damage result output device 50. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a pipeline damage prediction system and pipeline damage prediction method for predicting damage to pipelines such as water and gas due to an earthquake.

When a pipeline damage occurs due to an earthquake, it is difficult to identify the damaged part. For example, in the case of a water pipe, first, water is supplied to a pipe in a certain section and pressure is applied to search for a water leak point. When a leaking point is found, the found leaking point is repaired. And if all the leaking points in this section are repaired, then it is expanded to the section and the searching and repairing of the leaking point is repeated again in the same manner. This process is repeated until all pipelines are restored. For this reason, it takes time to restore the pipeline.
Japanese Patent Laid-Open No. 9-259186

  Among such pipelines, water and gas, which are lifelines in particular, need to be quickly restored. However, the above-described conventional method has a problem that it takes time to find a damaged part and a long time is required for recovery work.

  An object of the present invention is to provide a pipeline damage prediction system and a pipeline damage prediction method that can quickly detect damage to pipelines due to an earthquake and enable quick recovery work.

  A pipeline damage prediction system and a pipeline damage prediction method according to the present invention include a damage prediction target area forming unit that divides a damage prediction target area to form a plurality of damage prediction target areas, seismic intensity data of an earthquake that has occurred, and a pipe It is characterized by comprising damage prediction means for predicting damage for each damage prediction target area based on the data and geological data.

  The following nine inventions are described in the claims of the basic application (Japanese Patent Application No. 2001-241129) of this divisional application.

  The first invention includes a pipeline data file filed with pipeline data in a damage prediction target area, a geological data file filed with geological data in a damage prediction target area, and input means for inputting seismic intensity data of the earthquake that occurred The acceleration estimation means for estimating the magnitude of the earthquake motion acceleration from the seismic intensity data input by the input means, and the relationship between the magnitude of the acceleration of the earthquake motion, the specification of the pipeline, the geology, and the pipeline damage as a function, Damage prediction means for predicting damage to the pipeline in the damage prediction target area based on the estimated acceleration magnitude, the pipeline data file and the geological data file, and damage prediction results by the damage prediction means It is a damage prediction system for a pipeline caused by an earthquake, characterized by comprising damage prediction result output means for displaying

  According to a second aspect of the present invention, there is provided a moving image capturing means for capturing a moving image of the damage prediction target area, and an acceleration estimating means for estimating the magnitude of acceleration of earthquake motion from the moving image of the damage prediction target area captured when the damage occurs by the moving image capturing means. And a damage prediction system for a pipeline caused by an earthquake according to the first aspect of the present invention.

  According to a third aspect of the present invention, there is provided a photographing means for photographing the damage situation of the house after the disaster in the damage prediction target area, an image recording file of the house before the damage in the damage prediction target area, an image before the damage and the damage An earthquake damage prediction system according to a second aspect of the invention, comprising acceleration estimation means for estimating the magnitude, direction and displacement of the acceleration of earthquake motion from a later image.

  According to a fourth aspect of the invention, there is provided damage number estimation means for estimating the number of damages in pipelines within a unit area from acceleration of ground motion, and the damage prediction result output means outputs the estimated number of damages. It is a damage prediction system for a pipeline due to an earthquake according to any one of the inventions 1 to 3.

  According to a fifth aspect of the invention, the acceleration estimation means reads a simulated earthquake motion data file that records simulated earthquake motion acceleration, direction, and displacement, and outputs simulated earthquake motion acceleration, direction, and displacement. 1 is a damage prediction system for a pipeline caused by an earthquake according to any one of the first to fourth aspects of the present invention;

  According to a sixth aspect of the present invention, the damage predicting means is a structure in which the relationship between the magnitude, direction and displacement of acceleration of seismic motion, pipe specifications, geology, and pipe damage is expressed by a tree structure classification rule. A damage prediction system for a pipeline caused by an earthquake according to any one of the first to fifth inventions.

  In a seventh aspect of the invention, the damage prediction means has a relationship between the magnitude, direction and displacement of the acceleration of seismic motion, the specification of the pipeline, the geology, and the pipeline damage as an if-then rule. A damage prediction system for a pipeline caused by an earthquake according to any one of the first to fifth inventions.

  The eighth invention is characterized in that the damage predicting means has a neural network having a relationship among the magnitude, direction and displacement of the acceleration of seismic motion, the specification of the pipeline, the geology, and the pipeline damage. It is a damage prediction system of a pipe line by an earthquake of any 1st thru / or 5th invention.

