Bridge cluster pavement distress cross-diagnosis method based on dynamic deflection space-time correlation

Abstract

The bridge cluster pavement damage cross-diagnosis method based on the time-space correlation of dynamic deflection belongs to the technical field of civil engineering structure operation safety intelligent monitoring. Point type dynamic deflection sensors arranged at key sections of a bridge structure are used to obtain bridge dynamic deflection monitoring information, and then a bridge deck damage diagnosis factor is constructed based on the time-space correlation of the bridge cluster dynamic deflection. On this basis, a bridge deck damage cross-diagnosis threshold is constructed by using the cross-validation theory. Based on the anomaly diagnosis algorithm and in combination with the characteristics of the bridge cluster, the bridge deck damage state is diagnosed. The time-space correlation of the deflection monitoring data is used in combination with the characteristics of the similar bridge cluster structure and the related load to construct a bridge deck damage diagnosis factor. The diagnosis factor can effectively reflect the change of the bridge structure response caused by the pavement damage, can effectively remove the interference of the environmental factors on the pavement damage diagnosis, and realizes the accurate diagnosis of the bridge deck damage state.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring technology for the operational safety of civil engineering structures, specifically involving a cross-diagnosis method for bridge cluster pavement damage based on the spatiotemporal correlation of dynamic deflection. Background Technology

[0002] Bridge impact-related defects refer to a form of damage where the bridge structure experiences increased impact from traffic loads after damage to bridge deck components, leading to further structural damage. my country has a large number of bridges and complex vehicle traffic, resulting in a very high probability of impact-related defects, including bridge deck damage, expansion joint defects, damage at bridge deck continuity points, and damage to bridge approach slabs. The most significant factors causing impact-related defects are damage to the bridge deck and pavement. Bridge pavement is designed to protect the main beams from direct vehicle traffic and to distribute loads. During the bridge's service life, material aging, environmental corrosion, the effects of overloaded vehicles, and other accidents cause damage to the pavement, such as rutting, cracking, potholes, and surface abrasion. This severely affects the bridge's structural performance, further increasing the impact effect from traffic loads and exacerbating the aforementioned bridge deck defects as well as other types of structural defects, creating potential safety hazards.

[0003] Currently, bridge pavement damage diagnosis mainly relies on methods such as direct identification using manual labor or other equipment, and identification based on dynamic characteristics. These methods are time-consuming and labor-intensive, and struggle to provide timely and effective diagnosis of bridge pavement damage. In recent years, bridge structural health monitoring technology has been systematically developed for long-span bridges, achieving significant application results and providing strong support for solving the problem of bridge pavement damage diagnosis. Bridge structural health monitoring systems can achieve real-time acquisition of bridge structural monitoring data without interrupting traffic. However, bridge structural monitoring data is susceptible to environmental factors, and how to effectively utilize this data to achieve effective diagnosis of bridge pavement damage remains a challenging problem. Summary of the Invention

[0004] This invention addresses the problem of effectively diagnosing bridge pavement damage by providing a cross-diagnosis method for bridge cluster pavement damage based on the spatiotemporal correlation of dynamic deflection. A bridge cluster is defined as a set of multiple identical or similar bridges. By utilizing the characteristics of the bridge cluster, the spatiotemporal correlation of deflection monitoring data is constructed, thereby achieving effective diagnosis of pavement damage in the bridge cluster.

[0005] The technical solution adopted in this invention is:

[0006] A method for cross-diagnosis of pavement damage in bridge clusters based on the spatiotemporal correlation of dynamic deflection includes the following steps:

[0007] S1. Construct bridge deck damage diagnostic factors;

[0008] S2. Using the bridge deck damage diagnostic factors obtained in S1, construct the bridge deck damage cross-diagnosis threshold;

[0009] S3. Diagnose the damage condition of the bridge deck.

