A method, system, and medium for assessing bridge damage induced by ship impact

Abstract

The application provides a bridge damage assessment method and system caused by ship collision and a medium, and relates to the technical field of bridge health monitoring. The method comprises the following steps: collecting acceleration response signal arrays of different heights and positions of a bridge pier, and generating a time series matrix; drawing a time-frequency coherence spectrum under different base functions, and obtaining a coherence spectrum entropy; determining the optimal base function based on the information entropy principle; obtaining the local correlation coefficient of the time series matrix in the time-frequency space under the optimal base function, and drawing a time-frequency coherence spectrum; evaluating the correlation and dependence degree between time series according to the local correlation coefficient and the time-frequency coherence spectrum, and judging whether the ship-bridge collision damage occurs; if the collision damage occurs, calculating the time-frequency coherence damage factor of the time series matrix at different positions, and determining the collision position and the collision degree according to the maximum protruding value in the time-frequency coherence damage factor trajectory curve. The method can effectively separate the influence of environmental factors, and provides basic method support for establishing the ship-bridge collision damage assessment technology.

Description

Technical Field

[0001] This invention relates to the field of bridge health monitoring technology, specifically to a method, system, and medium for assessing bridge damage caused by ship collisions. Background Technology

[0002] Ship-bridge collisions occur frequently under complex climatic conditions and due to human factors. In ship-bridge accidents, cracks easily form in the localized fracture zone caused by ship impacts. If these hidden cracks are not detected in time, they can pose a potential threat to bridge safety. Currently, sensors typically collect acceleration response signals; however, due to environmental factors, it is difficult to distinguish the causes of abnormal signal characteristics, and the true damage characteristics are intertwined with complex environmental influences, making them difficult to effectively identify. Therefore, effectively eliminating environmental factors is crucial for ship-bridge collision damage identification research.

[0003] There is a near-complete lack of research on identifying localized, hidden cracks in bridges caused by ship collisions. Current methods are mostly limited to assessing the overall impact damage effect on bridges based on comprehensive indicators such as pier top displacement and rotation angle, determining whether a ship collision has caused damage to the bridge. However, there is a lack of effective detection methods that can accurately capture the dynamic characteristics of localized, hidden cracks in bridges after a collision, and pinpoint the location and extent of the cracks. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a method for assessing bridge damage caused by ship collisions. This method effectively separates environmental variables, enabling rapid and accurate extraction of bridge dynamic information, and overcomes the limitation of existing collision damage identification methods that cannot distinguish whether damage features are caused by actual damage.

[0005] To achieve the above objectives, the present invention provides the following technical solution.

[0006] A method for assessing bridge damage caused by ship collisions, comprising the following steps:

[0007] Acceleration response signal arrays at different heights and positions of bridge piers impacted by ships are collected, and a time series matrix of acceleration response signals is generated based on the acceleration response signal arrays;

[0008] The time-frequency coherence spectrum under different basis functions is plotted based on the time series matrix, and the coherence spectrum entropy is obtained. Based on the principle of information entropy, the information volume of the time-frequency coherence spectrum under different basis function spaces is measured by the coherence spectrum entropy, and the optimal basis function is determined.

[0009] Obtain the local correlation coefficients of the time series matrix in the time-frequency space under the optimal basis functions, and plot the time-frequency coherence spectrum; evaluate the degree of correlation between time series based on the local correlation coefficients and the time-frequency coherence spectrum, and determine whether the ship-bridge collision damage has occurred;

[0010] If a collision occurs, calculate the time-frequency coherence loss factor at different positions in the time series matrix and plot the time-frequency coherence loss factor trajectory curve; determine the collision location and degree of collision based on the maximum value of the time-frequency coherence loss factor trajectory curve.

[0011] Preferably, the method for collecting acceleration response signals from different heights and locations of the bridge impacted by the ship includes the following steps:

[0012] A wall-climbing robot is installed according to the characteristics of the target bridge pier; at least one acceleration sensor is evenly spaced on each side of the wall-climbing robot facing the bridge pier.

[0013] The robot is driven to move along the bridge pier from top to bottom or bottom to top to scan the pier. It collects acceleration response signal arrays at different heights and positions of the pier through acceleration sensors and generates a time series matrix.

