Traffic network disaster resistance key section screening method and device and electronic equipment

Abstract

The application relates to a traffic road network disaster resistance key road section screening method and device and electronic equipment, comprising the following steps: determining a target fault of a to-be-predicted area and a BPT condition occurrence probability and an equivalent annual occurrence rate of the target fault in a target prediction period, updating the equivalent annual occurrence probability to a seismic source model in combination with the seismic source model, calculating a time-related probability seismic risk spatial field, determining a function loss of each road section in the target highway segmented network, determining a road section topology importance of each road section, calculating a comprehensive criticality of each road section based on the function loss and the road section topology importance of each road section, and determining a key road section ranking or a key road section set of the to-be-predicted area. Thus, the problems of low earthquake risk assessment accuracy and deviation between key road section screening results and actual fault earthquake risk in the related art are solved, the reliability of earthquake risk assessment can be improved, and the key road section screening results can be ensured to be consistent with the actual fault earthquake risk.

Description

Technical Field

[0001] This application relates to the field of earthquake engineering and transportation network security screening technology, and in particular to a method, device and electronic equipment for screening critical road sections of transportation network disaster resilience. Background Technology

[0002] In related technologies, probabilistic seismic hazard analysis mostly adopts time-independent models, assuming that the earthquake occurrence rate is stable over time. Based on the annual occurrence rate of earthquake sources, magnitude-distance distribution, and ground motion prediction relationship, the intensity of ground motion under a given exceedance probability level is calculated, thereby conducting regional seismic hazard assessment.

[0003] However, the time-independent models in related technologies do not take into account the actual recurrence evolution of faults, making it difficult to depict the actual recurrence stage of active faults with historical recurrence data after the most recent characteristic earthquake event. They also fail to reflect the characteristics of the hazard evolution of faults as they approach recurrence, resulting in low accuracy of earthquake risk assessment, difficulty in identifying the real disaster risks at different times, and discrepancies between the screening results of key road sections and the actual earthquake risk of faults, which urgently need to be addressed. Summary of the Invention

[0004] This application provides a method, device, and electronic equipment for screening key road sections with disaster resilience in transportation networks, in order to solve the problems of low accuracy of earthquake risk assessment and deviation between the screening results of key road sections and the actual earthquake risk of faults in related technologies. It can improve the reliability of earthquake risk assessment and ensure that the screening results of key road sections are consistent with the actual earthquake risk of faults.

[0005] The first aspect of this application provides a method for screening critical road sections with disaster resilience in transportation networks, including the following steps: Determine the fault recurrence parameters, earthquake source model, target prediction period, and target highway segmentation network for the area to be predicted; Based on fault recurrence parameters, the Brownian Passage Time (BPT) conditional occurrence probability and equivalent annual occurrence rate of the target fault within the target prediction period are determined. Based on the equivalent annual occurrence rate and the seismic source model, the seismic source model is updated. Based on the updated seismic source model, the time-related probabilistic seismic hazard spatial field is calculated. Based on the time-correlated probabilistic seismic hazard spatial field, the functional loss of each road segment in the target highway segment network is determined, and an unordered node pair is constructed according to the control nodes in the target highway segment network. Based on multiple candidate acyclic paths of the unordered node pair, the topological importance of each road segment in the target highway segment network is determined. The comprehensive criticality of each road segment is calculated based on the functional loss and topological importance of each road segment, and the critical segment ranking or set of critical road segments in the area to be predicted is determined according to the comprehensive criticality of each road segment.

[0006] Optionally, in some embodiments, the tomographic recurrence parameters include the average recurrence interval, the time of the most recent characteristic event, a non-periodic parameter, and the recurrence interval. Based on the tomographic recurrence parameters, the probability of occurrence of the target fault and the equivalent annual incidence of BPT conditions within the target prediction period are determined, including: Based on fault recurrence parameters, target faults with average recurrence interval and the time of the most recent characteristic event are identified. The elapsed time of the target fault is calculated based on the start time of the target prediction period and the time of the most recent characteristic event. The recurrence interval probability density function is constructed based on the average recurrence interval, non-periodic parameter, and recurrence interval of the target fault. The cumulative distribution function of the target fault is determined based on the recurrence interval probability density function. The probability of BPT conditional occurrence of the target fault during the target prediction period is calculated based on the target prediction period, the elapsed time of the target fault, and the cumulative distribution function of the target fault. The equivalent annual incidence rate of the target fault during the target prediction period is then calculated based on the probability of BPT conditional occurrence.

[0007] Optionally, in some embodiments, the earthquake source model is updated based on the equivalent annual occurrence rate and the earthquake source model, including: The maximum magnitude and target magnitude range of the target fault are determined, and the incremental magnitude-frequency distribution of the target magnitude range is constructed based on the equivalent annual occurrence rate. Based on the incremental magnitude-frequency distribution of the target magnitude range, the updated target fault is obtained. Based on the updated target fault, the unupdated fault in the seismic source model, and the background seismic source, the seismic source model is updated to obtain the updated seismic source model.

[0008] Optionally, in some embodiments, the functional loss of each segment in the target highway segment network is determined based on the time-correlated probabilistic seismic hazard spatial field, including: Map each road segment in the target highway segment network to the time-related probabilistic seismic hazard spatial field to a unified coordinate reference system, determine the representative point of each road segment, and establish a nearest neighbor index on the time-related probabilistic seismic hazard spatial field based on the representative point of each road segment. Based on the preset matching rules, the peak ground acceleration in the time-related probability seismic hazard spatial field is assigned to the corresponding road segments in the target highway segment network according to the nearest neighbor index. At the same time, the matching relationship between the hazard field points and each road segment in the target highway segment network is recorded to obtain the first target highway segment network. Based on the preset road segment type, the fourth-level vulnerability parameters of each road segment in the first target highway segment network are determined, and the functional loss of each road segment in the target highway segment network is calculated based on the fourth-level vulnerability parameters of each road segment.

[0009] Optionally, in some embodiments, the functional loss of each segment in the target highway segment network is calculated based on the four-level vulnerability parameters of each segment, including: The probability of exceeding the level 4 damage for each road segment is calculated based on the level 4 vulnerability parameters of each road segment, and the probability of the level 5 damage state for each road segment is determined based on the probability of exceeding the level 4 damage for each road segment. The probability of the level 5 damage state includes the probability of no damage state, the probability of slight damage state, the probability of moderate damage state, the probability of severe damage state, and the probability of complete damage state. The severe damage state corresponding to the severe damage state probability and the complete damage state corresponding to the complete damage state probability are combined into the functional failure state to obtain the four-level functional state probability for each road segment. The four-level functional state probability includes the probability of no damage, the probability of slight damage, the probability of moderate damage, and the probability of functional failure. The expected speed retention rate and expected capacity retention rate of each road segment are calculated based on the probability of the fourth functional state of each road segment. The expected speed retention rate and expected capacity retention rate of each road segment are aggregated to obtain the functional retention index of each road segment. The functional loss of each road segment is calculated based on the functional retention index of each road segment.

[0010] Optionally, in some embodiments, determining the topological importance of each road segment in the target highway segment network based on multiple candidate acyclic paths of unordered node pairs includes: The path weight is the length of the continuous channel between adjacent control nodes. Based on the path weight, multiple candidate acyclic paths for unordered node pairs are searched. The unordered node pairs are divided into reachable and unreachable node pairs according to the multiple candidate acyclic paths. Calculate the segment topological importance of multiple candidate acyclic paths between reachable node pairs, and set the segment topological importance of multiple candidate acyclic paths between unreachable node pairs to zero.

[0011] Optionally, in some embodiments, the fault recurrence parameter includes a unique identifier for each road segment, and the critical segment ranking or set of critical road segments in the area to be predicted is determined based on the comprehensive criticality of each road segment, including: Based on the comprehensive criticality of each road segment, each road segment is sorted in descending order to obtain the critical road segment ranking of the area to be predicted, and a preset proportion of road segments are selected from the critical road segment ranking to form the critical road segment set of the critical road segment ranking of the area to be predicted. If multiple road segments have the same overall criticality, then these road segments with the same overall criticality will be sorted in ascending order based on their unique identifiers.