  According to a ninth aspect of the present invention, there is provided a seismic motion data file filed with seismic motion data that records the magnitude, direction and displacement of the acceleration of seismic motion that has occurred in the past, and the geology of the area where the pipeline damage has occurred due to the past earthquake. Read the geological data file that filed the recorded geological data, the earthquake damage data file that filed the earthquake damage data due to the earthquake that occurred in the past, the earthquake motion data file, the geological data file, and the earthquake damage data file A damage analysis means for extracting the relationship between the magnitude, direction and displacement of the acceleration of seismic motion, the specification of the pipeline, the geology, and the damage to the pipeline, and the analysis result of the damage analysis means is used to predict the damage. A damage prediction system for a pipeline caused by an earthquake according to any one of the sixth to eighth inventions, characterized by having a relationship with the means It is.

  According to the present invention, the search for a damaged part of a pipeline caused by an earthquake has been troublesome and time-consuming to find the damaged part. According to the invention, it is possible to perform restoration work preferentially from the predicted location, and a pipeline damage prediction system and pipe due to an earthquake that can be expected to be promptly restored in the event of an earthquake in a pipeline such as water or gas that is a lifeline. A road damage prediction method can be provided.

  DESCRIPTION OF EMBODIMENTS Hereinafter, an embodiment of a damage prediction system for a pipeline caused by an earthquake according to the present invention will be described with reference to the drawings.

  The damage prediction system of the present invention includes a damage prediction device 100, a seismometer network 30, a seismometer 40, and a damage prediction result output device 50, in which internal processes and file configurations may take a plurality of forms according to the embodiment. Depending on the embodiment, the video camera 10 or the still image camera 20 is provided.

  The damage prediction apparatus 100 includes a process unit 100A and a file unit 100B, and is realized by a computer.

  The process unit 100A includes a seismic intensity data input process 101, an acceleration estimation process 102, a damage prediction process 103, an earthquake damage number prediction process 104, a damage analysis process 105, and a simulated seismic intensity data input process 106.

  The file unit 100B includes a pipeline data file 111, a geological data file 112, an image recording file 113, a seismic motion data file 114, a geological data file 115, an earthquake damage data file 116, and a simulated seismic intensity data file 117. This is the data file above.

  The moving image shooting camera 10 and the still image shooting camera 20 are connected to the acceleration estimation process 102 of the damage prediction device 100 as input devices for moving images and still images, respectively.

  A plurality of seismometers 40 installed at a plurality of points and a seismometer network 30 connecting the seismometers 40 are connected to the seismic intensity data input process 101 of the damage prediction apparatus 100 to obtain seismic intensity information when an earthquake occurs.

  A monitor, a printer, or the like is connected to the damage prediction result output device 50.

  Each process and each file in the above will be described in each embodiment.

(First embodiment)
A first embodiment will be described with reference to FIGS.

  As shown in FIG. 2, the damage prediction system of this embodiment includes a damage prediction device 100-1, a seismometer network 30 and a seismometer 40, and a damage prediction result output device 50.

  The damage prediction apparatus 100-1 includes a seismic intensity data input process 101, an acceleration estimation process 102, a damage prediction process 103, a pipeline data file 111, and a geological data file 112.

  In this embodiment and each embodiment to be described later, the damage prediction target area is divided into mesh areas. As shown in FIG. 3, for example, if a 5 km square area is divided into mesh-like areas each having a side of 250 m, it can be divided into a total of 400 areas with 20 vertically and 20 horizontally. This area can be represented by horizontal (00-19) and vertical (00-19) coordinates.

  When an earthquake occurs in the damage prediction target area, the seismic intensity observed by the seismic intensity meter 40 is input to the seismic intensity data input process 101 through the seismic intensity meter network 30. The seismic intensity data input process 101 determines the seismic intensity of all areas shown in FIG. 2 based on the observed seismic intensity. First, the seismic intensity in the area where the seismic intensity meter 40 is installed is the observed value of the seismic intensity meter.

  Next, for example, in the damage prediction target area shown in FIG. 2, the seismic intensity of the area where the seismic intensity meter is not installed is determined according to the flowchart shown in FIG.

  As step S1, it moves to the first area, moves to step S2 and shifts to step S6 when the seismic intensity of the first area has been determined, and shifts to step S3 when it is not determined.

