A caisson wharf earthquake stability evaluation system and method based on digital twinning

Abstract

This invention belongs to the field of port engineering structural health monitoring and numerical simulation technology, specifically relating to a digital twin-based seismic stability assessment system and method for caisson wharves. The system includes a data acquisition and processing module, a model update module, and a performance evaluation and early warning module; the method includes data perception and feature extraction, parallel model calibration and update, coupled analysis, and intelligent early warning. This invention establishes a high-fidelity digital twin model that is synchronously updated with the physical entity and utilizes high-performance parallel computing technology to achieve real-time and accurate assessment and early warning of the stability of caisson wharves under combined seismic and wave effects.

Description

Technical Field

[0001] This invention belongs to the field of port engineering structural health monitoring and numerical simulation technology, specifically relating to a seismic stability assessment system and method for caisson wharves based on digital twins, and more particularly to a seismic stability assessment system and method for caisson wharves that integrates the open-source numerical simulation platform (OpenSees), digital twin technology and parallel optimization algorithms. Background Technology

[0002] Caisson wharves are critical structures in port engineering, and their stability under the coupled effects of multiple hazards such as earthquakes and waves is of paramount importance. Traditional assessment methods mainly rely on periodic manual inspections, simplified calculations based on fixed design parameters, or numerical simulations. These methods have significant limitations: manual inspections are inherently lagging; simplified calculations fail to reflect the nonlinearity of soil-structure interactions and the liquefaction effect of foundation soil; and the model parameters used in conventional numerical simulations remain unchanged over long periods, failing to reflect the degradation of structural material properties under long-term corrosion, erosion, and fatigue loads, leading to a gradual mismatch between the digital model and the physical entity, and a decline in the reliability of the assessment results over time.

[0003] Digital twin technology offers a new paradigm for the full lifecycle management of structures. While existing research has attempted to create digital twins of structures, most focuses on data visualization and status monitoring, failing to achieve the reverse-driving and automatic updating of core physical parameters of the simulation model based on monitoring data. In other words, it lacks the self-evolutionary capability to maintain high fidelity in the twin. Furthermore, the process of optimizing model parameters often involves enormous computational demands, and using a serial computing model is inefficient and fails to meet the timeliness requirements of engineering practice. Therefore, there is an urgent need for a system and method that can achieve monitoring data-driven, automatic online updating of model parameters, and real-time stability assessment based on high-performance computing. Summary of the Invention

[0004] This invention discloses a digital twin-based seismic stability assessment system and method for caisson wharves. The system establishes a high-fidelity digital twin model that is updated synchronously with the physical entity and utilizes high-performance parallel computing technology to achieve real-time and accurate assessment and early warning of the stability of caisson wharves under combined seismic and wave action.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: A seismic stability assessment system for caisson wharves based on digital twins includes a data acquisition and processing module, a model update module, and a performance assessment and early warning module. The data acquisition and processing module is used to collect dynamic response data of the caisson wharf under environmental and load excitation in real time, and to process the data for structural feature parameters used in model updates. The model update module is used to establish a parametric finite element model of the caisson wharf based on the design data, and to use the obtained structural feature parameters to drive a parallel optimization algorithm to automatically calibrate the physical parameters in the model and establish a digital twin model consistent with the current physical state. The performance evaluation and early warning module is used to perform nonlinear dynamic time history analysis based on the updated digital twin model, applying seismic and wave coupled loads, evaluating structural stability based on the analysis results, and providing visual early warning.

[0006] A method for using a digital twin-based seismic stability assessment system for caisson wharves includes the following steps: Step 1: Collect vibration response data of the caisson wharf under wave, earthquake, and wind excitation, perform noise reduction and feature recognition processing on the data to obtain structural feature parameters, and store the data; Step 2: Based on the design data of the caisson wharf, a three-dimensional parametric finite element model is established in the OpenSees platform; the structural feature parameters obtained in Step 1 are extracted, the physical parameters of the model that need to be updated and their value ranges are defined, the objective function is constructed based on the error between the measured and simulated modal parameters, and the model parameters are iteratively updated using a parallel computing cluster-driven optimization algorithm to establish a digital twin model; Step 3: Based on the digital twin model established in Step 2, define the engineering requirement parameters and performance thresholds for caisson displacement and soil strain; input the design ground motion and wave load, conduct nonlinear time history analysis, and calculate the various responses of the structure; evaluate the stability state and trigger corresponding early warnings based on the comparison between the response results and the performance thresholds.