[0012] A second aspect of this application provides a screening device for critical road sections with disaster resilience in transportation networks, comprising: The acquisition module is used to determine the fault recurrence parameters, earthquake source model, target prediction period, and target highway segment network of the area to be predicted; The first calculation module is used to determine the probability of occurrence of the target fault and the equivalent annual occurrence rate of the BPT condition of the target fault in the target prediction period based on the fault recurrence parameter, and to update the earthquake source model based on the equivalent annual occurrence rate and the earthquake source model, and to calculate the time-related probability seismic hazard spatial field based on the updated earthquake source model. The second calculation module is used to determine the functional loss of each road segment in the target highway segment network based on the time-related probability seismic hazard spatial field, and to construct disordered node pairs based on the control nodes in the target highway segment network. Based on the multiple candidate acyclic paths of the disordered node pairs, the module determines the topological importance of each road segment in the target highway segment network. The filtering module is used to calculate the comprehensive criticality of each road segment based on the functional loss and topological importance of each road segment, and to determine the critical segment ranking or set of critical road segments in the area to be predicted based on the comprehensive criticality of each road segment.

[0013] Optionally, in some embodiments, the tomographic recurrence parameters include the average recurrence interval, the time of the most recent characteristic event, a non-periodic parameter, and the recurrence interval. The first calculation module is specifically used for: Based on fault recurrence parameters, target faults with average recurrence interval and the time of the most recent characteristic event are identified. The elapsed time of the target fault is calculated based on the start time of the target prediction period and the time of the most recent characteristic event. The recurrence interval probability density function is constructed based on the average recurrence interval, non-periodic parameter, and recurrence interval of the target fault. The cumulative distribution function of the target fault is determined based on the recurrence interval probability density function. The probability of BPT conditional occurrence of the target fault during the target prediction period is calculated based on the target prediction period, the elapsed time of the target fault, and the cumulative distribution function of the target fault. The equivalent annual incidence rate of the target fault during the target prediction period is then calculated based on the probability of BPT conditional occurrence.

[0014] Optionally, in some embodiments, the first computing module is specifically used for: The maximum magnitude and target magnitude range of the target fault are determined, and the incremental magnitude-frequency distribution of the target magnitude range is constructed based on the equivalent annual occurrence rate. Based on the incremental magnitude-frequency distribution of the target magnitude range, the updated target fault is obtained. Based on the updated target fault, the unupdated fault in the seismic source model, and the background seismic source, the seismic source model is updated to obtain the updated seismic source model.

[0015] Optionally, in some embodiments, the second computing module is specifically used for: Map each road segment in the target highway segment network to the time-related probabilistic seismic hazard spatial field to a unified coordinate reference system, determine the representative point of each road segment, and establish a nearest neighbor index on the time-related probabilistic seismic hazard spatial field based on the representative point of each road segment. Based on the preset matching rules, the peak ground acceleration in the time-related probability seismic hazard spatial field is assigned to the corresponding road segments in the target highway segment network according to the nearest neighbor index. At the same time, the matching relationship between the hazard field points and each road segment in the target highway segment network is recorded to obtain the first target highway segment network. Based on the preset road segment type, the fourth-level vulnerability parameters of each road segment in the first target highway segment network are determined, and the functional loss of each road segment in the target highway segment network is calculated based on the fourth-level vulnerability parameters of each road segment.

[0016] Optionally, in some embodiments, the second computing module is specifically used for: The probability of exceeding the level 4 damage for each road segment is calculated based on the level 4 vulnerability parameters of each road segment, and the probability of the level 5 damage state for each road segment is determined based on the probability of exceeding the level 4 damage for each road segment. The probability of the level 5 damage state includes the probability of no damage state, the probability of slight damage state, the probability of moderate damage state, the probability of severe damage state, and the probability of complete damage state. The severe damage state corresponding to the severe damage state probability and the complete damage state corresponding to the complete damage state probability are combined into the functional failure state to obtain the four-level functional state probability for each road segment. The four-level functional state probability includes the probability of no damage, the probability of slight damage, the probability of moderate damage, and the probability of functional failure. The expected speed retention rate and expected capacity retention rate of each road segment are calculated based on the probability of the fourth functional state of each road segment. The expected speed retention rate and expected capacity retention rate of each road segment are aggregated to obtain the functional retention index of each road segment. The functional loss of each road segment is calculated based on the functional retention index of each road segment.

[0017] Optionally, in some embodiments, the second computing module is specifically used for: The path weight is the length of the continuous channel between adjacent control nodes. Based on the path weight, multiple candidate acyclic paths for unordered node pairs are searched. The unordered node pairs are divided into reachable and unreachable node pairs according to the multiple candidate acyclic paths. Calculate the segment topological importance of multiple candidate acyclic paths between reachable node pairs, and set the segment topological importance of multiple candidate acyclic paths between unreachable node pairs to zero.

[0018] Optionally, in some embodiments, the filtering module is specifically used for: Based on the comprehensive criticality of each road segment, each road segment is sorted in descending order to obtain the critical road segment ranking of the area to be predicted, and a preset proportion of road segments are selected from the critical road segment ranking to form the critical road segment set of the critical road segment ranking of the area to be predicted. If multiple road segments have the same overall criticality, then these road segments with the same overall criticality will be sorted in ascending order based on their unique identifiers.

[0019] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the method for screening critical road sections of a transportation network to improve disaster resilience as described in the first aspect embodiment.

[0020] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the method for screening critical road segments of a transportation network to enhance its disaster resilience, as described in the first aspect embodiment.

[0021] Therefore, the embodiments of this application have at least the following beneficial effects: (1) The embodiments of this application use continuous calculation of the probability of occurrence of BPT conditions, the equivalent annual occurrence rate of the target prediction period and the high magnitude interval update to enable the target fault with recurrence data constraints to participate in the probabilistic seismic hazard calculation according to its elapsed time, and provide a clear backtracking path for faults with missing parameters.

[0022] (2) The embodiments of this application convert the dangerous spatial field into segment-by-segment functional loss by using unified road segment midpoint matching, component classification, four-level vulnerability and function retention index calculation, so that different component types can be screened at the same road segment scale.

[0023] (3) In this application embodiment, by controlling the multiple candidate acyclic paths of the node pair and the accumulation of the contribution of the road segment, the topological importance of the road segment is obtained, which is different from the single shortest path, and together with the functional loss, the potential high loss channel and structural bottleneck information are retained.

[0024] (4) The embodiments of this application form a repeatable key road segment ranking or key road segment set by normalization within the scenario, zero range processing and deterministic ranking with the same score, which facilitates comparison of the ranking and set changes under the BPT update scenario and the Poisson baseline scenario.

[0025] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0026] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for screening critical road sections with disaster resilience in a transportation network, according to an embodiment of this application. Figure 2 This is a flowchart illustrating a method for screening critical road sections with disaster resilience in a transportation network according to an embodiment of this application; Figure 3 This is a BPT fault occurrence rate update and PSHA data flow graph provided according to an embodiment of this application; Figure 4 This is a flowchart illustrating a method for processing data on hazardous space fields and road segment functional losses according to an embodiment of this application; Figure 5 This is a flowchart illustrating a method for determining the topological importance of road segments and ranking critical road segments according to an embodiment of this application; Figure 6 This is a block diagram of a screening device for critical road sections of a transportation network to assess disaster resilience, according to an embodiment of this application. Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0027] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0028] The following describes a method, apparatus, and electronic device for screening critical road segments in the disaster resilience of transportation networks according to embodiments of this application, with reference to the accompanying drawings. Addressing the issues of low accuracy in earthquake risk assessment and discrepancies between the screening results of critical road segments and the actual seismic risk of faults mentioned in the background art, this application provides a method for screening critical road segments in the disaster resilience of transportation networks. This method can determine the target fault in the area to be predicted and the probability of occurrence of BPT conditions and the equivalent annual occurrence rate of the target fault within the target prediction period. It updates the earthquake source model based on the equivalent annual occurrence rate and the earthquake source model, and calculates the time-related probability seismic hazard spatial field, thereby determining the functional loss of each road segment in the target highway segment network. Furthermore, it constructs unordered node pairs based on the control nodes in the target highway segment network to determine the topological importance of each road segment in the target highway segment network. Finally, it calculates the comprehensive criticality of each road segment based on its functional loss and topological importance, thereby determining the ranking or set of critical road segments in the area to be predicted. This solves the problems of low accuracy in earthquake risk assessment and discrepancies between the screening results of key road sections and the actual earthquake risk of faults in related technologies, thereby improving the reliability of earthquake risk assessment and ensuring that the screening results of key road sections are consistent with the actual earthquake risk of faults.

[0029] Specifically, Figure 1 A flowchart of a method for screening critical road sections with disaster resilience of transportation networks provided in this application embodiment.

[0030] like Figure 1 As shown, the method for screening critical road sections with disaster resilience in this transportation network includes the following steps: In step S101, the fault recurrence parameters, earthquake source model, target prediction period, and target highway segment network of the area to be predicted are determined.

