Bridge dynamic interaction analysis method integrating inspection and monitoring data with hybrid substructures

Abstract

The present invention discloses a bridge dynamic interaction analysis method that integrates monitoring data and hybrid substructures. The method first uses hybrid substructure technology to determine the interface constraints of key areas by establishing mathematical equations; then, an expectation-oriented adaptive sparse Gaussian process algorithm is introduced to perform parameter identification of these key areas based on actual monitoring data. Furthermore, the present invention proposes a new theoretical framework to realize the dynamic interaction between substructure nonlinear analysis and main structure linear elastic analysis. The effectiveness of this method is verified by an application example on an actual concrete beam bridge. The present invention not only opens up a new path for the deep integration of monitoring data and finite element models, but also significantly enhances the accuracy of substructure analysis and overall structural performance evaluation.

Description

Technical Field

[0001] The present invention relates to the fields of structural safety assessment and data processing technology, and in particular to a hybrid substructure bridge dynamic interaction analysis method that integrates detection and monitoring data. Background Art

[0002] Existing structures are prone to structural stress changes due to defects such as cracks and changes in material properties, which in turn affects the assessment of structural service performance. Bridge structural health monitoring and inspection technology can provide timely and effective information on structural defects, making it possible to assess the structural condition. However, for large structures with complex stress states, it is difficult to truly and effectively analyze and evaluate the structural service performance using only structural inspection and monitoring data. The development of finite element theory has facilitated the analysis of bridge structures, but existing methods still have the following problems in integrating inspection and monitoring data with finite element models:

[0003] First, existing rapid detection methods for external defects mainly focus on the qualitative identification, positioning, and quantification of structural defects based on detection data, and fail to establish an effective connection between detection data and the local degradation performance of the structure.

[0004] Second, non-contact monitoring methods mainly focus on measuring structural deformation, but less research is done on the relationship between monitoring data and the intrinsic performance of the structure.

[0005] Third, finite element model correction technology mainly achieves consistency between theoretical values ​​and monitoring values ​​at measuring points by modifying internal model parameters. However, these corrections often reflect the overall structural properties and fail to fully utilize the inspection and monitoring data, resulting in large errors in the structural assessment results.

[0006] In summary, the finite element analysis method used in the existing structural safety performance assessment process has a very limited degree of integration of inspection and monitoring data, especially non-contact sensing data, which makes it easy for inaccuracies to occur in structural analysis and assessment. Summary of the Invention

[0007] This invention aims to provide a hybrid substructure bridge dynamic interaction analysis method that integrates inspection and monitoring data. This method addresses the existing difficulty in effectively integrating dynamic monitoring data with finite element models. By combining dynamic monitoring data with finite element models, this method not only enables detailed analysis of key areas but also feeds the analysis results of these key areas into the main simplified finite element model, thereby improving the accuracy of structural assessment.

[0008] In order to achieve the above technical objectives, the present invention adopts the following technical means:

[0009] The hybrid substructure bridge dynamic interaction analysis method integrating detection and monitoring data includes the following steps:

[0010] S1. Select key areas as substructures based on the structural stress characteristics and use refined finite element modeling;

[0011] S2, automatically segmenting cracks and calculating crack width based on deep learning models;

[0012] S3. Based on the above crack width data and corresponding damage mechanism, the weakened element method is used to consider the existing damage in the substructure;

[0013] S4. Establishing the monitoring data of other parts except the substructure and the mathematical equations of the large-scale finite element model and solving the dynamic boundary conditions of the substructure;

[0014] S5. Based on the substructure monitoring data, the expectation-guided adaptive sparse Gaussian process method is used to identify the substructure parameters, thereby achieving independent calculation of the substructure;

[0015] S6. A dynamic interaction analysis method between the substructure and the main structure is proposed. The dynamic response of the substructure interface is converted into an equivalent external dynamic load of the main structure, thereby achieving dynamic coupling between the two and enabling the substructure information to be transmitted to the main structure. The specific steps include the following:

[0016] S61. Take out a certain part of the large-scale structure to establish a substructure model, and simultaneously establish a main structure model;

[0017] S62. Achieve coordination of displacement boundaries and force balance between the main structure and the substructure by introducing nonlinear correction forces;

[0018] S63, the substructure is modeled using solid elements, the main structure is modeled using beam elements, and the two are coupled through multi-point constraints;

[0019] S64, nonlinear analysis of the substructure and linear elastic analysis of the main structure;

[0020] S65. Determine the material properties through the substructure monitoring data, and adjust the material constitutive parameters to update the corresponding parts of the main structure;

[0021] S66, apply the nonlinear correction force as an external load to the main structure;

[0022] S67. Ensure the displacement coordination between the main structure and substructure by deriving simplified bridge structure formulas;

[0023] S68. Solve the equilibrium equations between the main structure and the substructure by using a non-iterative solution method;

[0024] S7. Feedback the results of the substructure nonlinear analysis to the main structure to achieve equivalent dynamic linear analysis.

[0025] Step S2 specifically includes the following features:

[0026] A deep learning model is used to automatically segment crack images. The model adopts a U-shaped structure consisting of an encoder and a decoder, and introduces a group multi-axis Hadamard product attention module to enhance segmentation capabilities.

[0027] This deep learning model introduces a group aggregation bridge module at each stage between the encoder and decoder, fuses and processes multi-scale features by introducing mask information, and further enhances the segmentation capability of the model by using dilated convolution to better extract rich feature information from different resolutions.

[0028] The loss function consists of binary cross entropy and Dice loss, which is used to optimize model training and ensure the best effect in the segmentation task.

[0029] In step S3, the weakened element method includes the following sub-steps:

[0030] S31. Determine the weakened tensile properties based on the bilinear mode I stress-crack width relationship, and construct a stress-strain relationship suitable for the weakened unit by calculating the area under the ultimate strain-maintaining stress-strain curve.

[0031] S32, by not imparting tensile properties to finite elements with pre-existing crack overlap based on an assumed bilinear mode I stress-crack width relationship;

[0032] S33. Based on the measured crack width, a bilinear model is used to derive the tensile strength and the residual properties of the crack under stress-free conditions;

[0033] S34. In the finite element model of concrete, there are cracked areas. The residual properties derived above are introduced to consider the impact of cracks on the overall performance of the structure.

[0034] In step S4, the monitoring data of other parts include displacement, strain and inclination.

[0035] Specific implementation of step S4:

[0036] S41. Establish mathematical models based on overall measurement data and mechanical information of large-scale finite element models. In dynamics, Where F represents the external load, M and C are the structural mass and structural damping respectively, and x represents the structural displacement vector;

[0037] S42. Solve the boundary conditions of the substructure by using an adaptive sparsity matching pursuit method, where the boundary is a dynamic boundary.

[0038] In step S5, the expectation-guided adaptive sparse Gaussian process substructure parameter identification method includes:

[0039] S51, based on the optimal Latin hypercube design, by randomly generating a set of sample points p (1) ,p (2) ,…,p (N) Cover the entire design space; the initial sample points are around the current optimal point P optimal,t-1 Update within the range of ±20% to ensure that the sampling points cover the entire design space while focusing on the local area near the optimal point;

[0040] S52, applying time history displacement u PS,0-t , calculate the parameter set formed by the above sample points and obtain the corresponding response data

[0041] S53, calculating the error index R based on the current response data, and constructing a sparse Gaussian process model based on these data;

[0042] S54, calculating the expected value EI of the candidate sampling points, and selecting the point with the largest expected improvement as the next sampling point;

[0043] S55, updating the sparse Gaussian process model based on the updated sampling points;

[0044] S56, repeat the above steps until the convergence criterion is met;

[0045] S57 , when the maximum value of the expected value EI of the candidate sampling point approaches zero or reaches a preset upper limit of the sampling point, the optimization is stopped.