[0007] Preferably, step 1 includes the following specific steps: (1) Deploy a sensor network at key locations on the dock to collect acceleration and displacement data; (2) Data noise reduction is performed using Kalman filtering, and random decrementing technique and Hilbert transform are used to identify the natural frequency, damping ratio and mode shape of the structure from the environmental vibration response as structural characteristic parameters.

[0008] Preferably, step 2 includes the following specific steps: (1) Establish a wharf-foundation model in OpenSees, and set the elastic modulus of soil, elastic modulus of concrete, and contact surface properties as key physical parameters to be optimized variables X; (2) The model update process adopts the particle swarm optimization (PSO) algorithm and deploys the PSO task in parallel on a high-performance computing cluster. The objective function F(X) is the least square error between the measured and simulated natural frequencies. The modal confidence criterion (MAC) is introduced to match numerical simulation and measured mode shapes to identify the mode shape. In each generation, the master node encapsulates 40 sets of X into independent OpenSees analysis tasks and submits them to the cluster nodes for execution in parallel. (3) All nodes synchronously calculate the objective function value and return it. This process is repeated until convergence, and the optimal parameter X is output. opt Substituting the data into the model yields a calibrated digital twin model that matches the current physical state.

[0009] Preferably, in step 3, the performance evaluation, in addition to outputting the overall indicators of the caisson top displacement and base shear force, also calculates the excess pore water pressure ratio of the soil elements and uses color gradients to visualize and mark the liquefaction risk areas in the three-dimensional model to achieve damage location.

[0010] The beneficial effects of the digital twin-based seismic stability assessment system and method for caisson wharves of this invention are as follows: This invention establishes a high-fidelity digital twin model that is updated synchronously with the physical entity, and utilizes high-performance parallel computing technology to achieve real-time and accurate assessment and early warning of the stability of caisson wharves under the combined action of earthquakes and waves. Attached Figure Description

[0011] Figure 1 This is a flowchart of the system described in an embodiment of the present invention.

[0012] Figure 2 This is a schematic diagram of the data acquisition and modal parameter identification process in an embodiment of the present invention.

[0013] Figure 3 This is a schematic diagram of the model update process based on the parallel particle swarm algorithm in an embodiment of the present invention.

[0014] Figure 4 This is a schematic diagram of the parametric finite element model of the caisson wharf in an embodiment of the present invention.

[0015] Figure 5 This is the physical architecture of the earthquake-wave coupling analysis and stability assessment system in this embodiment of the invention. Detailed Implementation

[0016] The following description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0017] The following embodiments can be understood as illustrating a part of the structure or method of the present invention individually, or as combining the embodiments to explain the broader structure or method of the present invention.

[0018] like Figure 1 As shown, the implementation of this invention is divided into three core stages: data perception and feature extraction, parallel model calibration and update, and coupled analysis and intelligent early warning.

[0019] Phase 1: Data perception and feature extraction; Combination Figure 2 In this embodiment, triaxial acceleration sensors and displacement sensors are deployed at key locations of a gravity-type caisson wharf: the top of the breast wall, the middle of the caisson, and the near-shore seabed. The sensor data is initially filtered by an edge gateway before being transmitted to a cloud server. In the cloud, the data is processed using the following procedure: Step 1: Use Kalman filtering to denoise the continuous acceleration time history data.

[0020] Step 2: Apply random reduction technique to extract and average the free decay vibration signal of the structure from the random environmental vibration response.

[0021] Step 3: Perform a Hilbert transform on the free decay signal to solve for its instantaneous phase and amplitude, thereby automatically identifying the first few natural frequencies f of the structure. exp Damping ratio ξ exp , vibration shape ∅ exp In this example, the first three vertical and horizontal frequencies were successfully identified.

[0022] Phase 2: Parallel model calibration and update; This stage is the core of building a high-fidelity digital twin, and the process is as follows: Figure 3 As shown, it includes the following steps: Step 1: Construct a parametric model.