[0031] Specifically, this embodiment first reads the fault recurrence parameters of the area to be predicted, including: fault identifier, average recurrence interval, time of the most recent characteristic event, non-periodic parameters, maximum magnitude, and magnitude-frequency distribution. It then reads the seismic source model composed of fault sources and background sources, the target prediction start time and duration, and the unique identifier, geometry, length, bridge or tunnel labels, control nodes, and the highway segment network to which the target highway segment network belongs. Secondly, this embodiment verifies the obtained data, including the uniqueness of fault identifiers, the positive target prediction time period, the uniqueness of road segment identifiers, the validity of road segment geometry, and the resolvability of coordinate reference information. Finally, this embodiment obtains the verified fault recurrence parameters, seismic source model, target prediction time period, and the target highway segment network including highway segments and control nodes for the area to be predicted. This completes the data acquisition and verification process.

[0032] In step S102, based on the fault recurrence parameters, the probability of occurrence of the target fault and the equivalent annual occurrence rate of the BPT condition of the target fault within the target prediction period are determined. Based on the equivalent annual occurrence rate and the earthquake source model, the earthquake source model is updated. Based on the updated earthquake source model, the time-related probability seismic hazard spatial field is calculated.

[0033] In some embodiments, the fault recurrence parameters include the average recurrence interval, the time of the most recent characteristic event, a non-periodic parameter, and the recurrence interval. Based on the fault recurrence parameters, the target fault and the probability of BPT conditional occurrence and the equivalent annual incidence rate of the target fault within the target prediction period are determined, including: identifying a target fault with an average recurrence interval and the time of the most recent characteristic event based on the fault recurrence parameters; calculating the elapsed time of the target fault based on the start time of the target prediction period and the time of the most recent characteristic event, and constructing a recurrence interval probability density function based on the average recurrence interval, the non-periodic parameter, and the recurrence interval of the target fault, and determining the cumulative distribution function of the target fault based on the recurrence interval probability density function; calculating the probability of BPT conditional occurrence of the target fault within the target prediction period based on the target prediction period, the elapsed time of the target fault, and the cumulative distribution function of the target fault, and calculating the equivalent annual incidence rate of the target fault within the target prediction period based on the probability of BPT conditional occurrence.

[0034] In some embodiments, the earthquake source model is updated based on the equivalent annual occurrence rate and the earthquake source model, including: determining the maximum magnitude and target magnitude interval of the target fault, constructing the incremental magnitude-frequency distribution of the target magnitude interval based on the equivalent annual occurrence rate, and obtaining the updated target fault based on the incremental magnitude-frequency distribution of the target magnitude interval; and updating the earthquake source model based on the updated target fault, the unupdated fault in the earthquake source model, and the background earthquake source to obtain the updated earthquake source model.

[0035] Specifically, in this embodiment, for a target fault having an average recurrence interval and the time of the most recent characteristic event, the difference between the start time of the corresponding target prediction period and the time of the most recent characteristic event is calculated, and this difference is used as the elapsed time of the target fault. Based on the average recurrence interval, the non-periodic parameter, and the recurrence interval, a recurrence interval probability density function of the Brownian travel time model is constructed according to a first calculation formula. This function converts the average recurrence interval and the non-periodic parameter into a characteristic event recurrence interval distribution, the cumulative distribution function of which is denoted as... The first calculation formula is: ; in, Recurrence interval The probability density; A positive recurrence interval; The average recurrence interval and ; It is a non-periodic parameter and ; Pi; It is a natural exponential function.

[0036] Furthermore, the elapsed time before the known target fault... If the next characteristic event has not yet occurred, the embodiments of this application can calculate the probability of BPT occurrence of the target fault within the target prediction period according to the cumulative distribution function of the probability density function, the elapsed time, and the target prediction period, using a second calculation formula. The second calculation formula is as follows: ; in, This represents the probability of BPT occurring. The cumulative distribution function of BPT; For the time of departure and ; For the target prediction period; up to the time of death The probability of survival before the next characteristic event occurs. It should be understood that during the calculation process, it is required that... and Furthermore, the probability of the BPT condition should be between 0 and 1; any result exceeding this range is considered abnormal.

[0037] Furthermore, to enable the BPT condition occurrence probability to be integrated into the probabilistic seismic hazard analysis input expressed as an annual occurrence rate, this embodiment of the application can refer to the method of ensuring that the probability of at least one characteristic event occurring within the target prediction period is the same, and calculate the equivalent annual occurrence rate of the target fault within the target prediction period according to the third calculation formula, thereby completing the probability matching. The third calculation formula is: ; in, This represents the equivalent annual incidence rate of the target fault during the target prediction period, expressed as negative one-years. It represents the natural logarithm.

[0038] It should be understood that during the calculation process, it is required that... and This conversion makes the probability of at least one characteristic event occurring within the target prediction period, calculated based on the equivalent annual occurrence rate, equal to... Consistent; when the probability of BPT occurring is 0, the equivalent annual occurrence rate is 0; when the numerical result reaches 1, the probability is limited to a preset upper limit of a value that is strictly less than 1 and the boundary processing information is recorded.

[0039] For example, in this embodiment of the application, 2026 is used as the prediction start year and 50 years is used as the target prediction period. The non-periodic parameter is 0.37, and the above values ​​are all adjustable deterministic model settings. The probability of BPT conditions occurring in the output target fault should be equal to the equivalent annual occurrence rate.

[0040] Furthermore, embodiments of this application can set a target magnitude interval (i.e., a high-magnitude interval) based on the maximum magnitude of the target fault, and update the target magnitude interval using the equivalent annual occurrence rate calculated above. For faults lacking available recurrence parameters, the Poisson time-independent model is retained. Specifically, the original time-independent magnitude-frequency distribution occurrence rate below the target magnitude interval is retained, and an incremental magnitude-frequency distribution of the target magnitude interval is constructed based on the equivalent annual occurrence rate. The target fault is then updated based on the incremental magnitude-frequency distribution of the target magnitude interval to obtain the updated target fault. Simultaneously, for faults lacking available mean recurrence intervals or the time of the most recent characteristic event, their Poisson time-independent occurrence models are retained. For example, embodiments of this application can retain the original time-independent magnitude-frequency distribution occurrence rate below the target magnitude interval. The original Poisson truncated Gutenberg-Richter occurrence rate was used, and the central magnitude was taken as... An incremental magnitude-frequency distribution with a width of 0.5 represents the target magnitude interval. In cases where an analytical mean recurrence interval is lacking, or the time of the most recent characteristic event, elapsed time is not positive, survival probability does not meet calculation conditions, or the target magnitude interval does not meet the discretization requirements, the corresponding fault is not replaced; instead, its Poisson model is retained and the regression status is recorded.

[0041] Furthermore, this application embodiment requires Poisson benchmark comparison and verification. Specifically, this application embodiment needs to construct a Poisson benchmark scenario with the same source region, fault geometry, background source, field point, site conditions, ground motion model, and highway segmentation network as the specific implementation process described above, and perform BPT conditional probability update for the target fault. Next, for the BPT updated scenario and the Poisson benchmark scenario, respectively, the high-magnitude interval occurrence rate of the target fault is updated using the equivalent annual occurrence rate, and the time-correlated probability seismic hazard spatial field is calculated based on the updated seismic source model, and the key road segment ranking or key road segment set is output. The rank correlation coefficient of the key road segment ranking and the Jaccard coefficient of the key road segment set under the BPT updated scenario and the Poisson benchmark scenario are calculated respectively. Highway segments entering or leaving the key road segment set are counted and paired one by one according to the unique identifier of the same road segment. Finally, the changes in the ranking or set of key road segments caused by the change in the seismic source model are output.

[0042] Furthermore, in this embodiment, the annual exceedance rate of ground motion intensity exceeding a preset threshold can be calculated based on the magnitude-distance occurrence rate and ground motion condition exceedance probability of each earthquake source, and this annual exceedance rate can be converted into ground motion intensity corresponding to the target condition exceedance probability. Next, the updated target fault, the unupdated fault in the earthquake source model, and the background earthquake source are combined into an updated earthquake source model, and the annual exceedance rate of ground motion intensity exceeding the preset threshold is calculated according to the fourth calculation formula. The fourth calculation formula is: ; in, Earthquake intensity Exceeding the preset threshold The non-negative annual excess rate, in units of negative one-year; For earthquake source indexing; Magnitude; The distance from the epicenter to the field point; Earthquake source Annual occurrence density in terms of magnitude and distance; For a given earthquake source, magnitude, and distance, the ground motion intensity exceeds a preset threshold. The conditional probability; This represents the magnitude integral within the permissible magnitude range and distance range of the corresponding earthquake source; This represents the distance integral over the allowable magnitude and distance range of the corresponding earthquake source. Specifically, in this embodiment, by summing the values ​​for each earthquake source and integrating over the magnitude and distance range, the updated earthquake source activity rate and the probability of exceeding the seismic ground motion condition can be combined into a hazard curve.