[0014] Preferably, the time series matrix is ​​as shown in the following formula:

[0015] X ij = [x1x2...x l ]

[0016] Where m is the total number of sensor layers on the bridge pier, 1≤i≤m; n is the total number of sensors in each array layer, 1≤j≤n; and l is the total length of the time series data collection.

[0017] Preferably, the local correlation coefficient is calculated as follows:

[0018]

[0019] In the formula, R 2 (a,b) are the time-frequency local correlation coefficients between the two sequences, and S is the smoothing operator. and They are time series X ij and X ik The wavelet coefficients, where a is the scaling factor of the time-frequency transform and b is the translation factor;

[0020] In the formula, when j≠k, It is about time series X ij and X ik The cross wavelet transform function, Indicates its cross-wavelet power; when j = k, It is about time series X ij and X ik The autocorrelation wavelet transform function is shown in the following equation:

[0021]

[0022] In the formula, * denotes complex conjugation.

[0023] Preferably, it further includes:

[0024] Wavelet functions are used as basis functions to calculate the time-frequency coherence value R between time series. 2 Undetermined wavelet coefficients in (a,b) As shown in the following formula:

[0025]

[0026] In the formula, ψ is the selected wavelet basis function.

[0027] Preferably, the calculation of the coherence spectral entropy includes the following steps:

[0028] The time-frequency coherence spectrum is divided into N equal time-frequency planes. The number of correlation coefficients passing the 95% significance test within each time-frequency plane is ζ. w The total number A of the entire time-frequency coherence spectrum that passes the 95% significance test (w = 1, ..., N) is normalized for each region to obtain:

[0029]

[0030] Based on the information entropy approach, the coherence spectral entropy H of the time-frequency coherence spectrum is calculated using the following formula. c :

[0031]

[0032] Among them, the coherence spectral entropy H c The entropy value indicates the amount of information contained in the time-frequency coherence spectrum. The larger the entropy value, the greater the amount of information contained in the time-frequency coherence spectrum, and the higher its information value.

[0033] Preferably, the calculation of the time-frequency coherence loss factor includes the following steps:

[0034] The time-frequency coherence loss factor is defined as follows:

[0035] ∈=(AWC Intact -AWC Input ) / AWC Intact

[0036] In the formula, AWC Intact AWC refers to the average value of the time-frequency coherence of a non-damaging structure within a certain time-frequency range; Input It refers to the average value of the time-frequency coherence of the structure under test within a certain time-frequency range.

[0037] Preferably, determining the collision location and degree of collision based on the maximum value of the time-frequency coherence loss factor trajectory curve includes the following steps:

[0038] Based on the local correlation coefficient of the time series matrix in the time-frequency space, the Monte Carlo method is used to calculate the coherence of the time series at the 95% significance level, and the time-frequency coherence spectrum is plotted.

[0039] Among them, the time-frequency coherence spectrum fluctuates within a certain time-frequency range. When the degree of fluctuation cannot be restored to a stable state, it is determined that the ship-bridge collision has occurred. The degree of fluctuation is represented by the ratio of the number of time-frequency coherence spectra that pass the 95% significance test to the total number.

[0040] Based on the characteristics of the change in time-frequency coherent loss factor at the collision damage location, a trajectory curve of the time-frequency coherent loss factor is plotted. When the bridge is damaged, the time-frequency coherent loss factor near the collision damage location will suddenly increase, while the loss factor far away from the collision damage location will be almost zero. The level where the curve reaches its maximum value is considered to be the collision damage location.

[0041] A bridge damage assessment system induced by a ship collision, the system comprising:

[0042] processor;

[0043] A memory on which computer programs that can run on the processor are stored;

[0044] The computer program, when executed by the processor, implements the steps of the bridge damage assessment method caused by ship collision.

[0045] A computer-readable storage medium storing a data processing program, which, when executed by a processor, implements the steps of the bridge damage assessment method caused by a ship collision.