[0046] The specific steps of the adaptive sparse Gaussian process substructure parameter identification method and the dynamic calculation time history t are:

[0047] (1) Solve the structural dynamics at time t and convert the corresponding displacement u NS,t Send to NS;

[0048] (2) Read the structural response R at the tth time step PS Send to SPI-EASG module;

[0049] (3) Based on P optimal,t-1 Calculate the error NRMSE (R TS,0-t ,R PS,0-t ), and determine whether it exceeds the tolerance. If it exceeds the tolerance, calculate And obtain the new optimal parameter P from the SPI-EASG module optimal,t Otherwise, let P optimal,t =P optimal,t-1 ;

[0050] (4) Change NS from u NS,t-1 Load to uNS,t , and use P optimal,t-1 Calculate the response R NS,t ;

[0051] (5) Repeat steps 1 to 4 until the convergence criterion is met;

[0052] (6) When the convergence criterion is reached, SPI-EASG will output the optimal parameter combination P optimal , as the final parameter identification result of the finite element model.

[0053] In step S6, the implementation of the interaction between the substructure and the main structure specifically includes the following steps:

[0054] SA, extract the motion displacement of the control nodes C and D in the nth time step of the main structure isolation area and transmit it to the substructure, the motion displacement of the control nodes C and D in the nth time step of the main structure isolation area is used as the first interface physical quantity, and the first interface physical quantity is used as the displacement w' of the substructure control nodes C' and D' (m,n) Iteration initial value;

[0055] After receiving the above-mentioned first interface physical quantity, the SB,substructure performs a dynamic analysis and transmits the calculated reaction force R' of the substructure control nodes C', D' back to the main structure;

[0056] SC and the main structure receive the reaction force R', calculate the external force and load it to the main structure control nodes C and D, and perform a dynamic analysis;

[0057] SD, extract the displacement w of the main structure control nodes C and D c,(m,n) And transmit it to the substructure. According to the displacement coordination, the motion amount is the displacement w' of the substructure control node C', D' (m+1,n) , the substructure controls the displacement w' of nodes C', D' (m+1,n) As the second interface physical quantity;

[0058] After receiving the above-mentioned second interface physical quantity, the SE,substructure performs a dynamic analysis and transmits the calculated reaction force R of the substructure control nodes C', D' back to the main structure;

[0059] SF and the main structure receive the reaction force R, calculate the external force and load it to the main structure control nodes C and D, and then perform a dynamic analysis;

[0060] SG, extract the displacement w of the main structure control nodes C and D c,(m+1,n) , and transfer it to the substructure.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] First, achieving effective integration of inspection and health monitoring data with finite element models: Traditional structural inspection and health monitoring methods face many challenges when directly assessing structural status, particularly the accurate integration of data from diverse sources. This paper proposes an innovative framework that effectively combines inspection and health monitoring data with finite element models through hybrid substructure technology. This framework not only improves the accuracy of structural analysis and assessment, but also resolves the long-standing disconnect between inspection and health monitoring data and finite element models, providing a solid foundation for the safe operation of complex structures such as bridges.

[0063] Second, improve the ability of refined analysis of key areas: In order to meet the needs of refined analysis of key areas, the present invention proposes a hybrid substructure independent dynamic interaction method that integrates inspection and monitoring data. This method first uses solid units to establish a finite element model of the key area to improve the accuracy of the analysis. Then, based on the inspection data, the structural damage and its dynamic degradation model are introduced to reflect the performance changes of the structure during actual use. The interface constraints of the substructure are solved through monitoring data, and the key area parameters are identified based on the expectation-guided sparse Gaussian process, which ultimately realizes the independent dynamic analysis of the substructure and significantly improves the accuracy of local analysis.

[0064] Third, dynamic interaction analysis between substructures and the main structure: To address the inadequacy of interaction analysis between substructures and the main structure, this paper derives a hybrid substructure dynamic interaction analysis theory. This theory converts the dynamic response of the substructure interface into an equivalent external dynamic load on the main structure, thereby achieving dynamic coupling between the two and enabling substructure information to be transmitted to the main structure. Through this interaction mechanism, local dynamic responses can be more accurately reflected in the overall analysis, providing more reliable structural assessment under complex loads and environmental changes.