[0043] Furthermore, in this embodiment of the application, the annual exceedance rate can be converted into a time-period exceedance probability according to the fifth calculation formula. The fifth calculation formula is: ; in, For time period Inland seismic intensity Exceeding the threshold The probability of; The period for calculating positive risk; Earthquake intensity Exceeding the preset threshold Non-negative annual exceedance rate; This represents the natural exponential function. Where, when... and hour, It is between 0 and 1.

[0044] Furthermore, embodiments of this application can inversely calculate a preset threshold for the probability of exceeding a given target condition within the coverage area of ​​the hazard curve. This process yields the peak ground acceleration (PGA) for each field point. If the target probability exceeds the coverage of the hazard curve, the seismic intensity discretization range is expanded and recalculated, or the out-of-bounds state is output without generating unfounded field point values. The final output is a time-correlated probabilistic seismic hazard spatial field (i.e., the PGA hazard spatial field) with field point identifiers and coordinates. The target condition exceedance probability can be 9.516% within 50 years, and this value can be adjusted according to the screening task.

[0045] In step S103, based on the time-correlated probability seismic hazard spatial field, the functional loss of each road segment in the target highway segment network is determined, and an unordered node pair is constructed according to the control nodes in the target highway segment network. Based on the multiple candidate acyclic paths of the unordered node pair, the topological importance of each road segment in the target highway segment network is determined.

[0046] In some embodiments, determining the functional loss of each road segment in the target highway segment network based on the time-correlated probabilistic seismic hazard spatial field includes: mapping each road segment in the target highway segment network to the time-correlated probabilistic seismic hazard spatial field to a unified coordinate reference system, determining a representative point for each road segment, and establishing a nearest neighbor index on the time-correlated probabilistic seismic hazard spatial field based on the representative point of each road segment; assigning the peak ground acceleration in the time-correlated probabilistic seismic hazard spatial field to the corresponding road segment in the target highway segment network according to the nearest neighbor index based on a preset matching rule, while recording the matching relationship between the hazard field point and each road segment in the target highway segment network to obtain a first target highway segment network; determining the fourth-level vulnerability parameters of each road segment in the first target highway segment network based on a preset road segment type, and calculating the functional loss of each road segment in the target highway segment network based on the fourth-level vulnerability parameters of each road segment.

[0047] In some embodiments, the functional loss of each road segment in the target highway segment network is calculated based on the four-level vulnerability parameters of each road segment. This includes: calculating the level four damage exceedance probability of each road segment based on the level four vulnerability parameters of each road segment, and determining the level five damage state probability of each road segment based on the level four damage exceedance probability of each road segment. The level five damage state probabilities include the probability of no damage state, the probability of slight damage state, the probability of moderate damage state, the probability of severe damage state, and the probability of complete damage state. The severe damage state corresponding to the severe damage state probability and the complete damage state corresponding to the complete damage state probability are merged into the functional failure state to obtain the level four functional state probabilities of each road segment. The level four functional state probabilities include the probability of no damage, the probability of slight damage, the probability of moderate damage, and the probability of functional failure. The expected speed retention rate and the expected capacity retention rate of each road segment are calculated based on the level four functional state probabilities of each road segment. The expected speed retention rate and the expected capacity retention rate of each road segment are aggregated to obtain the functional retention index of each road segment. The functional loss of each road segment is calculated based on the functional retention index of each road segment.

[0048] In some embodiments, determining the topological importance of each road segment in the target highway segment network based on multiple candidate acyclic paths of unordered node pairs includes: using the continuous channel length between adjacent control nodes as the path weight, searching for multiple candidate acyclic paths of unordered node pairs based on the path weight, dividing the unordered node pairs into reachable and unreachable node pairs according to the multiple candidate acyclic paths of unordered node pairs; calculating the topological importance of multiple candidate acyclic paths between reachable node pairs, and setting the topological importance of multiple candidate acyclic paths between unreachable node pairs to zero.

[0049] Specifically, in this embodiment of the application, the spatial field of time-correlated probabilistic seismic hazard for each road segment in the target highway segment network can be transformed to a unified coordinate reference system, and a representative point of each road segment can be extracted. A nearest neighbor index can then be established on the coordinates of the points in the spatial field of time-correlated probabilistic seismic hazard. The representative point of each road segment can be the geometric midpoint located at a geometrically normalized distance of 0.5 along that road segment.

[0050] Furthermore, in this embodiment, the peak ground acceleration in the PGA of the time-correlated probabilistic seismic hazard spatial field point closest to the representative point of the road segment can be matched to the bridge segment, tunnel segment, and ordinary road segment in the target highway segment network according to the sixth calculation formula. The matching relationship between the hazard field point and each segment in the target highway segment network is recorded, and finally, a first target highway segment network with segment-by-segment PGA and matching traceability information is output. The sixth calculation formula is: ; in, For the first The seismic intensity input for each road segment can be PGA; For highway section indexing; For the nearest neighbor matching function of the discrete hazard space field; For the first Each road segment represents the coordinates of a point; Take the value of the hazardous site point that is closest to this coordinate.

[0051] It should be understood that if multiple field points have the same distance, they are selected in ascending order based on their unique field point identifiers; if no valid field point exists, subsequent calculations for that road segment are stopped and an abnormal status is output. During the matching process, it is necessary to record the field point identifier, field point coordinates, and matching distance, and verify that the number of output road segments matches the input, that the input and output sets of unique road segment identifiers are the same, and that each road segment is matched only once. It is a non-negative finite value.

[0052] Furthermore, this embodiment of the application can determine the four-level vulnerability parameters (i.e., four-level vulnerability relationships) of each road segment in the first target highway segment network based on a preset road segment type (i.e., road segment component type), and calculate the discrete damage state probability, speed retention rate, and capacity retention rate of each road segment in the target highway segment network according to the four-level vulnerability parameters of each road segment. It also calculates the function retention index of each road segment based on the speed retention rate and capacity retention rate, and determines the function loss of each road segment based on the function retention index. The final output state should be: outputting the damage state probability, expected speed retention rate, expected capacity retention rate, function retention index, and function loss segment by segment. Specifically, this embodiment of the application can select the corresponding four-level vulnerability parameters for each road segment according to the classification of bridges, tunnels, or ordinary roads, and calculate the four-level damage exceedance probability of each road segment using peak ground acceleration, median peak ground acceleration, and logarithmic standard deviation according to the seventh calculation formula. The four-level damage exceedance probability includes: exceedance probability of minor damage, exceedance probability of moderate damage, exceedance probability of severe damage, and exceedance probability of complete damage. The seventh calculation formula is: ; in, The condition is damaged. For the first A damage level threshold, where Numbers 1 to 4 correspond to minor, moderate, severe, and complete injury, respectively. The ground motion intensity is for non-negative road sections; It is the standard normal cumulative distribution function; To reach or exceed The median intensity of the ground motion and ; The corresponding logarithmic standard deviation and ; It represents the natural logarithm.

[0053] It should be understood that when At that time, according to The limit approaching 0 sets the probability of each damage to 0; when The probability calculated using the formula should be between 0 and 1. Bridges, tunnels, and ordinary roads should each use their respective corresponding formulas. and Furthermore, the four exceedance probability curves are constrained to increase with damage severity rather than incrementally, thereby obtaining physically compatible damage exceedance probabilities.

[0054] Furthermore, in this embodiment, the non-damage state and the mutual exclusion probabilities of the four damage states can be determined by the difference in the exceedance probabilities of adjacent damage levels. During functional mapping, severe damage and complete damage are merged into functional failure state. The probability of each functional state is weighted and summed with the corresponding velocity retention coefficient and capacity retention coefficient to obtain the velocity retention rate and capacity retention rate. The weighted harmonic average of the velocity retention rate and capacity retention rate is calculated as the functional retention index, and the value after subtracting the functional retention index is used as the energy loss.

[0055] Furthermore, embodiments of this application can make the first The probability of exceeding level 4 damage for this road segment is denoted as: to The probability of level 5 damage state for each road segment is calculated according to the eighth calculation formula, where the probabilities of level 5 damage states are mutually exclusive. The eighth calculation formula is as follows: ; in, to The order is number 1 The probability that a road segment is in a state of no damage, slight damage, moderate damage, severe damage, and complete damage; For the first The number of road sections reached or exceeded the number of The probability of each damage level.

[0056] It should be understood that, upon detecting a curve intersection, the embodiments of this application should limit the exceedance probability of the next level to no greater than that of the previous level, according to the damage level order. The resulting state probabilities should satisfy... The values ​​are not less than 0 and their sum is 1. Furthermore, the tolerance deviations caused by floating-point operations can be handled through boundary truncation and normalization.