  In step S3, if the seismic intensity of one or more areas adjacent to the first area has already been determined, the process proceeds to step S4, and if none has been determined, the process proceeds to step S5.

  In step S4, the average seismic intensity of the adjacent area where the seismic intensity is determined is determined as the seismic intensity of that area, and the process proceeds to step S6.

  In step S5, the seismic intensity of the area is determined and the process proceeds to step S6.

  In step S6, if there is a next area for which the seismic intensity is to be determined, the process proceeds to the next area and proceeds to step S2. If there is no next area in which the seismic intensity is to be determined, the process proceeds to step S7.

  If the seismic intensity of all areas has not been determined in step S7, the process proceeds to step S2, and if the seismic intensity of all areas has been determined, the process ends.

  The method for determining the seismic intensity of the area described above and illustrated is an example, and may be determined by other methods.

When the seismic intensity of all areas of the damage prediction target area shown in FIG. 2 is determined as described above, the acceleration for each area is estimated by the acceleration estimation process 101. The acceleration estimation process 101 has a relationship between the seismic intensity and the acceleration of ground motion as shown in the following table in advance, and can estimate the acceleration from the seismic intensity.

  The above is an example of the acceleration estimation method, and can be estimated by other methods.

  From the acceleration estimation process 101, an acceleration estimation result is obtained as follows.

<Example of estimated acceleration in each area>
Area = A00-00,120
Area = A00-01,120
Area = A00-02,210
Area = A00-03, 360
(Omitted)
The damage prediction process 103 reads the acceleration, pipe data file 111, and geological data file 112, and predicts pipe damage. The pipeline data file 111 has the following data.

<Example of pipeline data file 111 description>
Area = A00-00, Tube type = DCIP
Area = A00-00, Pipe type = CIP
Area = A00-01, Tube type = DCIP
Area = A00-02, Pipe type = CIP
Area = A00-02, Pipe type = SP
(Omitted)
The geological data file 112 has the following data.

<Description example of geological data file 112>
Area = A00-00, Geology = Landfill Area = A00-01, Geology = Landfill Area = A00-02, Geology = Alluvium (hereinafter omitted)
Further, the damage prediction process 103 has a function representing the relationship between damage, acceleration, tube type, and geology as an internal structure as shown in the table below.

  The damage prediction process 103 predicts the damage status for each entry in the pipeline data file. First, the estimated acceleration and geology of the area are examined. Next, the estimated acceleration, geology, and pipe type are input to the function that is the internal structure of the damage prediction process 103, and the damage prediction is obtained as an output. This is output to the damage prediction result output device 50.

  As described above, according to the present embodiment, it is possible to predict damage from a computer based on the relationship between the acceleration caused by the earthquake and the damage of the pipeline, predict the damaged part, and perform the restoration work preferentially from the predicted part. As a result, it is possible to quickly restore pipes such as water and gas that are lifelines in the event of an earthquake.

(Second Embodiment)
Next, a second embodiment will be described with reference to FIG.

  The present embodiment includes the configuration of the first embodiment and the moving image shooting camera 10. The video camera 10 is preferably installed in a place where the area such as the rooftop of a building can be looked over.

  With the above-described configuration, when an earthquake occurs, an image captured by the video camera 10 is input to the acceleration estimation process 101 of the earthquake damage prediction system via a network or a medium such as an optical disk. Note that the observation object in the captured image is determined in advance. The acceleration estimation process 101 measures the displacement for each frame of the observation object in the image and estimates the speed and acceleration. As for the displacement of the area photographed by the video camera 10, the acceleration is estimated as described above, and the other operations are the same as in the first embodiment.

  As described above, according to the present embodiment, it is possible to estimate the velocity and acceleration by measuring the displacement of each observation object for each frame from the image captured by the video camera 10, and the seismometer and the accelerometer Even if there is no video, you can estimate the acceleration of the earthquake and predict the damage if there is a movie, and you can preferentially restore from the predicted location, so this is a lifeline such as water and gas pipes Can be quickly recovered in the event of an earthquake.

(Third embodiment)
Next, a third embodiment will be described with reference to FIG.

  The present embodiment includes the configuration of the first embodiment, the image recording file 113, and the still image shooting camera 20. The still image shooting camera 20 is preferably installed in a place where an area such as the rooftop of a building can be looked over, like the moving image shooting camera 10 of the above embodiment. In the image recording file 113, still images periodically taken during normal times are recorded.

