A method, equipment, and medium for assessing the vulnerability of bridge piers to ship impact.

Abstract

This invention discloses a method for assessing the vulnerability of bridge piers under ship impact, comprising: obtaining the tensile stress threshold at the pier base for different failure states of the bridge pier under ship impact; extracting multiple parameter combinations from the core parameter space affecting the tensile stress at the pier base using the optimal Latin hypercube sampling method; calculating the tensile stress at the pier base corresponding to each parameter combination using a finite element model of the bridge pier under ship impact, and fitting an initial surrogate model for the tensile stress at the pier base; based on the prediction accuracy of the initial surrogate model, supplementing the extraction of multiple parameter combinations in the parameter space region where the prediction error or the degree of nonlinearity of the function is greater than the corresponding threshold; and using the supplemented extracted parameter combinations to return to the operation of calculating using the finite element model to obtain a new surrogate model; repeating this process until the prediction accuracy of the final surrogate model meets the requirements; and fitting the pier vulnerability curve for each failure state. This embodiment is more consistent with the damage mechanism of concrete cracking and structural failure.

Description

Technical Field

[0001] This invention relates to the field of bridge vulnerability assessment technology, and in particular to a method, equipment and medium for assessing the vulnerability of bridge piers under ship impact. Background Technology

[0002] With the development of the transportation industry, large-scale cross-river and cross-sea bridges have entered a phase of intensive construction. These projects are not only technically complex but also bring new challenges to maritime traffic safety. Bridge piers, as permanent obstacles in waterways, directly affect the safety of ship navigation. Especially with the development of shipping technology and the surge in transportation demand, ships are increasingly becoming larger, faster, and more densely packed, significantly increasing the risk of ship-bridge collisions. In waters with complex navigation environments and high ship traffic, the types of collision accidents are becoming more diverse, and the consequences are more severe. Therefore, conducting vulnerability assessments for bridge-to-pier collisions is of great significance for ensuring maritime traffic safety and the integrity of infrastructure.

[0003] Currently, assessments of bridge pier vulnerability to ship collisions primarily focus on pier vulnerability under seismic loading and the overall bridge response to ship collisions, lacking a systematic assessment method for the localized vulnerability of piers under ship collision loads. For example, patent CN120493657A proposes a method for assessing the impact toughness of corroded reinforced concrete bridge structures after a ship collision, but it does not extend to the pier level, making accurate localized assessments difficult. While patents CN120217518A and CN120493365A address the probability of damage and failure of various bridge components during earthquakes, including pier vulnerability assessments, they do not cover the effects of ship collisions. Although ship collisions and earthquakes are both low-probability, high-risk loads, they differ fundamentally in their mechanisms of action, uncertainties, and structural responses: earthquakes are sustained, overall vibrations lasting tens of seconds, easily leading to overall structural instability; ship collisions, on the other hand, are localized, instantaneous impacts lasting only 1-2 seconds, with the force concentrated on the piers and abutments, making them more prone to localized damage. Therefore, directly applying seismic vulnerability methods to assess the safety of bridge piers in the event of a ship collision has significant limitations.

[0004] Current risk assessments for ship collisions with bridges often rely on empirical guidelines like AASHTO or complex finite element simulations. The former lacks accuracy and ignores parameter uncertainties and the influence of pier shape, while the latter is computationally inefficient and difficult to perform probabilistic analysis. Summary of the Invention

[0005] This invention provides a method, equipment, and medium for assessing the vulnerability of bridge piers under ship impact, in order to solve at least one of the above-mentioned problems.

[0006] In a first aspect, embodiments of the present invention provide a method for assessing the vulnerability of bridge piers under ship impact, comprising:

[0007] Obtain the tensile stress threshold at the bottom of the bridge pier under different damage states of the pier after ship impact;

[0008] Using the optimal Latin hypercube sampling method, multiple parameter combinations are extracted from the core parameter space that affects the tensile stress at the bottom of the pier. The core parameters include concrete compressive strength, impact height, and impact kinetic energy.

[0009] Using a finite element model of a bridge pier under ship impact, the tensile stress at the bottom of the pier corresponding to various parameter combinations is calculated. Based on the calculation results and the Kriging model, an initial surrogate model for the tensile stress at the bottom of the pier is fitted.