[0057] Furthermore, in the functional mapping stage, embodiments of this application can merge severe damage and complete damage into a functional failure state, and calculate the probability of the fourth-level functional state for each road segment according to the ninth calculation formula. The ninth calculation formula is as follows: ; in, For the first The section is in the first The probability of each functional state, where... Take values ​​from 0 to 3; , and These represent the probabilities of functional states of no damage, slight damage, and moderate damage, respectively. It represents the probability of functional failure state and includes both severe damage and complete damage.

[0058] It should be understood that the above-mentioned... All values ​​are not less than 0 and their sum is 1. This merging does not change the level 4 damage exceedance probability and level 5 damage state probability of each road segment, and is only used for subsequent function retention rate mapping.

[0059] Furthermore, in this embodiment of the application, the desired speed retention rate and desired capacity retention rate of each road segment can be calculated according to the tenth calculation formula. The tenth calculation formula is: ; in, For the first The expected speed maintenance rate for each road segment, where the superscript... Indicates speed; For the first The expected capacity retention rate of each road segment, where the superscript Indicates capacity; For the first The section is in the first The probability of each functional state; For the first Speed ​​retention coefficients corresponding to each functional state; For the first The capacity retention coefficients corresponding to each functional state. Among them, It can take values ​​from 0 to 3.

[0060] It should be understood that all of the above retention coefficients should be between 0 and 1, therefore and It should also be between 0 and 1. This application's embodiments, by weighting the retention coefficients of each functional state according to state probability, can convert discrete functional states into two types of expected retention rates at the road segment scale. For example, the speed retention coefficient can be taken as... The capacity retention factor can be taken as... .

[0061] Furthermore, in this embodiment of the application, the expected speed retention rate and expected capacity retention rate of each road segment can be aggregated according to the eleventh calculation formula to obtain the functional loss. The eleventh calculation formula is as follows: ; in, For the first Functional maintenance indicators for each road segment; For the first Functional loss of each road section; Weighted by the expected speed retention rate and Among them, the expected speed retention rate is weighted. Can be taken .

[0062] It should be understood that when or When the value is 0, the embodiments of this application will Set to 0; otherwise, use a weighted harmonic average calculation and verify each road segment. , and Furthermore, the aforementioned coefficients and weights can be adjusted according to the component function mapping requirements corresponding to the preset road segment type.

[0063] Furthermore, embodiments of this application can determine the topological importance of each candidate acyclic path based on multiple candidate acyclic paths between control node pairs and the participation of highway segments in these paths. Specifically, embodiments of this application can construct control nodes from locations such as entrance interchanges, hub interchanges, boundary entrances and exits, and independent mainline toll stations. The length of continuous channels (i.e., continuous links) between adjacent control nodes is used as the path weight. All unordered node pairs are constructed for control nodes that meet the path analysis conditions. Based on the path weights, a preset number of candidate acyclic paths are searched between each unordered node pair. The unordered node pairs are then divided into reachable and unreachable pairs based on the multiple candidate acyclic paths available. Unreachable pairs are those where the two nodes belong to different connected components or where the path search result is empty. Embodiments of this application can use Yen path search to obtain up to 5 candidate acyclic paths for each unordered node pair.

[0064] Furthermore, for unreachable node pairs, this embodiment can include them as having zero contribution in the normalized denominator of all theoretical node pairs; for reachable node pairs, this embodiment can assign a unit weight to each candidate acyclic path between the node pairs, and sum the path weights containing highway segments and divide by the total number of theoretical node pairs to obtain the multi-path edge betweenness of the highway segments. The multi-path edge betweenness is then normalized using the largest multi-path edge betweenness among all highway segments, thereby obtaining the segment topological importance of the multiple candidate acyclic paths between reachable node pairs. The segment topological importance can be represented by the multi-path edge betweenness, and this embodiment can calculate the twelfth calculation formula according to the... The betweenness number of multiple paths in each road segment. The twelfth formula is: ; in, For the first The multi-path edge betweenness of a road segment; For the total number of theoretically disordered control node pairs and The outer layer sums and traverses all unordered control node pairs. ; For control nodes and The set of candidate acyclic paths between; for One of the paths; For path The set of road segments included; This represents the number of candidate paths for this node pair; For indicator functions, when the first The section is located If the condition is met, take 1; otherwise, take 0. As an indicator function, it is related to the above-mentioned seismic intensity. They have different meanings.

[0065] It should be understood that for a pair of reachable nodes, multiple candidate acyclic paths equally share a unit weight; when When the summation value is empty, the inner summation is treated as 0, and the unreachable node pair is still included. .Will Divide by all road sections The maximum value is used to obtain the normalized road segment topological importance in the range of 0 to 1. When all When all are 0, all Set to 0. Multipath edge betweenness is a specific implementation for determining the topological importance of a road segment based on multiple candidate acyclic paths. Furthermore, for cases where the maximum value of the multipath edge betweenness is zero or the maximum value of the functional loss equals the minimum value, the corresponding normalization index should be set to zero. Additionally, the final output should output the topological importance of the road segment along with... And path tracing information.

[0066] In step S104, the comprehensive criticality of each road segment is calculated based on the functional loss of each road segment and the road segment topological importance of each road segment, and the critical road segment ranking or critical road segment set of the area to be predicted is determined according to the comprehensive criticality of each road segment.

[0067] In some embodiments, the fault recurrence parameter includes a unique identifier for each road segment. Determining the critical road segment ranking or set of critical road segments for the area to be predicted based on the comprehensive criticality of each road segment includes: sorting each road segment in descending order based on its comprehensive criticality to obtain the critical road segment ranking for the area to be predicted; and selecting a preset proportion of road segments from the critical road segment ranking to form the critical road segment ranking set for the area to be predicted. If multiple road segments have the same comprehensive criticality, then these multiple road segments with the same comprehensive criticality are sorted in ascending order based on their unique identifiers.

[0068] Specifically, in this embodiment, the functional loss of each road segment under the current scenario can be minimum-maximum normalized, and the normalized functional loss and the road segment topological importance can be weighted and summed using functional loss weights to obtain the comprehensive criticality of each road segment. Specifically, this embodiment can use the minimum and maximum values ​​of the functional loss of all highway road segments within the current earthquake scenario, perform range normalization of the functional loss according to the thirteenth calculation formula, and fuse the normalized functional loss with the normalized road segment topological importance according to the fourteenth calculation formula. The thirteenth calculation formula is as follows: ; in, Index of hazardous scenarios; For the context Next Functional loss of each road section; This represents the minimum functional loss across all road segments in this scenario; This represents the maximum functional loss across all road segments in this scenario; This is due to the loss of normalized functionality.

[0069] It should be understood that all the above calculation results should be determined within the same scenario. When hour, Located between 0 and 1; when At that time, all of this scenario Set it to 0 to avoid division by zero and maintain deterministic output.

[0070] Accordingly, the fourteenth calculation formula is: ; in, For the context Next The comprehensive criticality of each road section; For functional loss weights and ; This represents the normalized functional loss. To normalize the topological importance of road segments.

[0071] It should be understood that when two hazardous scenarios utilize the same highway network and control nodes, This serves as a static topological component shared by both scenarios. When both normalized components are between 0 and 1, It is also located between 0 and 1.

[0072] Furthermore, embodiments of this application can be tailored according to the comprehensive criticality of each road segment. The road segments are arranged in descending order. When multiple road segments have the same overall criticality, this embodiment can arrange the road segments in ascending order according to the unique identifier of each road segment to generate a deterministic ranking (i.e., the same input and parameters produce the same critical road segment ranking and set). Then, this embodiment can select the top-ranked road segments according to a preset proportion or preset number to form a critical road segment set, and save the weights and thresholds with the output. The saved weights and thresholds may include per-segment PGA, probability of damage state, speed retention rate, capacity retention rate, etc. , The importance of road segment topology, comprehensive criticality, ranking, and key set labeling.

[0073] It should be understood that in practical applications, the preset weights, proportions, or quantities can be adjusted according to actual management needs, for example, the weight of functional loss. The value can be 0.5, and the preset ratio can be 5%. Furthermore, the above results are used for relative road segment screening under unified conditional hazard levels and static network scales. Additionally, since this application embodiment uses the PGA of each hazard point under unified conditional exceedance probability, it neither generates spatial ground motion samples occurring simultaneously in a single earthquake event, nor redistributes traffic flow or calculates dynamic connectivity after component damage. Therefore, the function retention index, function loss, and comprehensive criticality calculated in this application embodiment are only used for relative road segment screening within the current model.