With the configuration described above, when an earthquake occurs, an image captured by the still image capturing camera 20 is input to the acceleration estimation process 101 of the earthquake damage prediction system via a network or a medium such as an optical disk. The acceleration is estimated from the difference between the captured image after the earthquake and the image recording file 113. If the difference between the images is large, it can be estimated that there are many destroyed buildings, and therefore the acceleration can be estimated based on the degree. The relationship between the image difference and the acceleration is determined in advance as shown in the table below, and the acceleration is estimated.

  The above is an example of an acceleration estimation method, and other methods are possible.

  After estimating the acceleration, the operation is the same as in the first embodiment.

  As described above, according to the present embodiment, an image captured by the still image capturing camera 20 can be used if there are still images before and after an earthquake even if there are no seismometers and accelerometers. Because it is possible to estimate the acceleration of damage and predict damage, it is possible to restore work preferentially from the predicted location, so that the pipelines such as water and gas that are lifelines can be quickly restored in the event of an earthquake It becomes possible.

(Fourth embodiment)
Next, a fourth embodiment will be described with reference to FIG.

  The fourth embodiment includes an earthquake damage number prediction process 104 in addition to the first to third embodiments. The earthquake damage number prediction process 104 is realized as a process on a computer.

  An empirical formula for each tube type representing the relationship between the number of damage per unit length of the tube and the acceleration is obtained from past earthquake damage data. The earthquake damage number prediction process 104 predicts the damage number of each pipe type for each area using this. This predicted number is also output to the damage prediction result output device 50.

  7 shows a configuration in which the earthquake damage number prediction process 104 is added to the configuration in FIG. 1, but the configuration in which the earthquake damage number prediction process 104 is added to the configuration in FIG. 5 or the earthquake damage number prediction in the configuration in FIG. A configuration to which the process 104 is added may be used.

  As described above, according to the present embodiment, it is possible to predict the damage location and display the number of earthquake damages at the same time so that the extent of the damage can be grasped and the restoration work can be preferentially performed from the predicted location. It is possible to quickly restore pipes such as water and gas that are lifelines in the event of an earthquake.

(Fifth embodiment)
Next, a fifth embodiment will be described with reference to FIG.

  The fifth embodiment includes a simulated seismic intensity data input process 106 and a simulated seismic intensity data file 117 in addition to the first to fourth embodiments.

The simulated seismic intensity data input process 106 is realized by a process on a computer, and the simulated seismic intensity data file 117 is realized by a file on the computer. In the simulated seismic intensity data file 117, simulated seismic intensity data is registered. The simulated seismic intensity data input process reads the simulated seismic intensity data file 117 and passes the seismic intensity data to the acceleration estimation process 101 in the same manner as the seismic intensity data input process 101. Other processes are the same as those in the first to fourth embodiments.

  As described above, according to the present embodiment, not only damage prediction when an actual earthquake occurs but also countermeasures such as finding and replacing a pipeline requiring attention in normal times can be performed.

(Sixth embodiment)
Next, a sixth embodiment will be described with reference to FIG.

  In the sixth embodiment, the damage prediction process 103 receives inputs from the pipeline data file 111, the geological data file 112, and the acceleration estimation process 102, and combines them using an area as a key to create one table. Examples of bonding are given below. Assume that the following pipeline data file 111, geological data file 112, and acceleration estimation process 102 output.

<Example of estimated acceleration in each area>
Area = A00-00, acceleration = 120
Area = A00-01, acceleration = 120
Area = A00-02, acceleration = 210
<Example of pipeline data file 111 description>
Area = A00-00, Tube type = DCIP
Area = A00-00, Pipe type = CIP
Area = A00-01, Tube type = DCIP
Area = A00-02, Pipe type = CIP
Area = A00-02, Pipe type = SP
<Description example of geological data file 112>
Area = A00-00, Geology = Landfill Area = A00-01, Geology = Landfill Area = A00-02, Geology = Alluvium When the above is combined, the result is as follows.

<Combined example>
Area = A00-00, Acceleration = 120, Pipe type = DCIP, Geology = Landfill area = A00-00, Acceleration = 120, Pipe type = CIP, Geology = Landfill area = A00-01, Acceleration = 120, Pipe type = DCIP, geology = landfill area = A00-02, acceleration = 210, pipe type = CIP, geology = alluvium area = A00-02, acceleration = 210, pipe type = SP, geology = alluvium Damage prediction process 103 is The damage prediction is performed for each row of the combined data. That is, in the first line of the above example, the area A00-00 is a landfill, the acceleration is 120, and a DCIP conduit is laid. At this time, it is predicted whether or not this pipe has been damaged.