[0046] The beneficial effects of this invention are:

[0047] This invention proposes a method for assessing bridge damage caused by ship collisions. Based on massive sensor signals collected by a wall-climbing robot, this method calculates the time-frequency coherence spectral entropy between signals under different basis functions, optimizes the selection of basis functions, and then plots the time-frequency coherence spectrum in the optimal basis function time-frequency space. This method effectively separates environmental variables, enabling rapid and accurate extraction of bridge dynamic information, overcoming the limitation of existing collision damage identification methods that cannot distinguish whether damage features are caused by actual damage. Furthermore, this invention addresses the characteristic of changes in the time-frequency local correlation coefficient near the collision location by establishing a time-frequency coherence loss factor. The trajectory curve of this factor identifies the location and extent of ship-bridge collision damage, providing fundamental methodological support for establishing ship-bridge collision damage assessment technology. Attached Figure Description

[0048] Figure 1 This is a flowchart of a bridge damage assessment method caused by a ship collision according to an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the numerical simulation of the pier structure and the wall-climbing robot structure according to an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of an acceleration signal acquisition node array according to an embodiment of the present invention, wherein, Figure 3 (a) is a schematic diagram of the overall data acquisition node array. Figure 3 (b) is a schematic diagram of the acquisition node array for each layer;

[0051] Figure 4 These are the time-frequency coherence spectra of different wavelet basis functions in embodiments of the present invention, wherein, Figure 4 (a) represents the Morlet wavelet basis functions. Figure 4 (b) is the Dog wavelet basis function. Figure 4 (c) is a fourth-order Paul wavelet basis function;

[0052] Figure 5 This is a schematic diagram of the time-frequency coherence spectrum of symmetric nodes 2 and 4 in an embodiment of the present invention, wherein, Figure 5 (a) is an undamaged pier. Figure 5 (b) is a damaged pier;

[0053] Figure 6 This is a trajectory curve of the time-frequency coherence loss factor between different nodes at different crack depths according to an embodiment of the present invention;

[0054] Figure 7 This is a time-frequency coherence loss factor trajectory curve of symmetrical nodes 2 and 4 at different array levels under different crack locations in an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0056] Example 1

[0057] The present invention provides a method for assessing bridge damage caused by ship collisions, the specific process of which is as follows: Figure 1 As shown, it includes the following steps:

[0058] S1: Collect acceleration response signal arrays at different heights and positions of bridge piers impacted by ships, and generate a time series matrix of acceleration response signals based on the acceleration response signal array.

[0059] S2: Plot the time-frequency coherence spectrum under different basis functions based on the time series matrix and obtain the coherence spectrum entropy; based on the principle of information entropy, measure the information volume of the time-frequency coherence spectrum under different basis function spaces according to the coherence spectrum entropy, and determine the optimal basis function.

[0060] S3: Obtain the local correlation coefficients of the time series matrix in the time-frequency space under the optimal basis functions, and plot the time-frequency coherence spectrum; evaluate the degree of correlation between time series based on the local correlation coefficients and the time-frequency coherence spectrum, and determine whether the ship-bridge collision damage has occurred.

[0061] S4: If a collision occurs, calculate the time-frequency coherence loss factor at different positions in the time series matrix and plot the time-frequency coherence loss factor trajectory curve; determine the collision location and degree of collision based on the maximum value of the time-frequency coherence loss factor trajectory curve.

[0062] Specifically, in S1, the acceleration response signal array at different heights and locations of the bridge impacted by the ship is collected, including the following steps:

[0063] Based on the characteristics of the target bridge pier, a wall-climbing robot is installed. The wall-climbing robot consists of multiple acceleration sensors, climbing units, and control units. If the number of sensors is sufficient to cover the area, a digital inspection skin can be established through a single vertical scan by the wall-climbing robot.

[0064] The robot is driven to move along the bridge pier from top to bottom or bottom to top to scan the pier. It collects acceleration response signal arrays at different heights and positions of the pier through acceleration sensors and generates a time series matrix.

[0065] Furthermore, in S1, the solution for the time series matrix is ​​shown in the following equation:

[0066] X ij = [x1x2...x l ]

[0067] Where m is the total number of sensor layers on the bridge pier, 1≤i≤m; n is the total number of sensors in each array layer, 1≤j≤n; and l is the total length of the time series data collection.

[0068] Furthermore, the local correlation coefficient in S3 is calculated as follows:

[0069]

[0070] In the formula, R 2 (a,b) are the time-frequency local correlation coefficients between the two sequences, and S is the smoothing operator. and They are time series X ij and Xik The wavelet coefficients are denoted as follows: a is the scaling factor of the time-frequency transform, and b is the translation factor.