[0074] Therefore, this application proposes a unified data processing method that can be executed by a computer. This method does not require interpreting the relative screening score as event-type traffic performance or physical loss. It can associate fault recurrence status, probabilistic seismic hazard, road segment vulnerability with road segment topology based on multiple candidate paths to form a reproducible ranking result or set of key road segments for highways.

[0075] Furthermore, to enable those skilled in the art to better understand the method for screening critical road sections of transportation network disaster resilience of this application, the following description is provided in conjunction with specific embodiments.

[0076] Specifically, this application evaluated approximately 50,000 highway segments. The average PGA of the segments in the BPT update scenario and the Poisson baseline scenario were 0.1389g and 0.1325g, respectively, and the average function retention indices were 0.9565 and 0.9622, respectively. The Spearman coefficient for ranking the function retention indices of the two scenarios was 0.9949; the Jaccard coefficient for the top 5% of segments with the largest function loss was 0.7981. The BPT update scenario added and removed 292 segments compared to the baseline scenario.

[0077] Furthermore, under the condition of equal weighting of functional loss and road segment topological importance, the Spearman coefficient for the comprehensive criticality ranking of the two scenarios is 0.9957, the Jaccard coefficient for the top 5% critical road segment set is 0.7712, and there are 336 newly added and 336 removed road segments. The Jaccard coefficients at the top 1%, top 5%, and top 10% thresholds are 0.4138, 0.7712, and 0.8535, respectively. Comparing the functional loss weights of 0.3, 0.5, and 0.7, a total of 431 road segments were selected as stable cores under both scenarios and the three weight sets.

[0078] Furthermore, the aforementioned statistics were recalculated by pairing the 52,009 identical road segments with unique identifiers; the maximum sum error of state probabilities, the error of the FI identity, and the error of the comprehensive critical formula were all checked according to preset numerical tolerances. Statistical results show that the embodiments of this application can output a sorting and set of road segments that are generally stable but locally changeable. These values ​​are descriptive verifications under the current data and parameters.

[0079] Therefore, the embodiments of this application can combine the functional loss of road segments with the topological importance of road segments to calculate the comprehensive criticality of each road segment, and complete the sorting of road segments and the screening of key road segment sets based on the comprehensive criticality. For road segments with the same comprehensive criticality, a stable and reproducible sorting result can be achieved by using the unique identifier of the road segment.

[0080] Furthermore, embodiments of this application can integrate the aforementioned method for screening critical road segments based on fault recurrence probability updates and road segment topological importance into a highway critical road segment screening system. This system includes: a data acquisition module, a fault probability update module, a hazard calculation module, a road segment matching module, a functional loss calculation module, a road segment topological importance calculation module, and a critical road segment output module. Specifically, the data acquisition module acquires fault recurrence parameters, earthquake source models, target prediction periods, and highway segment networks; the fault probability update module calculates the BPT condition occurrence probability of the target fault and the equivalent annual occurrence rate of the target prediction period, and updates the high-magnitude interval occurrence rate of the target fault; the hazard calculation module calculates time-related probabilistic seismic hazard analysis based on the updated earthquake source model. The system includes: a PSHA (Probability of Seismic Hazard) spatial field analysis module; a road segment matching module to match the peak ground acceleration (PGA) in the time-correlated probabilistic seismic hazard spatial field to bridge, tunnel, and ordinary road segments; a function loss calculation module to calculate discrete damage state probabilities, expected velocity retention rates, expected capacity retention rates, function retention indices, and function losses based on four levels of vulnerability parameters; a road segment topology importance calculation module to determine the topology importance of each road segment in the target highway segment network based on multiple candidate acyclic paths between control node pairs and the participation of each target highway segment in these multiple candidate acyclic paths; and a key road segment output module to normalize and weightedly fuse the function loss and road segment topology importance, outputting a ranking or set of key road segments for the area to be predicted, with optional Poisson baseline scenario comparison. The outputs of all the above modules retain the input version, parameters, and unique road segment identifiers to support recalculation and auditing.

[0081] It should be understood that the aforementioned modules can transmit fault identifiers, occurrence rates, hazardous locations, unique road segment identifiers, and segment-by-segment indicators through shared databases, file interfaces, or in-memory data structures. The specific year, prediction duration, parameters, number of paths, fusion weights, and screening thresholds can all be dynamically adjusted based on the target area, prediction task, management needs, and actual application scenarios.

[0082] Therefore, this embodiment can calculate the probability of BPT occurrence and the equivalent annual occurrence rate of the target prediction period based on the fault mean recurrence interval, elapsed time, and non-periodic parameters, update the high-magnitude interval of the target fault, and calculate the time-related probability seismic hazard spatial field; match peak ground acceleration to each segment of bridges, tunnels, and ordinary roads, and obtain functional loss through four-level vulnerability calculation; determine the topological importance of road segments based on multiple candidate acyclic paths between control node pairs and the participation of each highway segment in the path; normalize and fuse the functional loss and the topological importance of road segments, and output the key segment ranking or key segment set. This embodiment can reflect the fault recurrence stage and distinguish between potentially high functional loss channels and static network bottlenecks.

[0083] Furthermore, to enable those skilled in the art to better understand the method for screening critical road segments for disaster resilience of transportation networks in this application, the following is combined with... Figures 2 to 5 The specific embodiments shown will be described in detail.

[0084] Figure 2 A flowchart illustrating a method for screening critical road sections with disaster resilience in transportation networks, provided in an embodiment of this application.

[0085] like Figure 2 As shown, the method for screening critical road sections with disaster resilience in this transportation network includes the following steps: S201, obtain fault, seismic source, time period and road network data.

[0086] S202, calculate the conditional probability of BPT and the equivalent annual incidence rate.

[0087] S203, update the high-magnitude interval and calculate the PSHA spatial field.

[0088] S204 will match the PGA to Class III highway sections.

[0089] S205 calculates the probability of damage, FI, and functional loss.

[0090] S206, calculates the betweenness number and topological importance of multipath edges.

[0091] S207, outputs the critical road segment ranking or the critical road segment set.

[0092] Therefore, this embodiment takes regional active faults, seismic source models, target prediction periods, and highway segment networks as inputs, and sequentially executes steps S201 to S207 to complete BPT updates, PSHA data processing, segment matching, vulnerability and functional loss calculations, segment topological importance calculations based on multiple candidate acyclic paths, and key segment ranking. This results in segment-by-segment hazard, functional loss, segment topological importance, and comprehensive criticality, and outputs a ranking of key segments or a set of key segments. Even with only partial active fault recurrence parameters, regional seismic source models, and highway segment networks, the incidence changes caused by fault recurrence stages are transferred to segment PGA and functional loss, and are collaboratively calculated with segment topological importance considering alternative pathways, thereby obtaining a stable and traceable highway key segment screening output. Thus, this embodiment can adapt to application scenarios with missing input parameters, coupling seismic hazard evolution effects with road network topological attributes to achieve stable and traceable highway key segment screening.

[0093] Furthermore, Figure 3 This application provides a BPT fault occurrence rate update and PSHA data flow graph for embodiments of the present application.

[0094] like Figure 3 As shown, the BPT fault occurrence rate update and PSHA data stream include the following steps: S301, determine recurrence parameters and time of death.

[0095] S302, using the probability of BPT conditional occurrence for probability matching.

[0096] S303, calculate the equivalent annual incidence rate.

[0097] S304, high-magnitude interval update.

[0098] S305, using the updated seismic source model for PSHA.

[0099] S306, PGA hazardous space field.

[0100] S307, Poisson model for missing parameter faults.

[0101] Therefore, the embodiments of this application can complete the input of fault recurrence parameters, BPT conditional probability matching and equivalent annual occurrence rate solution based on the data stream, realize the update of high-magnitude fault intervals, and perform probabilistic seismic hazard analysis based on the updated earthquake source model to generate PGA hazard spatial field. At the same time, Poisson model is retained for faults with missing recurrence parameters to ensure model applicability and provide seismic hazard calculation support for subsequent key road segment ranking or key road segment screening.

[0102] Furthermore, Figure 4 A flowchart illustrating a method for processing data on the functional loss of hazardous spatial fields and road sections, provided in an embodiment of this application.

[0103] S401, PGA hazardous space field.

[0104] S402, nearest neighbor matching at the midpoint of the road segment.

[0105] S403 classifies bridges, tunnels, and ordinary roads, and outputs selection parameters.

[0106] S404, Level 4 vulnerability and probability of damage state, and perform functional mapping.

[0107] S405, with speed and capacity retention, and undergoes blending polymerization.

[0108] S406, FI and functional loss.