  The damage prediction has the following tree structure classification rules on the memory of the damage prediction process 103.

  An example of the classification rule is shown in FIG. In this example, the tree structure classification rule as described above is followed from the left for each row and row as a result of the combination. An example of a classification rule enclosed in <> is an attribute, and the condition surrounded by () indicates which of the tree structure should be followed by the attribute value. If you follow the conditions one after the other, you will eventually reach the predicted damage area surrounded by [].

  In the above classification rule example, first, there is <tube type> at the left end, so this attribute is examined.

Since the tube type is DCIP in the data in the first row of the combined example, the data is traced to (DCIP). Next, the acceleration is 120 and smaller than 210 (<210). Then, [Fitting] is reached. Therefore, it is predicted that the DCIP pipe laid in the area A00-00 is highly likely to be damaged in the joint. Prediction is executed for all the rows of data combined in this way, and this is output by the damage prediction result output device 50.

  As described above, according to the present embodiment, since the relationship between the earthquake and the damage is made into a tree structure, it can be easily created by a computer, the damage location can be predicted, and the number of earthquake damage can be predicted. The restoration work can be preferentially performed from the location, and this makes it possible to quickly restore the lifelines such as water and gas pipelines in the event of an earthquake.

(Seventh embodiment)
Next, a seventh embodiment will be described with reference to FIG.

  In the seventh embodiment, the damage prediction process 103 has damage prediction rules in an if-then rule format. An example is shown below.

<Example of classification rules>
if (tube type = CIP & geology = alluvium & acceleration <210)
then joint if (tube type = CIP & geology = alluvium &acceleration> = 210)
then tube if (tube type = CIP & geology = landfill)
then joint if (tube type = DCIP & acceleration <210)
then joint if (tube type = DCIP &acceleration> = 210)
then tube damage prediction process 103 sequentially tests the combined data described in the sixth embodiment for each line and line from the top of the classification rule. To do. Others are the same as in the sixth embodiment.

  As described above, according to the present embodiment, by having the damage prediction process 103 in an if-then rule format that is more familiar than a tree structure, human readability can be improved and the number of earthquake damages can be predicted. The restoration work can be preferentially performed from the predicted location, and thereby it is possible to quickly restore the pipelines such as water and gas that are lifelines in the event of an earthquake.

(Eighth embodiment)
Next, an eighth embodiment will be described with reference to FIG.

  In the eighth embodiment, the damage prediction process 103 has a damage prediction rule as a neural network. In the input layer of the neural network, there are neurons corresponding to attributes such as tube type and geology. An example of a neural network is shown below.

  An example of a neural network is shown in FIG. In FIG. 13, when an input is applied to a neuron corresponding to the attribute value indicated in the data, an output is output to the neuron in the output layer. The neuron with the output from the output layer is assumed as the predicted damage location. Others are the same as the embodiment of the base 6.

  Thus, according to the present embodiment, the damage prediction process 103 can be a prediction system that gradually learns damage prediction rules by using a neural network, and can predict the number of earthquake damages. Therefore, it is possible to perform restoration work with priority, and thereby it is possible to quickly restore the lifelines such as water and gas pipelines in the event of an earthquake.

(Ninth embodiment)
Next, a ninth embodiment will be described with reference to FIG.

  The ninth embodiment includes a damage analysis process 105, a seismic motion data file 114, a geological data file 115, and a seismic damage data file 116 in addition to the sixth to eighth embodiments. The damage analysis process 105 is realized as a process on a computer, and the earthquake motion data file 114, the geological data file 115, and the earthquake damage data file 116 are realized as files on the computer.

  The tree structure classification rules, if-then rules, and neural networks possessed by the damage prediction process 103 are generated from the earthquake motion data file 114, the geological data file 115, and the earthquake damage data file 116 by the damage analysis process 105. The seismic motion data file 114 corresponds to the estimated acceleration of each area of the sixth to eighth embodiments.

<Description example of earthquake motion data file 114>
Area = A00-00, acceleration = 120
Area = A00-01, acceleration = 120
Area = A00-02, acceleration = 210
The geological data file 115 corresponds to the geological data file 112.

<Description example of geological data file 115>
Area = A00-00, Geology = Landfill Area = A00-01, Geology = Landfill Area = A00-02, Geology = Alluvium Also, the earthquake damage data file 116 is the pipeline data file 111 plus the damage location. is there.