[0071] In the formula, when j≠k, It is about time series X ij and X ik The cross wavelet transform function, Indicates its cross-wavelet power; when j = k, It is about time series X ij and X ik The autocorrelation wavelet transform function is shown in the following equation:

[0072]

[0073] In the formula, * denotes complex conjugation.

[0074] Specifically, wavelet functions are used as basis functions to calculate the time-frequency coherence value R between time series. 2 Undetermined wavelet coefficients in (a,b) As shown in the following formula:

[0075]

[0076] In the formula, ψ is the selected wavelet basis function.

[0077] Furthermore, in S2, the calculation of the coherence spectral entropy includes the following steps:

[0078] The time-frequency coherence spectrum is divided into N equal time-frequency planes. The number of correlation coefficients passing the 95% significance test within each time-frequency plane is ζ. w The total number A of the entire time-frequency coherence spectrum that passes the 95% significance test (w = 1, ..., N) is normalized for each region to obtain:

[0079]

[0080] Based on the information entropy approach, the coherence spectral entropy H of the time-frequency coherence spectrum is calculated using the following formula. c :

[0081]

[0082] Among them, the coherence spectral entropy H c The entropy value indicates the amount of information contained in the time-frequency coherence spectrum. The larger the entropy value, the greater the amount of information contained in the time-frequency coherence spectrum, and the higher its information value.

[0083] Furthermore, in S4, the calculation of the time-frequency coherence loss factor includes the following steps:

[0084] The time-frequency coherence loss factor is defined as follows:

[0085] ∈=(AWC Intact -AWC Input ) / AWC Intact

[0086] In the formula, AWC Intact AWC refers to the average value of the time-frequency coherence of a non-damaging structure within a certain time-frequency range; Input It refers to the average value of the time-frequency coherence of the structure under test within a certain time-frequency range.

[0087] Furthermore, in S4, the location and extent of the collision are determined based on the peak value of the time-frequency coherence loss factor trajectory curve, including the following steps:

[0088] Based on the local correlation coefficient of the time series matrix in the time-frequency space, the Monte Carlo method is used to calculate the coherence of the time series at the 95% significance level, and the time-frequency coherence spectrum is plotted.

[0089] Among them, the time-frequency coherence spectrum fluctuates within a certain time-frequency range. When the degree of fluctuation cannot be restored to a stable state, it is determined that the ship-bridge collision has occurred. The degree of fluctuation is represented by the ratio of the number of time-frequency coherence spectra that pass the 95% significance test to the total number of spectra.

[0090] Based on the characteristics of the change in the time-frequency coherent loss factor at the collision damage location, a trajectory curve of the time-frequency coherent loss factor is plotted. When a bridge is damaged, the time-frequency coherent loss factor increases sharply near the collision damage location, while the loss factor is almost zero far from the collision damage location. The level at which the curve shows the maximum value is considered the collision damage location. Furthermore, as the bridge damage deepens, the increase in the time-frequency coherent loss factor becomes significant, effectively identifying the location and extent of the collision damage between the ship and the bridge.

[0091] In this embodiment, the feasibility of the present invention is verified by conducting response analysis of bridge piers under ship collision load:

[0092] S1: Construct a numerical model of the bridge pier. The finite element model of the pier has the following dimensions: length (L=6m), width (W=3m), and height (H=16m). The bottom is fixed and the top is free.

[0093] S2: Based on the characteristics of collision damage to the bridge, the damage is set as a breathing crack.

[0094] The state of a crack in a structure is related to the stress it experiences. Generally, cracks are not always open, but rather exhibit a periodic opening and closing nonlinear behavior in response to structural vibrations. The opening and closing behavior of breathing cracks is usually considered as a contact problem between the surfaces where the crack is located, meaning its behavior can be defined using surface-to-surface contact.

[0095] In surface-to-surface contact, the target surface is allowed to penetrate the contact surface, but the contact surface is not allowed to penetrate the target surface. During the vibration of the pier column, the breathing crack exists in three states: fully open (no contact between nodes), transitional (partial contact between nodes), and fully closed (complete contact between nodes).

[0096] S3: The crack is located at a height h = 4.5m from the bottom of the pier. The crack length is set to a, the width to b, and it is not allowed to propagate. For ease of definition, the damage level is defined as follows:

[0097]

[0098] In the formula, p is the relative depth of the crack, a is the crack length, and L is the pier length. Three damage levels were set, namely p=0.2, p=0.5, and p=0.8, and an undamaged pier was set as a control.