[0109] Therefore, this application embodiment considers that screening for seismic impacts on highway sections involves transmitting spatial seismic inputs to different components such as bridges, tunnels, and ordinary roads. If sorting is only based on seismic motion or vulnerability, it is difficult to identify road sections that play a key role in static connectivity. If sorting is only based on a single shortest path index, it cannot reflect the fault probability update and potential functional losses caused by component vulnerability. Based on this, this application embodiment can extract the geometric midpoint of the highway section and match the nearest hazardous field point, assigning PGA values ​​to bridge sections, tunnel sections, and ordinary road sections to realize the transformation of component damage into road section functional indicators; select four levels of log-normal vulnerability parameters according to the preset road section type, and calculate the probability of occurrence of five mutually exclusive damage states; in the functional mapping stage, severe damage and complete damage are merged into functional failure states, and then the speed retention rate, capacity retention rate, functional retention index, and functional loss are solved. Therefore, the embodiments of this application can complete the matching of seismic inputs for different types of road sections, combine the vulnerability of components to achieve probabilistic solutions for multiple damage states, and complete the transformation of disasters into road network functional losses through damage state merging, providing a quantitative basis for calculating functional losses for subsequent comprehensive critical assessment of road sections.

[0110] Furthermore, Figure 5 A flowchart illustrating a method for determining the topological importance of road segments and ranking key road segments, as provided in an embodiment of this application.

[0111] S501 uses control nodes and the Link network for pathfinding.

[0112] S502 performs path weight accumulation on multiple acyclic paths for a node.

[0113] S503 normalizes the maximum value of the betweenness number of multipath edges.

[0114] S504, calculate topological importance.

[0115] S505, calculates the functional components based on the normalized functional loss.

[0116] S506, perform deterministic ranking based on comprehensive criticality.

[0117] S507, key road segment sorting or set.

[0118] The method for screening critical road segments for disaster resilience of transportation networks proposed in this application can determine the target fault in the area to be predicted and the probability of occurrence of BPT conditions and the equivalent annual occurrence rate of the target fault during the target prediction period. Based on the equivalent annual occurrence rate and the seismic source model, the seismic source model is updated, and the time-related probabilistic seismic hazard spatial field is calculated. This determines the functional loss of each road segment in the target highway segment network. Furthermore, unordered node pairs are constructed based on the control nodes in the target highway segment network to determine the topological importance of each road segment. Finally, the comprehensive criticality of each road segment is calculated based on its functional loss and topological importance, thereby determining the ranking or set of critical road segments in the area to be predicted. This solves the problems of low accuracy in seismic risk assessment and discrepancies between the screening results of critical road segments and the actual seismic risk of faults in related technologies. It improves the reliability of seismic risk assessment and ensures that the screening results of critical road segments closely match the actual seismic risk of faults.

[0119] Next, referring to the accompanying drawings, the critical road segment screening device for disaster resilience of the transportation network proposed in this application is described.

[0120] Figure 6 This is a block diagram of the critical road segment screening device for disaster resilience of transportation networks proposed in the embodiments of this application.

[0121] like Figure 6 As shown, the key road segment screening device 10 for disaster resilience of the transportation network includes: an acquisition module 100, a first calculation module 200, a second calculation module 300, and a screening module 400.

[0122] The system comprises the following modules: 100, which determines fault recurrence parameters, seismic source model, target prediction period, and target highway segment network for the area to be predicted; 200, which, based on fault recurrence parameters, determines the probability of occurrence of the target fault and the BPT condition of the target fault within the target prediction period, and the equivalent annual occurrence rate, updates the seismic source model based on the equivalent annual occurrence rate and the seismic source model, and calculates the time-related probabilistic seismic hazard spatial field based on the updated seismic source model; 300, which, based on the time-related probabilistic seismic hazard spatial field, determines the functional loss of each segment in the target highway segment network, constructs disordered node pairs based on the control nodes in the target highway segment network, and determines the segment topological importance of each segment based on multiple candidate acyclic paths of the disordered node pairs; and 400, which, based on the functional loss and topological importance of each segment, calculates the comprehensive criticality of each segment, and determines the critical segment ranking or set of critical segments for the area to be predicted based on the comprehensive criticality of each segment.

[0123] Optionally, in some embodiments, the fault recurrence parameters include the average recurrence interval, the time of the most recent characteristic event, a non-periodic parameter, and the recurrence interval. The first calculation module 200 is specifically used for: identifying a target fault with an average recurrence interval and the time of the most recent characteristic event based on the fault recurrence parameters; calculating the elapsed time of the target fault according to the start time of the target prediction period and the time of the most recent characteristic event, and constructing a recurrence interval probability density function according to the average recurrence interval, the non-periodic parameter, and the recurrence interval of the target fault, and determining the cumulative distribution function of the target fault according to the recurrence interval probability density function; calculating the BPT conditional occurrence probability of the target fault within the target prediction period according to the target prediction period, the elapsed time of the target fault, and the cumulative distribution function of the target fault, and calculating the equivalent annual incidence rate of the target fault within the target prediction period according to the BPT conditional occurrence probability.

[0124] Optionally, in some embodiments, the first calculation module 200 is specifically used to: determine the maximum magnitude and target magnitude interval of the target fault, and construct the incremental magnitude-frequency distribution of the target magnitude interval based on the equivalent annual occurrence rate, and obtain the updated target fault based on the incremental magnitude-frequency distribution of the target magnitude interval; and update the earthquake source model based on the updated target fault, the unupdated fault in the earthquake source model and the background earthquake source to obtain the updated earthquake source model.

[0125] Optionally, in some embodiments, the second calculation module 300 is specifically used for: mapping each road segment in the target highway segment network to a time-correlated probability seismic hazard spatial field to a unified coordinate reference system, determining a representative point for each road segment, and establishing a nearest neighbor index on the time-correlated probability seismic hazard spatial field based on the representative point of each road segment; assigning the peak ground acceleration in the time-correlated probability seismic hazard spatial field to the corresponding road segment in the target highway segment network according to a preset matching rule and the nearest neighbor index, while recording the matching relationship between the hazard field point and each road segment in the target highway segment network to obtain a first target highway segment network; determining the fourth-level vulnerability parameters of each road segment in the first target highway segment network based on a preset road segment type, and calculating the functional loss of each road segment in the target highway segment network according to the fourth-level vulnerability parameters of each road segment.

[0126] Optionally, in some embodiments, the second calculation module 300 is specifically used for: calculating the level 4 damage exceedance probability of each road segment based on the level 4 vulnerability parameters of each road segment, and determining the level 5 damage state probability of each road segment based on the level 4 damage exceedance probability of each road segment, wherein the level 5 damage state probability includes the probability of no damage state, the probability of slight damage state, the probability of moderate damage state, the probability of severe damage state, and the probability of complete damage state; merging the severe damage state corresponding to the severe damage state probability and the complete damage state corresponding to the complete damage state probability into a functional failure state to obtain the level 4 functional state probability of each road segment, wherein the level 4 functional state probability includes the probability of no damage, the probability of slight damage, the probability of moderate damage, and the probability of functional failure; calculating the expected speed retention rate and the expected capacity retention rate of each road segment based on the level 4 functional state probability of each road segment, and aggregating the expected speed retention rate and the expected capacity retention rate of each road segment to obtain the functional retention index of each road segment, and calculating the functional loss of each road segment based on the functional retention index of each road segment.

[0127] Optionally, in some embodiments, the second calculation module 300 is specifically used to: use the continuous channel length between adjacent control nodes as the path weight, and search for multiple candidate acyclic paths for unordered node pairs based on the path weight, and divide the unordered node pairs into reachable node pairs and unreachable node pairs according to the multiple candidate acyclic paths for unordered node pairs; calculate the segment topological importance of the multiple candidate acyclic paths between reachable node pairs, and set the segment topological importance of the multiple candidate acyclic paths between unreachable node pairs to zero.

[0128] Optionally, in some embodiments, the filtering module 400 is specifically used to: sort each road segment in descending order based on the comprehensive criticality of each road segment to obtain the critical road segment ranking of the area to be predicted, and select a preset proportion of road segments from the critical road segment ranking to form a critical road segment set of the critical road segment ranking of the area to be predicted; wherein, if there are multiple road segments with the same comprehensive criticality, the multiple road segments with the same comprehensive criticality are sorted in ascending order according to the unique identifier of the multiple road segments.

[0129] It should be noted that the explanation of the above-mentioned embodiment of the method for screening critical road sections of disaster resilience of transportation networks also applies to the screening device for critical road sections of disaster resilience of transportation networks in this embodiment, and will not be repeated here.