<Example of pipeline data file 111 description>
Area = A00-00, Pipe type = DCIP, Damaged part = Fitting Area = A00-00, Pipe type = CIP, Damaged part = Tube area = A00-01, Pipe type = DCIP, Damaged part = Fitting Area = A00- 02, Pipe type = CIP, Damaged part = Tube area = A00-02, Pipe type = SP, Damaged part = Tube When the three files are combined, the following data is obtained.

<Combination example>
Area = A00-00, acceleration = 120, geology = landfill, pipe type = DCIP, damage location = joint area = A00-00, acceleration = 120, geology = landfill, pipe type = CIP, damage location = tube area = A00-01, acceleration = 120, geology = landfill, pipe type = DCIP, damage location = joint area = A00-02, acceleration = 210, geology = alluvium, pipe type = CIP, damage location = tube area = A00-02, acceleration = 210, geology = alluvium, tube type = SP, damage location = tube In the ninth embodiment corresponding to the sixth embodiment, the damage analysis process 105 uses the id3 algorithm, A tree structure classification rule for classifying the data with respect to the damage location is generated and passed to the damage prediction process 103.

  In the ninth embodiment corresponding to the seventh embodiment, the damage analysis process 105 uses a list of conditions from the root node of the obtained tree structure classification rule to each leaf node as an if-then rule. Generate.

  In the ninth embodiment corresponding to the eighth embodiment, the damage analysis process 105 is based on the difference between the output of the neurons in the output layer corresponding to the input corresponding to the attributes of acceleration, geology, and tube type and the actual damage location. Learning neural networks using backpropagation. The neural network obtained as a result of learning is passed to the damage prediction process 103.

  As described above, according to the present embodiment, when an earthquake occurs, damage is predicted, and after the damage data is revealed immediately after the earthquake occurs, the damage prediction system can be refined using the data, and the number of earthquake damage can be predicted. Therefore, it is possible to preferentially perform restoration work from the predicted location, and thereby it is possible to quickly restore pipelines such as water and gas that are lifelines in the event of an earthquake.

The block diagram of the damage prediction system of the pipe line by the earthquake which concerns on this invention. The block diagram which shows the damage prediction system of the 1st Embodiment of this invention. The figure explaining the damage prediction object area in the embodiment. The flowchart which shows the method of calculating | requiring the seismic intensity of the area where the seismometer in the same embodiment is not installed. The block diagram which shows the damage prediction system of the 2nd Embodiment of this invention. The block diagram which shows the damage prediction system of the 3rd Embodiment of this invention. The block diagram which shows the damage prediction system of the 4th Embodiment of this invention. The block diagram which shows the damage prediction system of the 5th Embodiment of this invention. The block diagram which shows the damage prediction system of the 6th Embodiment of this invention. The figure explaining the classification | category rule of the tree structure in 6th Embodiment. The block diagram which shows the damage prediction system of the 7th Embodiment of this invention. The block diagram which shows the damage prediction system of the 8th Embodiment of this invention. The figure explaining the example which performs the damage prediction in the embodiment with a neural network. The block diagram which shows the damage prediction system of the 9th Embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Movie camera 20 Still image camera 30 Seismometer network 40 Seismometer 50 Damage prediction result output device 100 Damage prediction device 101 Acceleration estimation process 102 Acceleration data input process 103 Damage prediction process 104 Damage prediction process 105 Damage prediction process 106 Damage prediction Process 111 Pipeline data file 112 Geological data file 113 Image recording file 114 Earthquake motion data file 115 Geological data file 116 Earthquake damage data file 117 Simulated seismic intensity data file

Claims (4)

A damage prediction target area forming means for dividing a damage prediction target area to form a plurality of damage prediction target areas;
Damage prediction means for predicting damage for each area subject to damage prediction based on seismic intensity data, pipeline data and geological data of the earthquake that occurred,
A system for predicting damage to pipes caused by earthquakes. The pipeline damage prediction system according to claim 1, wherein the pipeline data is pipe type data. Divide the damage prediction target area to form multiple damage prediction target areas,
A method for predicting damage to pipes caused by an earthquake, wherein damage is predicted for each area subject to damage prediction based on seismic intensity data, pipe data and geological data of the earthquake that occurred. The pipe damage data prediction method according to claim 3, wherein the pipe data is pipe type data.

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