[0065] Fourth, interactive analysis is applied to actual bridge dynamic calculations: Compared to traditional static analysis, the method of this invention focuses on dynamic analysis and applies it to actual bridges. By incorporating dynamic monitoring data and nonlinear analysis, this method can more accurately simulate the behavior of bridges under actual traffic loads and impact vibrations. This not only improves the accuracy of the analysis, but also makes the structural assessment results more realistic. Furthermore, the effectiveness of the method has been verified through field tests, further demonstrating its feasibility and reliability in practical engineering applications.

[0066] Fifth, improve the efficiency and accuracy of overall structural analysis: The feasibility of this method is demonstrated through experimental verification of a prestressed concrete beam bridge. The experimental results show that the proposed method can accurately evaluate the dynamic stress state of existing structures and significantly improve the accuracy of performance analysis of large and complex bridge structures. This method can effectively analyze the status of the overall structure and key areas while using a small number of sensors, thereby achieving a comprehensive assessment of important infrastructure such as bridges. The integration of structural disease detection data significantly improves the accuracy of local analysis, and the analysis efficiency of the overall structure is further improved through dynamic interaction analysis between substructures and the main structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is the basic idea of ​​the bridge dynamic interaction analysis method proposed in this invention that integrates inspection and monitoring data with hybrid substructures;

[0068] Figure 2 This is the deep learning-based automatic crack segmentation algorithm proposed in the present invention;

[0069] Figure 3 This is a correlation diagram between crack width and weakening unit properties proposed in the present invention;

[0070] Figure 4 This is a flow chart of the substructure analysis method for fusing measurement data and finite element models proposed in the present invention;

[0071] Figure 5 A simplified schematic diagram of the SPI-EASG proposed in the present invention;

[0072] Figure 6 This is a flow chart of the SPI-EASG proposed in the present invention;

[0073] Figure 7 Schematic diagram of the principle of dynamic interaction analysis between substructure and main knot proposed in the present invention;

[0074] Figure 8 The concrete soil continuous box girder bridge mentioned as an example includes (a) elevation; (b) photo of the actual bridge; (c) cross-sectional view;

[0075] Figure 9 Schematic diagram of the sensor layout proposed for the example, including: (a) the overall situation of sensor layout; (b) the specific situation of accelerometer layout;

[0076] Figure 10 The crack identification results based on the deep learning model proposed in this example;

[0077] Figure 11 Substructure analysis procedures for integrated detection and monitoring data;

[0078] Figure 12 The substructure parameter identification results include: (a) sampling samples; (b) impact load identification results; (c) vehicle dynamic load identification results;

[0079] Figure 13 The calculation results of the substructure proposed for the example;

[0080] Figure 14 The calculation results of the impact vibration strain mentioned in the example;

[0081] Figure 15 Calculation results of the vehicle dynamic load in the example. DETAILED DESCRIPTION

[0082] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0083] Some of the definitions involved in this application are as follows:

[0084] Non-contact sensing data: refers to the detection and monitoring data obtained by non-contact measurement technology, specifically including intelligent crack detection data based on optical cameras and structural deformation monitoring data based on laser scanning technology.

[0085] Substructure and main structure: The main structure is the overall finite element model of the structure, and the substructure is the finite element model of the local area.