[0130] The critical road segment screening device for disaster resilience of transportation networks proposed in this application can determine the target fault in the area to be predicted and the probability of occurrence of the target fault under BPT conditions and the equivalent annual occurrence rate of the target fault within the target prediction period. It updates the seismic source model based on the equivalent annual occurrence rate and the seismic source model, and calculates the time-related probabilistic seismic hazard spatial field, thereby determining the functional loss of each road segment in the target highway segment network. It also constructs disordered node pairs based on the control nodes in the target highway segment network to determine the topological importance of each road segment in the target highway segment network. Finally, it calculates the comprehensive criticality of each road segment based on its functional loss and topological importance, thus determining the ranking or set of critical road segments in the area to be predicted. This solves the problems of low accuracy in seismic risk assessment and deviation between the screening results of critical road segments and the actual seismic risk of faults in related technologies, improving the reliability of seismic risk assessment and ensuring that the screening results of critical road segments closely match the actual seismic risk of faults.

[0131] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0132] When the processor 702 executes the program, it implements the method for screening critical road sections of the disaster resilience of the transportation network provided in the above embodiments.

[0133] Furthermore, the electronic device also includes: Communication interface 703 is used for communication between memory 701 and processor 702.

[0134] The memory 701 is used to store computer programs that can run on the processor 702.

[0135] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0136] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0137] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0138] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0139] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following... Figure 1 The method for screening key road sections with strong disaster resilience of transportation networks is shown.

[0140] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0141] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0142] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0143] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0144] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0145] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0146] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0147] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for screening critical road sections with disaster resilience in transportation networks, characterized in that, include: Determine the fault recurrence parameters, earthquake source model, target prediction period, and target highway segmentation network for the area to be predicted; Based on the fault recurrence parameters, the probability of occurrence of BPT conditions and the equivalent annual occurrence rate of the target fault during the target prediction period are determined. Based on the equivalent annual occurrence rate and the earthquake source model, the earthquake source model is updated. Based on the updated earthquake source model, the time-related probability seismic hazard spatial field is calculated. Based on the time-correlated probabilistic seismic hazard spatial field, the functional loss of each road segment in the target highway segment network is determined, and an unordered node pair is constructed according to the control nodes in the target highway segment network. Based on the multiple candidate acyclic paths of the unordered node pair, the topological importance of each road segment in the target highway segment network is determined. The comprehensive criticality of each road segment is calculated based on the functional loss of each road segment and the road segment topological importance of each road segment, and the critical road segment ranking or critical road segment set of the region to be predicted is determined according to the comprehensive criticality of each road segment.

2. The method for screening critical road sections with disaster resilience in transportation networks according to claim 1, characterized in that, The fault recurrence parameters include the average recurrence interval, the time of the most recent characteristic event, non-periodic parameters, and the recurrence interval. Based on these fault recurrence parameters, determining the target fault and the probability of BPT occurrence and the equivalent annual incidence rate of the target fault within the target prediction period includes: Based on the fault recurrence parameters, the target fault that has the average recurrence interval and the time of the most recent characteristic event is identified; The elapsed time of the target fault is calculated based on the start time of the target prediction period and the time of the most recent characteristic event. A recurrence interval probability density function is constructed based on the average recurrence interval of the target fault, the non-periodic parameter, and the recurrence interval. The cumulative distribution function of the target fault is determined based on the recurrence interval probability density function. The probability of BPT conditional occurrence of the target fault during the target prediction period is calculated based on the target prediction period, the elapsed time of the target fault, and the cumulative distribution function of the target fault. The equivalent annual incidence rate of the target fault during the target prediction period is then calculated based on the probability of BPT conditional occurrence.

3. The method for screening critical road sections with disaster resilience in transportation networks according to claim 1, characterized in that, The step of updating the earthquake source model based on the equivalent annual occurrence rate and the earthquake source model includes: The maximum magnitude and target magnitude range of the target fault are determined, and based on the equivalent annual occurrence rate, the incremental magnitude-frequency distribution of the target magnitude range is constructed. Based on the incremental magnitude-frequency distribution of the target magnitude range, the updated target fault is obtained. Based on the updated target fault, the unupdated fault in the earthquake source model, and the background earthquake source, the earthquake source model is updated to obtain the updated earthquake source model.

4. The method for screening critical road sections with disaster resilience in transportation networks according to claim 1, characterized in that, The determination of the functional loss of each segment in the target highway segment network based on the time-correlated probabilistic seismic hazard spatial field includes: Map each road segment in the target highway segment network to the time-related probabilistic seismic hazard spatial field to a unified coordinate reference system, determine the representative point of each road segment, and establish a nearest neighbor index on the time-related probabilistic seismic hazard spatial field based on the representative point of each road segment. Based on the preset matching rules, the peak ground acceleration in the time-related probability seismic hazard spatial field is assigned to the corresponding road segment in the target highway segment network according to the nearest neighbor index. At the same time, the matching relationship between the hazard field point and each road segment in the target highway segment network is recorded to obtain the first target highway segment network. Based on the preset road segment type, the fourth-level vulnerability parameters of each road segment in the first target highway segment network are determined, and the functional loss of each road segment in the target highway segment network is calculated based on the fourth-level vulnerability parameters of each road segment.

5. The method for screening critical road sections with disaster resilience in transportation networks according to claim 4, characterized in that, The calculation of the functional loss of each road segment in the target highway segment network based on the four-level vulnerability parameters of each road segment includes: The probability of exceeding the level 4 damage for each road segment is calculated based on the level 4 vulnerability parameters of each road segment, and the probability of the level 5 damage state for each road segment is determined based on the probability of exceeding the level 4 damage for each road segment. The probability of the level 5 damage state includes the probability of no damage state, the probability of slight damage state, the probability of moderate damage state, the probability of severe damage state, and the probability of complete damage state. The severe damage state corresponding to the severe damage state probability and the complete damage state corresponding to the complete damage state probability are combined into the functional failure state to obtain the four-level functional state probability of each road segment. The four-level functional state probability includes no damage probability, slight damage probability, moderate damage probability and functional failure probability. The expected speed retention rate and expected capacity retention rate of each road segment are calculated based on the probability of the fourth functional state of each road segment. The expected speed retention rate and expected capacity retention rate of each road segment are aggregated to obtain the functional retention index of each road segment. The functional loss of each road segment is calculated based on the functional retention index of each road segment.

6. The method for screening critical road sections with disaster resilience in transportation networks according to claim 1, characterized in that, The determination of the topological importance of each road segment in the target highway segmentation network based on multiple candidate acyclic paths of the unordered node pairs includes: The length of the continuous channel between adjacent control nodes is used as the path weight. Based on the path weight, multiple candidate acyclic paths for the unordered node pair are searched. The unordered node pair is then divided into reachable node pairs and unreachable node pairs according to the multiple candidate acyclic paths. Calculate the segment topological importance of multiple candidate acyclic paths between the reachable node pairs, and set the segment topological importance of multiple candidate acyclic paths between the unreachable node pairs to zero.

7. The method for screening critical road sections with disaster resilience in transportation networks according to claim 1, characterized in that, The fault recurrence parameter includes a unique identifier for each road segment. Determining the order or set of critical road segments in the area to be predicted based on the comprehensive criticality of each road segment includes: Based on the comprehensive criticality of each road segment, the road segments are sorted in descending order to obtain the critical road segment ranking of the area to be predicted, and a preset proportion of road segments are selected from the critical road segment ranking to form the critical road segment set of the critical road segment ranking of the area to be predicted. If multiple road segments have the same overall criticality, then these multiple road segments with the same overall criticality are sorted in ascending order based on their unique identifiers.

8. A screening device for critical road sections with disaster resilience in transportation networks, characterized in that, include: The acquisition module is used to determine the fault recurrence parameters, earthquake source model, target prediction period, and target highway segment network of the area to be predicted; The first calculation module is used to determine the probability of occurrence of the target fault and the equivalent annual occurrence rate of the BPT condition of the target fault in the target prediction period based on the fault recurrence parameter, and to update the earthquake source model based on the equivalent annual occurrence rate and the earthquake source model, and to calculate the time-related probability seismic hazard spatial field based on the updated earthquake source model. The second calculation module is used to determine the functional loss of each road segment in the target highway segment network based on the time-related probability seismic hazard spatial field, construct disordered node pairs according to the control nodes in the target highway segment network, and determine the segment topological importance of each road segment in the target highway segment network based on multiple candidate acyclic paths of the disordered node pairs. The filtering module is used to calculate the comprehensive criticality of each road segment based on the functional loss of each road segment and the road segment topological importance of each road segment, and to determine the critical road segment ranking or critical road segment set of the region to be predicted based on the comprehensive criticality of each road segment.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for screening critical road segments for disaster resilience of transportation networks as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by the processor, it implements the method for screening critical road sections with disaster resilience of transportation networks as described in any one of claims 1-7.

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