[0010] Based on the prediction accuracy of the initial surrogate model, in the parameter space region where the prediction error or the degree of nonlinearity of the function is greater than the corresponding threshold, multiple parameter combinations are extracted; and the operation of calculating the tensile stress at the bottom of the pier using the finite element model is returned using the extracted parameter combination set to obtain a new surrogate model.

[0011] This process is repeated until the prediction accuracy of the final surrogate model meets the requirements.

[0012] For each failure state, the following steps are taken: large-scale sampling is performed from the parameter space, and the tensile stress at the bottom of the pier corresponding to each sampling point is predicted using the final surrogate model; based on the tensile stress threshold at the bottom of the pier in the current failure state, it is determined whether each prediction result causes structural failure; and based on the number of structural failures under the same impact kinetic energy, the vulnerability curve of the pier in the current failure state is determined, wherein the curve is used to characterize the change law of structural failure probability with impact kinetic energy.

[0013] Secondly, embodiments of the present invention provide a system for assessing the vulnerability of bridge piers under ship impact, comprising:

[0014] The failure threshold acquisition module is used to acquire the tensile stress threshold at the bottom of the bridge pier under different damage states of the pier under ship impact.

[0015] The parameter sampling module is used to extract multiple parameter combinations from the core parameter space affecting the tensile stress at the bottom of the pier using the optimal Latin hypercube sampling method. The core parameters include concrete compressive strength, impact height, and impact kinetic energy.

[0016] The surrogate model building module is used to calculate the tensile stress at the bottom of the pier corresponding to various parameter combinations using the finite element model of the pier under ship impact, and to fit an initial surrogate model of the tensile stress at the bottom of the pier based on the calculation results and the Kriging model.

[0017] The surrogate model optimization module is used to extract multiple parameter combinations in the parameter space region where the prediction error or the degree of nonlinearity of the function is greater than the corresponding threshold, based on the prediction accuracy of the initial surrogate model; and use the extracted parameter combination set to return the operation of calculating the tensile stress at the bottom of the pier using the finite element model to obtain a new surrogate model; this process is repeated until the prediction accuracy of the final surrogate model meets the requirements.

[0018] The vulnerability curve construction module is used to perform the following for each failure state: large-scale sampling from the parameter space; predicting the tensile stress at the bottom of the pier corresponding to each sampling point using the final surrogate model; determining whether each prediction result causes structural failure based on the tensile stress threshold at the bottom of the pier in the current failure state; and determining the pier vulnerability curve for the current failure state based on the number of structural failures under the same impact kinetic energy. The curve is used to characterize the change law of structural failure probability with impact kinetic energy.

[0019] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0020] One or more processors;

[0021] Memory, used to store one or more programs.

[0022] When the one or more programs are executed by the one or more processors, the one or more processors implement the vulnerability assessment method for bridge piers under ship impact as described in any embodiment.

[0023] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for assessing the vulnerability of bridge piers under ship impact as described in any embodiment.

[0024] In summary, this invention provides an efficient method for assessing the impact vulnerability of bridge piers using tensile stress at the pier base as a damage indicator. It uses the maximum principal tensile stress at the pier base caused by barge impact as a direct physical indicator for damage assessment. Through coupled parameter adaptive experimental design, a high-precision machine learning surrogate model, large-scale sampling, and system reliability analysis, it achieves a refined analysis of the entire process of pier impact resistance performance, from global sensitivity to local probability assessment. This method not only significantly improves computational efficiency and assessment accuracy but also deeply explores the influence patterns of design parameters, providing quantitative decision support for the impact resistance design and protection of bridge piers. Specifically:

[0025] 1. Existing vulnerability studies mostly focus on overall displacement or shear force indicators, while there is insufficient research on the damage mechanism of tensile stress at the bottom of the pier, which directly reflects concrete cracking and structural failure. In this embodiment, the maximum principal tensile stress at the bottom of the pier caused by barge impact is used as a direct physical indicator for damage assessment, which can better reflect the pier damage mechanism.