[0086] like Figure 1 As shown, a hybrid substructure bridge dynamic interaction analysis method integrating detection and monitoring data includes the following steps:

[0087] S1. Select key areas as substructures based on the structural stress characteristics and use refined finite element modeling;

[0088] S2, automatically segmenting cracks and calculating crack width based on deep learning models;

[0089] S3. Based on the above crack width data and corresponding damage mechanism, the weakening element theory is used to consider the existing damage in the substructure;

[0090] S4. Establishing the monitoring data of other parts except the substructure and the mathematical equations of the large-scale finite element model and solving the dynamic boundary conditions of the substructure, wherein the monitoring data of other parts include displacement, strain and inclination;

[0091] S5. Based on the substructure monitoring data, the expectation-guided adaptive sparse Gaussian process method is used to identify substructure parameters, thereby achieving independent substructure analysis;

[0092] S6. Propose a dynamic interaction analysis theory between the substructure and the main structure, which includes the following sub-steps:

[0093] S61. Decompose large-scale structures into multiple detailed substructure models and a relatively coarse main structure model;

[0094] S62. Achieve coordination of displacement boundaries and force balance between the main structure and the substructure by introducing nonlinear correction forces;

[0095] S63, the substructure is modeled using solid elements, the main structure is modeled using beam elements, and the two are coupled through multi-point constraints;

[0096] S64, nonlinear analysis of the substructure and linear elastic analysis of the main structure;

[0097] S65. Determine the material properties through the substructure monitoring data, and adjust the material constitutive parameters to update the corresponding parts of the main structure;

[0098] S66, apply the nonlinear correction force as an external load to the main structure;

[0099] S67. Ensure the displacement coordination between the main structure and substructure by deriving simplified bridge structure formulas;

[0100] S68. Solve the equilibrium equations between the main structure and the substructure through non-iterative solution.

[0101] S7. Feedback the results of the substructure nonlinear analysis to the main structure to achieve equivalent dynamic linear analysis.

[0102] like Figure 2 As shown, the automatic crack segmentation based on the deep learning model in step S2 has the following characteristics:

[0103] (1) Automatically segment crack images using a deep learning model. The model adopts a U-shaped structure consisting of an encoder and a decoder, and introduces a group multi-axis Hadamard product attention module to enhance segmentation capabilities.

[0104] (2) The deep learning model introduces a group aggregation bridge module at each stage between the encoder and decoder, which fuses and processes multi-scale features by introducing mask information and further enhances the segmentation capability of the model by using dilated convolution to better extract rich feature information from different resolutions.

[0105] (3) The loss function consists of binary cross entropy and Dice loss, which is used to optimize model training and ensure the best effect in the segmentation task.

[0106] (4) Combined with image processing methods, quantitative information such as crack length and width can be quickly extracted.

[0107] like Figure 3As shown, in step S3, based on the crack width data and the corresponding damage mechanism, the weakening element theory is used to consider the existing damage in the substructure. The weakening element method includes:

[0108] (1) Determine the weakened tensile properties based on the bilinear model I stress-crack width relationship;

[0109] (2) Based on the measured crack width, a bilinear model is used to derive the tensile strength and the residual opening of the crack under stress-free conditions;

[0110] (3) constructing a stress-strain relationship applicable to the weakened unit by calculating the area under the ultimate strain-maintaining stress-strain curve equal to the ratio of the residual fracture energy to the crack bandwidth;

[0111] (4) By introducing crack weakening units to simulate the impact of cracks on the overall performance of the structure, the mechanical properties of the material are adjusted to simulate the weakening effect of cracks on the structure.

[0112] like Figure 4 As shown, the specific implementation of step S4 is:

[0113] S41. Establish mathematical models based on overall measurement data and mechanical information of large-scale finite element models. In dynamics, Where F represents the external load, M and C are the structural mass and structural damping respectively, and x represents the structural displacement vector;

[0114] S42. The boundary conditions of the substructure are solved by the adaptive sparsity matching pursuit method, where the boundary is the dynamic boundary.