[0023] Based on the design drawings, use OpenSees to build such a system. Figure 4 The caisson-foundation system shown is a three-dimensional finite element model. The model incorporates the elastic modulus E of the silt layer. silt Elastic modulus E of sandy soil layer sand Elastic modulus E of caisson concrete c The four parameters, including the soil-structure friction coefficient μ, are defined as variables to be updated, X=[p1,p2,p3,p4], and their reasonable physical ranges are set.

[0024] Step 2: Configure the parallel computing environment.

[0025] A private computing cluster is built using the dispy framework on a Linux cluster with one master node and eight compute nodes. The master script is responsible for task scheduling.

[0026] Step 3: Define the parallel optimization task.

[0027] The objective function is defined as follows: ; Among them, f i (X) and ∅ i (X) represents the i-th frequency and mode shape calculated by the model corresponding to parameter set X, MAC is the modal confidence criterion, and α is the weighting coefficient.

[0028] ; Among them, {∅ exp} and {∅ FE} represent the array matrices calculated by the numerical model and identified in actual engineering, respectively. MAC is a scalar with a value between 0 and 1; the larger the value, the higher the modal consistency.

[0029] Step 4: Perform cluster parallel optimization.

[0030] A particle swarm optimization algorithm is used, with a population size of 40. In each generation of evolution, the master node assigns 40 sets of parameters X. j Each task is packaged into an independent OpenSees analysis task, i.e., running the model once and calculating F(X). j The tasks are submitted synchronously to the computing cluster via JobCluster. The cluster automatically assigns tasks to idle nodes for parallel execution. After all tasks are completed, the master node collects the results and executes the PSO algorithm update logic to generate the next generation of parameters. This parallel mode improves computational efficiency by nearly 8 times.

[0031] Step 5: Complete the model update.

[0032] The optimization stops when F(X) is less than the threshold or the maximum number of iterations is reached, and the optimal parameter set X is output. opt X opt Substituting the data into the model yields the calibrated digital twin model.

[0033] Phase 3: Invoke the assessment and early warning module.

[0034] The full-field response time history data obtained from the nonlinear time history analysis in step 2 is input into the system's pre-set intelligent evaluation and early warning module. The physical architecture is as follows: Figure 5 As shown, this module serves as the decision-making center of the digital twin, automatically executing the following core functions according to a preset program:

[0035] (1) Extraction of multi-dimensional performance indicators.

[0036] The module utilizes a built-in performance extraction algorithm to automatically calculate key evaluation metrics from complex time-history data. These mainly include: Overall index extraction: Calculate the maximum horizontal displacement U of the caisson top. max Maximum shear force V at the base max .

[0037] Local damage identification: Calculate the maximum excess pore water pressure ratio r for all soil elements. u,max The system uses preset rules (such as r) to determine the appropriate rules. u,max A value >0.8 indicates high risk, 0.6 <r u,max Units are classified into risk categories based on a threshold of ≤0.8 (the warning zone).

[0038] (2) Threshold-based automatic security status determination.

[0039] The module compares the calculated performance indicators with multi-level security thresholds pre-stored in the database in real time, and automatically outputs the security status level of the structure based on the decision logic. The thresholds and decision logic can be set according to standards and engineering experience. In this embodiment:

[0040] When the maximum horizontal displacement of the top of the caisson is U max When the area of ​​a high-risk liquefaction zone exceeds 100mm or the proportion of such zones exceeds the set value, the judgment is "dangerous (red alert)".

[0041] When the maximum horizontal displacement of the top of the caisson is 50mm max When the thickness is ≤100mm, the judgment is "warning (yellow warning)".

[0042] When the maximum horizontal displacement of the top of the caisson is U max When the thickness is ≤50mm and there is no significant risk of liquefaction, the judgment is "safe (blue normal)".

[0043] Based on the calculation results of this embodiment (U) max (72mm), the module automatically triggers a "yellow warning" status.

[0044] (3) Generation of structured assessment reports and decision support.

[0045] The module automatically integrates raw data, calculated indicators, status judgment results, and a pre-built library of response strategies to generate a structured "Seismic Stability Assessment Report for Caisson Wharf". This report includes at least: a summary of operating conditions, a list of key indicators, safety status conclusions, and preliminary maintenance recommendations, providing direct decision support for operations and maintenance personnel.

[0046] (4) Visualization-driven data output.