[0099] S4: Apply a uniformly distributed force F near the crack location, where F is the bridge impact force obtained during the bridge collision simulation.

[0100] S5: Install a wall-climbing robot on the target bridge pier. Set up sensor data acquisition devices on each side of the pier, specifically collecting the acceleration response at symmetrical nodes 1, 3, 2, and 4 at each height. The wall-climbing robot moves from top to bottom, collecting data once at 2-meter intervals. Define different height positions as a, b, c, ..., i, for a total of 9 height positions, constructing a 9*4 acceleration time series H. The sensor acquisition positions are as follows... Figure 3 As shown.

[0101] S6: Taking the symmetric nodes 2 and 4 at height position f as an example, plot the time-frequency coherence spectra of the two node sequences under different basis functions, and calculate their coherence spectral entropy H respectively. c ,like Figure 4 As shown, where Figure 4 (a) represents the Morlet wavelet basis function with a coherence spectral entropy of 10.6157. Figure 4 (b) is the Dog wavelet basis function with a coherence spectral entropy of 6.8329; Figure 4 (c) is a 4th-order Paul wavelet basis function with a coherence spectral entropy of 8.2761. As can be observed from the figure, (a) has more fluctuation information compared to (b) and (c), which is also important information for judging bridge collision damage. Therefore, the optimal basis function is selected to calculate the local coefficients of the time series in the time-frequency space.

[0102] S7: Taking symmetric nodes 2 and 4 as examples, calculate the local correlation coefficient of the time series in the time-frequency space under the optimal basis function, and plot the time-frequency coherence spectrum. For example... Figure 5 As shown, Figure 5 (a) is an undamaged pier. Figure 5(b) shows a damaged pier. In the undamaged state, the time-frequency coherence spectrum of the pier structure shows no fluctuations, and the local time-frequency correlation coefficient is 1. In contrast, the time-frequency coherence spectrum of the damaged pier exhibits periodic fluctuations within a certain time-frequency range. This is due to the nonlinear behavior of the breathing cracks during pier vibration. Based on this, the method can clearly determine the occurrence of ship-bridge collision damage.

[0103] S8: If a collision damage to the bridge is determined, calculate the time-frequency coherence loss factor ∈ and plot the loss factor trajectory curve. From Figure 6 , 7 It can be seen that under undamaged conditions, the time-frequency loss factor is almost zero. As the degree of cracking increases, the fluctuation of the time-frequency coherence spectrum deepens, and the time-frequency loss factor between different nodes gradually increases, which can effectively characterize the degree of damage to the bridge. At the same time, the location of the impact can be accurately identified by highlighting the loss factor trajectory curves at different heights.

[0104] The above embodiments demonstrate the effectiveness of the present invention in identifying the characteristics of ship-bridge collision damage, and provide fundamental methodological support for establishing ship-bridge collision damage assessment technology.

[0105] The above is one embodiment of the bridge damage assessment method caused by ship collisions provided in this embodiment. Based on the same idea, this embodiment also provides a corresponding bridge damage assessment system caused by ship collisions. Specific limitations of the bridge damage assessment system caused by ship collisions can be found in the limitations of the bridge damage assessment method caused by ship collisions described above, and will not be repeated here. Each module in the above-described bridge damage assessment system caused by ship collisions can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0106] This embodiment also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The provided method for assessing bridge damage caused by ship collisions.