[0026] 2. This embodiment constructs a refined assessment method for the impact vulnerability of piers based on a machine learning surrogate model and reliability theory. This method uses the tensile stress at the pier base as a direct damage indicator, employs optimal Latin hypercube sampling for initialization, and automatically supplements samples based on prediction errors. Through iterative training, a high-precision Kriging surrogate model is constructed, achieving accurate mapping of the complex nonlinear relationship of the impact response with minimal computation. It also overcomes the accuracy bottleneck of traditional polynomial response surface models when fitting highly nonlinear problems, thus improving the accuracy and reliability of vulnerability curve prediction. Attached Figure Description

[0027] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0028] Figure 1 This is a flowchart of a method for assessing the vulnerability of bridge piers under ship impact, provided by an embodiment of the present invention;

[0029] Figure 2 This is a flowchart of another method for assessing the vulnerability of bridge piers under ship impact, provided by an embodiment of the present invention;

[0030] Figure 3 This is a schematic diagram of a bridge pier vulnerability assessment system under ship impact provided in an embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0033] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0034] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0035] Figure 1 This is a flowchart illustrating a method for assessing the vulnerability of bridge piers under ship impact, provided in an embodiment of the present invention. The method is executed by electronic equipment, such as... Figure 1 As shown, the method specifically includes:

[0036] S110. Obtain the tensile stress threshold at the bottom of the pier under different damage states of the pier under ship impact.

[0037] This step uses the tensile stress at the bottom of the pier as an indicator of damage and determines the vulnerability failure criterion for ship-collision bridge based on the tensile stress at the bottom of the pier.

[0038] In one specific implementation, the impact strength index is first determined. Optionally, given the deterministic condition of the ship impact strength A, the vulnerability of the bridge pier under ship collision can be quantitatively defined, and the barge impact kinetic energy E can be selected as the index parameter A for measuring the impact strength.

[0039] Then, the engineering requirements parameters and failure criteria are determined. Specifically, in this embodiment, the maximum principal tensile stress σ at the bottom of the pier caused by barge impact is used. t As a core damage indicator, five levels of ultimate limit states (or failure states) and their corresponding tensile stress thresholds L at the pier bottom can be defined. s For example, L0, L1, L2, L3, and L4 correspond to five states: no damage, minor damage, moderate damage, severe damage, and collapse, respectively.

[0040] Table 1 Damage classification of bridge piers in equivalent model

[0041]

[0042] In other words, the tensile stress threshold at the bottom of the pier under mild damage is the tensile strength of the concrete; the tensile stress threshold at the bottom of the pier under moderate damage is half of the standard tensile strength; the tensile stress threshold at the bottom of the pier under severe damage is the standard tensile strength; and the tensile stress threshold at the bottom of the pier under collapse is much greater than the standard tensile strength.

[0043] A function can be established based on the above thresholds. Optionally, for a certain limiting state, the threshold L... s Its function Z can be defined as:

[0044]

[0045] Where, σ t < L s Indicates structural safety (Z > 0); σ t ≥ L s This indicates structural failure (Z ≤ 0).

[0046] Simultaneously, a vulnerability function can be defined. The vulnerability F of the response is defined as the structural response reaching or exceeding a certain limit state L under a given impact kinetic energy E=a. s The conditional probability P:

[0047]

[0048] S120. Using the optimal Latin hypercube sampling method, multiple parameter combinations are extracted from the core parameter space that affects the tensile stress at the bottom of the pier. The core parameters include concrete compressive strength, impact height, and impact kinetic energy.

[0049] This step focuses on identifying key parameters and performing sequential sampling for the tensile stress at the bottom of the pier, a core indicator for assessing damage.

[0050] Optionally, through literature review and preliminary parameter analysis, the factors affecting the tensile stress σ at the pier bottom can be determined. t The core parameter is: concrete compressive strength f c The impact height h and impact kinetic energy E are considered. Within the parameter space defined by these three parameters, initial sample points are generated using the Optimal Latin Hypercube Sampling (OLHS) method. OLHS achieves a uniform and unbiased sample distribution within the parameter space, capturing overall spatial characteristics better than traditional BBD design, and is particularly suitable for nonlinear problems involving ship-bridge collision dynamic parameters.

[0051] S130. Using the finite element model of the bridge pier under ship impact, calculate the tensile stress at the bottom of the pier corresponding to each parameter combination, and fit an initial surrogate model of the tensile stress at the bottom of the pier based on the calculation results and the Kriging model.

[0052] This step utilizes the finite element model and calculates the response at sample points, and then constructs an initial surrogate model based on the Kriging model based on the response at the sample points.