[0010] Compared with the prior art, the present invention has the following advantages:

[0011] Compared with existing technologies, this invention utilizes the spatiotemporal correlation of deflection monitoring data and combines the characteristics of similar bridge cluster structures and load correlation to construct a bridge deck damage diagnostic factor. This diagnostic factor can effectively reflect the changes in bridge structural response caused by pavement damage and can effectively remove the interference of environmental factors on pavement damage diagnosis, thus achieving accurate diagnosis of bridge deck damage status. Attached Figure Description

[0012] Figure 1 This is a flowchart of the invention;

[0013] Figure 2 This is a finite element model diagram of the bridge structure in an implementation example;

[0014] Figure 3 This is a diagram showing the cross-diagnosis results of a single bridge deck damage incident in an implementation example;

[0015] Figure 4 This is a diagram showing the damage assessment and decision-making results in an implementation example; Detailed Implementation

[0016] To better understand the purpose, structure, and function of this invention, the invention will be described in further detail below with reference to the accompanying drawings.

[0017] Reference Figure 1 As shown, this invention provides a method for cross-diagnosis of pavement damage in bridge clusters based on the spatiotemporal correlation of dynamic deflection.

[0018] First, point-type dynamic deflection sensors deployed at key sections of the bridge structure are used to acquire dynamic deflection monitoring information of the bridge. Then, based on the spatiotemporal correlation of dynamic deflection of the bridge cluster, a bridge deck damage diagnostic factor is constructed. Second, based on this, cross-validation theory is used to construct a cross-diagnostic threshold for bridge deck damage. Finally, based on anomaly diagnosis algorithms and combined with the characteristics of the bridge cluster, an effective diagnosis of bridge deck damage status is achieved.

[0019] Includes the following steps:

[0020] S1. Construct bridge deck damage diagnostic factors;

[0021] By using point-type dynamic deflection sensors deployed at key sections of the bridge structure, dynamic deflection monitoring information of the bridge is obtained. Based on this, bridge deck damage diagnostic factors are constructed by utilizing the spatiotemporal correlation of dynamic deflection of bridge clusters.

[0022] S2. Using the bridge deck damage diagnostic factors obtained in S1, and based on the cross-validation theory, construct the bridge deck damage cross-diagnosis threshold;

[0023] S3. Diagnose the damage condition of the bridge deck;

[0024] Based on anomaly diagnosis algorithms and combined with the characteristics of bridge clusters, the damage status of bridge decks is diagnosed.

[0025] The specific steps are as follows:

[0026] S1. The method for constructing bridge deck damage diagnostic factors is as follows:

[0027] S11. A bridge cluster is defined as a collection of multiple identical or similar bridges. Point-type dynamic deflection sensors are deployed at key cross-sections of the bridge structure to acquire dynamic deflection monitoring data, as shown in the following formula.

[0028]

[0029] In the formula, For the kth i Deflection monitoring data of the i-th bridge within the cluster at time ω; i Let n be the deflection vector of the i-th bridge within the cluster; i The number of monitoring times selected for the i-th bridge; For the kth j Deflection monitoring data of the j-th bridge within the cluster at time ω; j Let n be the deflection vector of the j-th bridge within the cluster; j The number of monitoring times selected for the j-th bridge;

[0030] S12. Based on Dynamic Time Warping (DTW) technology, calculate the bridge deck damage diagnostic factor, as shown in the following formula.

[0031]

[0032] In the formula, d i,j R is the bridge deck damage diagnostic factor for the j-th bridge with the i-th bridge as a reference; R(·) is the minimum distance returned by the dynamic time warping technique.

[0033] S13. Calculate the bridge deck damage diagnostic factor for all bridges according to formula (2), as shown in the following formula.

[0034]

[0035] In the formula, d is the bridge deck pavement damage diagnostic factor matrix; d κLet d be the bridge deck damage diagnostic factor vector constructed with the κ-th bridge as a reference; κ,i Let be the bridge deck damage diagnostic factor of the i-th bridge with the κ-th bridge as a reference; n is the total number of bridges in the cluster.