[0047] ​To support the visualization of the results, this module also generates a standard visualization-driven data package. This data package contains the geometric information of the model, corresponding to U... max Displacement field data, and based on r u,max Risk classification labels for the divided soil units. This data package is sent to the 3D visualization service module, which uses the labels and values ​​in the data to call the graphics engine to render and generate interactive displacement cloud maps and liquefaction risk zoning maps, thus completing the transformation from data to intuitive graphics.

[0048] Through the above process, this invention realizes a closed loop of "monitoring-updating-evaluation" for the digital twin model of caisson terminals, transforming the traditional static and offline safety assessment into a dynamic, online, and intelligent early warning system, which significantly improves the safety operation and maintenance level of port infrastructure.

Claims

1. A seismic stability assessment system for caisson wharves based on digital twins, characterized in that, It includes a data acquisition and processing module, a model update module, and a performance evaluation and early warning module; The data acquisition and processing module is used to collect dynamic response data of the caisson wharf under environmental and load excitation in real time, and to process the data for structural feature parameters used in model updates. The model update module is used to establish a parametric finite element model of the caisson wharf based on the design data, and to use the obtained structural feature parameters to drive a parallel optimization algorithm to automatically calibrate the physical parameters in the model and establish a digital twin model consistent with the current physical state. The performance evaluation and early warning module is used to perform nonlinear dynamic time history analysis based on the updated digital twin model, applying seismic and wave coupled loads, evaluating structural stability based on the analysis results, and providing visual early warning.

2. The method of using the digital twin-based seismic stability assessment system for caisson wharves as described in claim 1, characterized in that: Includes the following steps: Step 1: Collect vibration response data of the caisson wharf under wave, earthquake, and wind excitation, perform noise reduction and feature recognition processing on the data to obtain structural feature parameters, and store the data; Step 2: Based on the design data of the caisson wharf, a three-dimensional parametric finite element model is established in the OpenSees platform; the structural feature parameters obtained in Step 1 are extracted, the physical parameters of the model that need to be updated and their value ranges are defined, the objective function is constructed based on the error between the measured and simulated modal parameters, and the model parameters are iteratively updated using a parallel computing cluster-driven optimization algorithm to establish a digital twin model; Step 3: Based on the digital twin model established in Step 2, define the engineering requirement parameters and performance thresholds for caisson displacement and soil strain; input the design ground motion and wave load, conduct nonlinear time history analysis, and calculate the various responses of the structure; evaluate the stability state and trigger corresponding early warnings based on the comparison between the response results and the performance thresholds.

3. The method of using the digital twin-based seismic stability assessment system for caisson wharves as described in claim 2, characterized in that: Step 1 includes the following specific steps: (1) Deploy a sensor network at key locations on the dock to collect acceleration and displacement data; (2) Data noise reduction is performed using Kalman filtering, and random decrementing technique and Hilbert transform are used to identify the natural frequency, damping ratio and mode shape of the structure from the environmental vibration response as structural characteristic parameters.

4. The method of using the digital twin-based seismic stability assessment system for caisson wharves as described in claim 2, characterized in that: Step 2 includes the following specific steps: (1) Establish a wharf-foundation model in OpenSees, and set the elastic modulus of soil, elastic modulus of concrete, and contact surface properties as key physical parameters to be optimized variables X; (2) The model update process adopts the particle swarm optimization (PSO) algorithm and deploys the PSO task in parallel on a high-performance computing cluster. The objective function F(X) is the least square error between the measured and simulated natural frequencies. The modal confidence criterion (MAC) is introduced to match numerical simulation and measured mode shapes to identify the mode shape. In each generation, the master node encapsulates 40 sets of X into independent OpenSees analysis tasks and submits them to the cluster nodes for execution in parallel. (3) All nodes synchronously calculate the objective function value and return it. This process is repeated until convergence, and the optimal parameter X is output. opt Substituting the data into the model yields a calibrated digital twin model that matches the current physical state.

5. The method of using the digital twin-based seismic stability assessment system for caisson wharves as described in claim 2, characterized in that, In step 3, the performance evaluation, in addition to outputting the overall indicators of the caisson top displacement and base shear force, also calculates the excess pore water pressure ratio of the soil elements and uses color gradients to visualize and mark the liquefaction risk areas in the three-dimensional model to achieve damage localization.

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