[0053] In one specific implementation, a parametric finite element model is first constructed, that is, a refined nonlinear finite element model of ship-bridge collision is established. The model supports the cross-sectional dimensions of the bridge piers and material properties (f... c The parameterization and automatic modification of the impact location (h) and initial velocity (determined E) are then implemented. Based on the sample point scheme generated in S120, finite element calculations are submitted in batches using a script, automatically extracting and storing the maximum tensile stress response value σ at the pier bottom for each working condition. t This forms a database used to train agent models.

[0054] Next, the Kriging model is used as the core surrogate model. The Kriging model not only provides predicted values ​​for unknown points but also gives the mean squared error of the predictions, making it a global approximation method with statistical inference capabilities. Its model expression is as follows:

[0055]

[0056] in, It is a polynomial trend function, usually a constant or a linear term; The mean is zero and the variance is σ. 2 The spatial correlation of a Gaussian random process is determined by the correlation function.

[0057] By substituting the parameter combinations from the above sample points as independent variables x, and the tensile stress at the pier bottom calculated using the finite element model corresponding to each parameter combination as the dependent variable y, into the Kriging model, and training the parameters in the model, an initial surrogate model for the tensile stress at the pier bottom can be obtained. The excellent nonlinear fitting ability of the Kriging model can more accurately capture f. c h, E and σ t The complex mapping relationship between them.

[0058] S140. Based on the prediction accuracy of the initial surrogate model, in the parameter space region where the prediction error or the degree of nonlinearity of the function is greater than the corresponding threshold, multiple parameter combinations are extracted; and the operation of calculating the tensile stress at the bottom of the pier using the finite element model is returned using the extracted parameter combination set to obtain a new surrogate model; this process is repeated until the prediction accuracy of the final surrogate model meets the requirements.

[0059] This step verifies the accuracy of the initial surrogate model and further improves the model accuracy through adaptive sample point supplementation design.

[0060] Optionally, leave-one-out cross-validation (LOOCV) and the coefficient of determination R can be used. 2 The accuracy of the initial surrogate model is comprehensively evaluated using various metrics. If the accuracy does not meet the requirements, new sample points can be automatically added in regions with large prediction errors or high nonlinearity of the function, based on the prediction results of the initial surrogate model. Then, a new surrogate model is retrained using all the added sample point sets. This process is iterated until the prediction accuracy of the surrogate model meets the requirements, thereby achieving convergence to a high-precision model in the most efficient way.

[0061] S150. For each failure state, respectively: large-scale sampling is performed from the parameter space, and the tensile stress at the bottom of the pier corresponding to each sampling point is predicted using the final surrogate model; based on the tensile stress threshold at the bottom of the pier in the current failure state, it is determined whether each prediction result causes structural failure; and based on the number of structural failures under the same impact kinetic energy, the vulnerability curve of the pier in the current failure state is determined, wherein the curve is used to characterize the change law of structural failure probability with impact kinetic energy.

[0062] After obtaining the high-precision surrogate model, this step constructs a pier vulnerability curve based on Monte Carlo sampling. This curve uses impact kinetic energy as the independent variable and the failure probability of the pier structure as the dependent variable to evaluate the vulnerability of the pier under different impact kinetic energies.

[0063] In one specific implementation, the probability distributions of three core parameters affecting bridge pier damage are first determined, i.e., the key random variables (f) are identified. c The probability distribution type and parameters of (h, E).

[0064] Then, large-scale sampling and rapid prediction are performed based on the probability distribution. Optionally, the Monte Carlo method can be used to analyze the random variable f. c A large-scale random sampling is performed on h and E, with a total sample size of N=10. 6 The combined variables from each sampling are input into the trained Kriging surrogate model to instantly predict the corresponding tensile stress σ at the pier bottom. t .

[0065] Next, the failure probability is calculated based on the prediction results. Specifically, for a given impact kinetic energy 'a' and the limiting state L... s If the predicted tensile stress at the pier base exceeds the threshold tensile stress at the pier base in the current failure state, it is determined that the prediction caused structural failure. This allows for the statistical analysis of all failures (σ) in the sample. t ≥ L s The number of times N) failCalculate the conditional failure probability at the given kinetic energy 'a'. :

[0066]

[0067] Finally, vulnerability curves are generated. Optionally, by changing the magnitude of the impact kinetic energy E and repeating the steps of large-scale sampling and rapid prediction and calculation of failure probability based on the probability distribution, a series of failure probabilities corresponding to different kinetic energy points can be calculated. These points (a, P) f Connecting (a) gives the vulnerability curve of the bridge pier under a certain limit state. Repeating this process for multiple limit states yields a set of vulnerability curves.