[0036] The method for constructing the cross-diagnostic threshold for S2 bridge deck damage is as follows:

[0037] S21. The mean value of the bridge deck damage diagnostic factor is defined as the cross-diagnostic characteristic index of bridge deck damage, calculated according to the following formula:

[0038]

[0039] In the formula, The cross-diagnostic characteristic index of bridge deck damage for the i-th bridge;

[0040] S22. The 95th percentile of the cross-diagnostic characteristic indicators of bridge deck damage across all bridges in the cluster is used as the cross-diagnostic threshold for bridge deck damage, as shown in the following formula.

[0041]

[0042] In the formula, Ψ is the cross-diagnostic feature index vector of bridge deck damage for bridges within the cluster. 0.95 (·) indicates the calculation of the 95th percentile of a vector; The threshold for cross-diagnosis of bridge deck damage.

[0043] The diagnostic method for the damage condition of the S3 bridge deck is as follows:

[0044] S31. A single determination of the bridge deck damage status within the cluster is performed, as shown in the following formula.

[0045]

[0046] In the formula, z t,i Let be the bridge deck damage discrimination factor for the i-th bridge in the t-th time period;

[0047] S32. Perform T judgments on the bridges within the cluster according to formula (6), and calculate the number of anomalies, as shown in the following formula.

[0048]

[0049] In the formula, S i Let T be the number of anomalies occurring on the i-th bridge; T is the number of judgments performed.

[0050] S33. Based on the anomaly diagnosis theory, the threshold for judging the bridge deck damage state is determined as shown in the following formula.

[0051]

[0052] In the formula, P r The set confidence level (can be 0.95); i The threshold for judging the bridge deck damage state at the corresponding confidence level;

[0053] S34. The bridge deck damage status is diagnosed using a threshold for determining the damage status and the number of anomalies, as shown in the following formula.

[0054] S i >l i (9)

[0055] S i ≤l i (10)

[0056] If equation (9) holds, it can be determined that the bridge deck of the i-th bridge is in a damaged state; if equation (10) holds, it can be determined that the bridge deck of the i-th bridge is in a normal state.

[0057] Implementation example:

[0058] This implementation example uses a finite element model of an actual bridge cluster as a case study to verify the effectiveness of the proposed cross-diagnosis method for bridge cluster pavement damage based on the spatiotemporal correlation of dynamic deflection. The bridge cluster consists of five identical 20m span simply supported hollow slab beam bridges, named Bridge A, Bridge B, Bridge C, Bridge D, and Bridge E. Their structural parameters are shown in Table 1, and the ANSYS finite element models are as follows: Figure 2 As shown. The bridge is modeled using BEAM188 elements, and the concrete grade is C50. When simulating the action of a moving vehicle, a simplified spring-damped mass block is used, with the mass element simulated using MASS21 elements and the spring damping using COMBIN14 elements. The displacement coupling method, commonly used in ANSYS vehicle-bridge coupling, is employed for simulation. When the vehicle moves to the main beam node, the degrees of freedom of the vehicle and the main beam node are coupled to obtain the dynamic deflection response of the main beam.

[0059] Simulated pavement damage was performed on Bridge C. The cross-diagnosis results and damage discrimination decision results for a single pavement damage incident are as follows: Figure 3 , Figure 4 As shown. From Figure 3 As can be seen, even with minor damage to the C bridge deck, a single cross-diagnosis of bridge deck damage can clearly identify the damaged bridge. Further considering the impact of random factors, from... Figure 4 The results show that the proposed algorithm can effectively identify the damage of Bridge C. Although the damage discrimination factors of the other bridges fluctuate with the increase of the number of evaluations, they are still far below the threshold, effectively avoiding misdiagnosis in a single damage diagnosis.

[0060] Table 1. Parameter information of the finite element model of bridges within the cluster.