[0107] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0108] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing bridge damage caused by ship collision, characterized in that, Includes the following steps: Acceleration response signal arrays at different heights and positions of bridge piers impacted by ships are collected, and a time series matrix of acceleration response signals is generated based on the acceleration response signal arrays; Based on the time series matrix, the time-frequency coherence spectrum under different basis functions is plotted, and the coherence spectrum entropy is obtained; Based on the principle of information entropy, the information volume of the time-frequency coherence spectrum under different basis function spaces is measured according to the coherence spectrum entropy, and the optimal basis function is determined. Obtain the local correlation coefficients of the time series matrix in the time-frequency space under the optimal basis functions, and plot the time-frequency coherence spectrum; evaluate the degree of correlation between time series based on the local correlation coefficients and the time-frequency coherence spectrum, and determine whether the ship-bridge collision damage has occurred; If collision occurs, calculate the time-frequency coherence loss factor at different positions in the time series matrix and plot the trajectory curve of the time-frequency coherence loss factor; The location and extent of collision damage are determined by the point of the maximum value of the time-frequency coherence loss factor trajectory curve. The calculation of the time-frequency coherence loss factor includes the following steps: Time-frequency coherence loss factor ϵ The definition is as follows: ； In the formula, It refers to the average value of the time-frequency coherence of a non-damaging structure within a certain time-frequency range; It refers to the average value of the time-frequency coherence of the structure under test within a certain time-frequency range; The method of determining the collision location and degree based on the maximum value of the time-frequency coherence loss factor trajectory curve includes the following steps: Based on the local correlation coefficient of the time series matrix in the time-frequency space, the Monte Carlo method is used to calculate the coherence of the time series at the 95% significance level, and the time-frequency coherence spectrum is plotted. Among them, the time-frequency coherence spectrum fluctuates within a certain time-frequency range. When the degree of fluctuation cannot be restored to a stable state, it is determined that the ship-bridge collision has occurred. The degree of fluctuation is represented by the ratio of the number of time-frequency coherence spectra that pass the 95% significance test to the total number. Based on the characteristics of the change in time-frequency coherent loss factor at the collision damage location, a trajectory curve of the time-frequency coherent loss factor is plotted. When the bridge is damaged, the time-frequency coherent loss factor near the collision damage location will suddenly increase, while the loss factor far away from the collision damage location will approach 0. The level where the curve's maximum value is located is considered to be the collision damage location.

2. The method for assessing bridge damage caused by ship collisions according to claim 1, characterized in that, The process of collecting acceleration response signals from bridge piers impacted by ships at different heights and locations includes the following steps: A wall-climbing robot is installed according to the characteristics of the target bridge pier; at least one acceleration sensor is evenly spaced on each side of the wall-climbing robot facing the bridge pier. The robot is driven to move along the bridge pier from top to bottom or bottom to top to scan the pier. It collects acceleration response signal arrays at different heights and positions of the pier through acceleration sensors and generates a time series matrix.

3. The method for assessing bridge damage caused by ship collisions according to claim 2, characterized in that, The time series matrix is ​​shown in the following formula: ， ； in, m The total number of sensor layers installed on the bridge piers. ; n The total number of sensors in each layer of the array. ; This represents the total length of the collected time series.

4. The method for assessing bridge damage caused by ship collisions according to claim 1, characterized in that, The local correlation coefficient is calculated as follows: ； In the formula, It is the time-frequency local correlation coefficient between two sequences. For smoothing operators, and Time series and wavelet coefficients, It is the scaling factor of the time-frequency transform. The translation factor; In the formula, when hour, It's about time series. and The cross wavelet transform function, Indicates its cross-wavelet power; when , It's about time series. and The autocorrelation wavelet transform function is shown in the following equation: ； In the formula, Indicates complex conjugation.

5. The method for assessing bridge damage caused by ship collision according to claim 4, characterized in that, Also includes: Wavelet functions are used as basis functions to calculate the time-frequency coherence values ​​between time series. Undetermined wavelet coefficients , As shown in the following formula: ； In the formula, It is the selected wavelet basis function.

6. The method for assessing bridge damage caused by ship collision according to claim 5, characterized in that, The calculation of the coherent spectral entropy includes the following steps: Divide the time-frequency coherence spectrum into equal parts. N Given time-frequency planes of equal area, the number of correlation coefficients passing the 95% significance test within each time-frequency plane is... The total number of the entire time-frequency coherence spectrum that passed the 95% significance test A After normalizing each region, we get: ， ； Based on the information entropy approach, the coherence spectral entropy of the time-frequency coherence spectrum is calculated using the following formula. : ； Among them, coherence spectral entropy The entropy value indicates the amount of information contained in the time-frequency coherence spectrum. The larger the entropy value, the greater the amount of information contained in the time-frequency coherence spectrum, and the higher its information value.

7. A bridge damage assessment system caused by ship collision, characterized in that, The system includes: processor; A memory on which computer programs that can run on the processor are stored; When the computer program is executed by the processor, it implements the steps of the bridge damage assessment method caused by ship collision as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a data processing program, which, when executed by a processor, implements the steps of the bridge damage assessment method caused by ship collision as described in any one of claims 1 to 6.

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