[0068] Furthermore, based on the above high-precision surrogate model, global sensitivity analysis and parameter importance ranking can also be performed. Optionally, based on the constructed high-precision surrogate model, the variance-based Sobol sensitivity analysis method can be used to calculate the importance of each random variable (f). c The response of σ, h, E to the tensile stress at the pier bottom t The first-order and total-order sensitivity indices. This analysis can quantify:

[0069] First-order exponent S i : Measures the proportion of a single parameter's contribution to the response variance.

[0070] Total order exponent S Ti : Measures the proportion of the contribution of a single parameter to the variance of the response through its interaction with other parameters.

[0071] This step clarifies which parameters are the most critical factors affecting the collision resistance reliability of bridge piers, providing a clear direction for design optimization.

[0072] Furthermore, based on the above models and curves, system reliability assessments and design optimizations can also be performed. For example:

[0073] Conduct system failure probability assessment: Consider the correlation between multiple extreme states (such as minor damage, moderate damage, and severe damage) to assess the system failure probability of the bridge piers suffering different levels of damage under ship collision.

[0074] Reliability-based design optimization combines the aforementioned vulnerability model with optimization algorithms, using concrete strength, cross-sectional dimensions, etc., as design variables, and reliability indices as constraints or objective functions, to seek the most economical and safest design scheme under a given collision risk.

[0075] Visualization and Decision Support: Based on the visualization results such as generated vulnerable surfaces and sensitivity analysis radar charts, the impact resistance performance of bridge piers can be comprehensively evaluated, providing a clear and intuitive scientific basis for engineering decisions.

[0076] The entire process described above can be combined Figure 2 I understand.

[0077] In summary, this embodiment provides an efficient method for assessing the impact vulnerability of bridge piers using tensile stress at the pier base as a damage indicator. It uses the maximum principal tensile stress at the pier base caused by barge impact as a direct physical indicator for damage assessment. Through coupled parameter adaptive experimental design, high-precision machine learning surrogate models, Monte Carlo sampling, and system reliability analysis, it achieves a refined analysis of the entire process of pier impact resistance performance, from global sensitivity to local probability assessment. This method not only significantly improves computational efficiency and assessment accuracy but also deeply explores the influence patterns of design parameters, providing quantitative decision support for the impact resistance design and protection of bridge piers. Specifically:

[0078] 1. Existing vulnerability studies mostly focus on overall displacement or shear force indicators, while there is insufficient research on the damage mechanism of tensile stress at the bottom of the pier, which directly reflects concrete cracking and structural failure. In this embodiment, the maximum principal tensile stress at the bottom of the pier caused by barge impact is used as a direct physical indicator for damage assessment, which can better reflect the pier damage mechanism.

[0079] 2. This embodiment constructs a refined assessment method for the impact vulnerability of piers based on a machine learning surrogate model and reliability theory. This method uses the tensile stress at the pier base as a direct damage indicator, employs optimal Latin hypercube sampling for initialization, and automatically supplements samples based on prediction errors. Through iterative training, a high-precision Kriging surrogate model is constructed, achieving accurate mapping of the complex nonlinear relationship of the impact response with minimal computation. It also overcomes the accuracy bottleneck of traditional polynomial response surface models when fitting highly nonlinear problems, thus improving the accuracy and reliability of vulnerability curve prediction.

[0080] 3. This embodiment introduces variance-based global sensitivity analysis (Sobol index) to quantify and rank the importance of key parameters affecting the impact resistance performance of bridge piers. This method can not only assess the contribution of individual parameters such as concrete strength, impact height, and kinetic energy, but also accurately quantify the impact of multi-parameter interactions, thereby revealing the failure mechanism of ship collisions with bridge piers and providing a clear scientific basis for optimizing the seismic and impact-resistant design of bridge piers, surpassing traditional vulnerability analysis methods that only provide failure probabilities.

[0081] 4. This embodiment significantly improves the computational efficiency of the evaluation process and the scalability of the system. Through optimal Latin hypercube sampling and adaptive sampling strategies, a high-precision surrogate model can be constructed with the fewest finite element calculations, overcoming the drawback of the traditional Monte Carlo method's massive computational load when directly calling the finite element model. Simultaneously, this framework has good scalability, easily integrates other machine learning models, and can be further extended to consider more parameters such as pier dimensions, reinforcement ratio, and soil-structure interaction, demonstrating strong engineering applicability and promising prospects for widespread application.