[0061]

[0062] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A method for cross-diagnosis of pavement damage in bridge clusters based on the spatiotemporal correlation of dynamic deflection, characterized in that: Includes the following steps: S1. Construct bridge deck damage diagnostic factors; S2. Using the bridge deck damage diagnostic factors obtained in S1, construct the bridge deck damage cross-diagnosis threshold; S3. Diagnose the damage condition of the bridge deck. Specifically, S3 involves diagnosing the damage condition of the bridge deck based on anomaly diagnosis algorithms and considering the characteristics of the bridge cluster. Includes the following steps: S31. A single determination of the bridge deck damage status within the cluster is performed, as shown in the following formula. （6） In the formula, For the t-th time period, the first... The bridge deck damage discrimination factor for the bridge; S32. Perform the following steps on the bridges within the cluster according to formula (6). The next step is to determine the number of anomalies and calculate the number of occurrences, as shown in the following formula. （7） In the formula, For the first The number of abnormal occurrences on the bridge; The number of discriminations performed; S33. Based on the anomaly diagnosis theory, the threshold for judging the bridge deck damage state is determined as shown in the following formula. （8） In the formula, The set confidence level; The threshold for judging the bridge deck damage state at the corresponding confidence level; S34. The bridge deck damage status is diagnosed using a threshold for determining the damage status and the number of anomalies, as shown in the following formula. （9） （10） If equation (9) holds, then it can be determined that the first... The bridge deck is in a damaged state; if equation (10) holds, then it can be determined that the first bridge deck is damaged. The bridge deck is in normal condition.

2. The method for cross-diagnosis of bridge cluster pavement damage based on spatiotemporal correlation of dynamic deflection according to claim 1, characterized in that: Specifically, S1 involves using point-type dynamic deflection sensors arranged at key sections of the bridge structure to acquire dynamic deflection monitoring information of the bridge, and then using the spatiotemporal correlation of dynamic deflection of the bridge cluster to construct bridge deck damage diagnostic factors.

3. The method for cross-diagnosis of bridge cluster pavement damage based on spatiotemporal correlation of dynamic deflection according to claim 2, characterized in that: The method for constructing bridge deck damage diagnostic factors in S1 includes the following steps: S11. A bridge cluster is defined as a collection of multiple identical or similar bridges. Point-type dynamic deflection sensors are deployed at key cross-sections of the bridge structure to acquire dynamic deflection monitoring data, as shown in the following formula. （1） In the formula, For the first At time n, the cluster is in the th... Deflection monitoring data of the bridge; For the first in the cluster The deflection vector of the bridge; For the first The number of monitoring times selected for each bridge; For the first At time n, the cluster is in the th... Deflection monitoring data of the bridge; For the first in the cluster The deflection vector of the bridge; For the first The number of monitoring times selected for each bridge; S12. Based on dynamic time warping technology, the bridge deck damage diagnostic factor is calculated as shown in the following formula. （2） In the formula, For the first The bridge is the first one for reference. Diagnostic factors for bridge deck damage; The minimum distance returned by dynamic time warping technique; S13. Calculate the bridge deck damage diagnostic factor for all bridges according to formula (2), as shown in the following formula. （3） In the formula, A diagnostic factor matrix for bridge deck pavement damage; For the first The bridge deck damage diagnostic factor vector is constructed with the bridge as a reference. For the first The bridge is the first one for reference. Diagnostic factors for bridge deck damage; This represents the total number of bridges in the cluster.

4. The method for cross-diagnosis of bridge cluster pavement damage based on spatiotemporal correlation of dynamic deflection according to claim 1, characterized in that: In S2, a cross-diagnosis threshold for bridge deck damage is constructed based on cross-validation theory.

5. The method for cross-diagnosis of bridge cluster pavement damage based on spatiotemporal correlation of dynamic deflection according to claim 4, characterized in that: The method for constructing the cross-diagnostic threshold for bridge deck damage in S2 includes the following steps: S21. The mean value of the bridge deck damage diagnostic factor is defined as the cross-diagnostic characteristic index of bridge deck damage, calculated according to the following formula: （4） In the formula, For the first Cross-diagnostic characteristic indicators of bridge deck damage; S22. The 95th percentile of the cross-diagnostic characteristic indicators of bridge deck damage across all bridges in the cluster is used as the cross-diagnostic threshold for bridge deck damage, as shown in the following formula. （5） In the formula, This is a vector of cross-diagnostic feature indicators for bridge deck damage within the cluster. This represents the 95th percentile of a vector. The threshold for cross-diagnosis of bridge deck damage.

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