[0082] Figure 3 This is a schematic diagram of a bridge pier vulnerability assessment system under ship impact provided by an embodiment of the present invention. Figure 3 As shown, the system includes:

[0083] The failure threshold acquisition module is used to acquire the tensile stress threshold at the bottom of the bridge pier under different damage states of the pier under ship impact.

[0084] The parameter sampling module is used to extract multiple parameter combinations from the core parameter space affecting the tensile stress at the bottom of the pier using the optimal Latin hypercube sampling method. The core parameters include concrete compressive strength, impact height, and impact kinetic energy.

[0085] The surrogate model building module is used to calculate the tensile stress at the bottom of the pier corresponding to various parameter combinations using the finite element model of the pier under ship impact, and to fit an initial surrogate model of the tensile stress at the bottom of the pier based on the calculation results and the Kriging model.

[0086] The surrogate model optimization module is used to extract multiple parameter combinations in the parameter space region where the prediction error or the degree of nonlinearity of the function is greater than the corresponding threshold, based on the prediction accuracy of the initial surrogate model; and use the extracted parameter combination set to return the operation of calculating the tensile stress at the bottom of the pier using the finite element model to obtain a new surrogate model; this process is repeated until the prediction accuracy of the final surrogate model meets the requirements.

[0087] The vulnerability curve construction module is used to perform the following for each failure state: large-scale sampling from the parameter space; predicting the tensile stress at the bottom of the pier corresponding to each sampling point using the final surrogate model; determining whether each prediction result causes structural failure based on the tensile stress threshold at the bottom of the pier in the current failure state; and determining the pier vulnerability curve for the current failure state based on the number of structural failures under the same impact kinetic energy. The curve is used to characterize the change law of structural failure probability with impact kinetic energy.

[0088] This embodiment is based on the same inventive concept as the above method embodiment. Any limitations in the above method embodiment are applicable to this embodiment and can achieve the same beneficial effects as the above method embodiment.

[0089] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 4 As shown, the device includes a processor 60, a memory 61, an input device 62, and an output device 63; the number of processors 60 in the device can be one or more. Figure 4 Taking a processor 60 as an example; the processor 60, memory 61, input device 62, and output device 63 in the device can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0090] The memory 61, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the ship impact vulnerability assessment method for bridge piers in this embodiment of the invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 61, thereby realizing the aforementioned ship impact vulnerability assessment method for bridge piers.

[0091] The memory 61 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 61 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include memory remotely located relative to the processor 60, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0092] Input device 62 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 63 may include display devices such as a display screen.

[0093] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vulnerability assessment method for bridge piers under ship impact according to any embodiment.

[0094] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0095] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0096] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0097] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as C or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the vulnerability of bridge piers under ship impact, characterized in that, include: Obtain the tensile stress threshold at the bottom of the bridge pier under different damage states of the pier after ship impact; Using the optimal Latin hypercube sampling method, multiple parameter combinations are extracted from the core parameter space that affects the tensile stress at the bottom of the pier. The core parameters include concrete compressive strength, impact height, and impact kinetic energy. Using a finite element model of a bridge pier under ship impact, the tensile stress at the pier bottom corresponding to various parameter combinations is calculated. Construct the following kriging model: ， in, It is a polynomial trend function, consisting of a constant or a linear term; The mean is zero and the variance is σ. 2 Gaussian random process; The combination of parameters is used as the independent variable x, and the tensile stress at the bottom of the pier calculated based on the combination of parameters is used as the dependent variable y. These are substituted into the Kriging model for training to obtain the initial surrogate model of the tensile stress at the bottom of the pier. Based on the prediction accuracy of the initial surrogate model, in the parameter space region where the prediction error or the degree of nonlinearity of the function is greater than the corresponding threshold, multiple parameter combinations are extracted; and the operation of calculating the tensile stress at the bottom of the pier using the finite element model is returned using the extracted parameter combination set to obtain a new surrogate model. This process is repeated until the prediction accuracy of the final surrogate model meets the requirements. For each failure state, the following steps are taken: large-scale sampling is performed from the parameter space, and the tensile stress at the bottom of the pier corresponding to each sampling point is predicted using the final surrogate model; based on the tensile stress threshold at the bottom of the pier in the current failure state, it is determined whether each prediction result causes structural failure; and based on the number of structural failures under the same impact kinetic energy, the vulnerability curve of the pier in the current failure state is determined, wherein the curve is used to characterize the change law of structural failure probability with impact kinetic energy.

2. The method for assessing the vulnerability of bridge piers under ship impact according to claim 1, characterized in that, The method for obtaining the tensile stress threshold at the bottom of the bridge pier under different damage states following a ship impact includes: The tensile stress threshold at the bottom of the pier under slightly damaged conditions is the tensile strength of the concrete. The tensile stress threshold at the bottom of the pier under moderate damage is half of the standard tensile strength. The tensile stress threshold at the bottom of the pier under severe damage is the standard tensile strength; The tensile stress threshold at the bottom of the pier in the collapsed state is much greater than the standard tensile strength before collapse.

3. The method for assessing the vulnerability of bridge piers under ship impact according to claim 1, characterized in that, The step of determining whether each prediction result will cause structural failure based on the tensile stress threshold at the current failure state of the pier includes: If the predicted tensile stress at the bottom of the pier is greater than the tensile stress threshold at the bottom of the pier in the current state of failure, it is determined that the prediction result of the predicted tensile stress at the bottom of the pier caused structural failure.

4. The method for assessing the vulnerability of bridge piers under ship impact according to claim 1, characterized in that, After this process is repeated until the prediction accuracy of the final surrogate model meets the requirements, the following steps are also included: The final surrogate model was analyzed using the variance-based Sobol sensitivity analysis method to calculate the first-order and summary sensitivity indices of each core parameter to the tensile stress at the pier bottom. Based on the various sensitivity indices, the key factors affecting the collision resistance reliability of bridge piers are calculated.

5. The method for assessing the vulnerability of bridge piers under ship impact according to claim 1, characterized in that, Also includes: Considering the correlation between multiple failure states, assess the system failure probability of bridge piers suffering different levels of damage under ship impact.

6. The method for assessing the vulnerability of bridge piers under ship impact according to claim 1, characterized in that, Also includes: The reliability index is calculated based on the vulnerability curve of the pier. With concrete strength and cross-sectional dimensions as design variables and the reliability index as a constraint or objective function, the optimal design scheme is sought under a given collision risk.

7. A vulnerability assessment system for bridge piers under ship impact, characterized in that, include: The failure threshold acquisition module is used to acquire the tensile stress threshold at the bottom of the bridge pier under different damage states of the pier under ship impact. The parameter sampling module is used to extract multiple parameter combinations from the core parameter space affecting the tensile stress at the bottom of the pier using the optimal Latin hypercube sampling method. The core parameters include concrete compressive strength, impact height, and impact kinetic energy. The proxy model building module is used to calculate the tensile stress at the bottom of the pier corresponding to various parameter combinations using the finite element model of the bridge pier under ship impact. Construct the following kriging model: ， in, It is a polynomial trend function, consisting of a constant or a linear term; The mean is zero and the variance is σ. 2 Gaussian random process; The combination of parameters is used as the independent variable x, and the tensile stress at the bottom of the pier calculated based on the combination of parameters is used as the dependent variable y. These are substituted into the Kriging model for training to obtain the initial surrogate model of the tensile stress at the bottom of the pier. The surrogate model optimization module is used to extract multiple parameter combinations in the parameter space region where the prediction error or the degree of nonlinearity of the function is greater than the corresponding threshold, based on the prediction accuracy of the initial surrogate model; and use the extracted parameter combination set to return the operation of calculating the tensile stress at the bottom of the pier using the finite element model to obtain a new surrogate model; this process is repeated until the prediction accuracy of the final surrogate model meets the requirements. The vulnerability curve construction module is used to perform the following for each failure state: large-scale sampling from the parameter space; predicting the tensile stress at the bottom of the pier corresponding to each sampling point using the final surrogate model; determining whether each prediction result causes structural failure based on the tensile stress threshold at the bottom of the pier in the current failure state; and determining the pier vulnerability curve for the current failure state based on the number of structural failures under the same impact kinetic energy. The curve is used to characterize the change law of structural failure probability with impact kinetic energy.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for assessing the vulnerability of bridge piers under ship impact as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method for assessing the vulnerability of bridge piers under ship impact as described in any one of claims 1-6.

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