[0115] like Figure 5 As shown, in step S5, the expectation-guided adaptive sparse Gaussian process substructure parameter identification method includes:

[0116] (1) Based on the Optimal Latin Hypercube Design (OLHD), this method randomly generates a set of sample points p (1) ,p (2) ,…,p (N) Cover the entire design space. The initial sample points are around the current optimal point P optimal,t-1 Update within the range of ±20% to ensure that the sampling points cover the entire design space while focusing on the local area near the optimal point;

[0117] (2) Applying time history displacement u PS,0-t , calculate the parameter set formed by the above sample points and obtain the corresponding response data

[0118] (3) Calculate the error index R based on the current response data and construct a sparse Gaussian process model based on this data;

[0119] (4) Calculate the expected improvement (EI) of the candidate sampling points and select the point with the largest expected improvement as the next sampling point;

[0120] (5) updating the sparse Gaussian process model based on the updated sampling points;

[0121] (6) Repeat the above steps until the convergence criterion is met;

[0122] (7) Stop the optimization when the maximum value of EI approaches zero or reaches the preset upper limit of sampling points.

[0123] like Figure 6 As shown, the specific steps of the expectation-guided adaptive sparse Gaussian process substructure parameter identification method at time t are described as follows:

[0124] (1) Read the structural response R at the tth time step PS Send to SPI-EASG module;

[0125] (2) Based on P optimal,t-1 Calculate the error NRMSE (R TS,0-t ,R PS,0-t ), and determine whether it exceeds the tolerance. If it exceeds the tolerance, calculate And obtain the new optimal parameter P from the SPI-EASG module optimal,t Otherwise, let P optimal,t =P optimal,t-1 ;

[0126] (3) Change NS from u NS,t-1 Load to u NS,t , and use P optimal,t-1 Calculate the response R NS,t ;

[0127] (4) Repeat steps 1 to 4 until the convergence criterion is met;

[0128] (5) When the convergence criterion is reached, SPI-EASG will output the optimal parameter combination P optimal , as the final parameter identification results of the finite element model. These identified parameters can be used for model calibration and improve the accuracy of structural response prediction.

[0129] like Figure 7 As shown, in step S6, the dynamic interaction analysis method between the substructure and the main structure has the following characteristics:

[0130] Steps to implement substructure interaction:

[0131] (1) Extract the displacement of the control nodes C and D in the isolation area of ​​the main structure at the nth time step and transmit it to the substructure, and use the interface physical quantity as the displacement w' of the substructure control nodes C' and D' (m,n) Iteration initial value;

[0132] (2) After receiving the above interface physical quantities, the substructure performs a dynamic analysis and transmits the calculated reaction force R' of the substructure control nodes C' and D' back to the main structure;

[0133] (3) The main structure receives the reaction force R', and the external force is calculated according to formula (8) and loaded to the main structure control nodes C and D to perform a dynamic analysis;

[0134] (4) Extract the displacement w of the main structure control nodes C and D c,(m,n) And it will be transmitted to the substructure. According to the displacement coordination, the movement amount is the displacement w' of the substructure control node C', D' (m+1,n) ;

[0135] (5) After receiving the above interface physical quantities, the substructure performs a dynamic analysis and transmits the calculated reaction force R' of the substructure control nodes C' and D' back to the main structure;

[0136] (6) The main structure receives the reaction force R', and the external force is calculated according to formula (8) and loaded to the main structure control nodes C and D, and then a dynamic analysis is performed;

[0137] (7) Extract the displacement w of the main structure control nodes C and D c,(m+1,n) , and transfer it to the substructure.

[0138] The solution of this embodiment is illustrated below using an actual bridge scenario.

[0139] like Figure 8 As shown in the figure, the test object is a three-span prestressed concrete variable-section single-box single-chamber continuous box girder bridge with a superstructure size of 53 meters + 85 meters + 53 meters. The box girder height gradually increases from 2.4 meters at 1 meter from the mid-span to 5.0 meters at 1.5 meters from the pier center, presenting a quadratic parabola shape. Figure 9As shown, to comprehensively monitor the bridge's response under various loads, a variety of sensors were installed throughout the bridge: 25 long-gauge strain sensors were used to monitor the structure's strain response under load; 15 inclinometers were deployed to capture changes in rotational angle during loading; and 25 accelerometers were also installed to record the structure's dynamic response under dynamic loads. The test was conducted in two phases. The first phase involved a truck dynamic load test, simulating the load effects of actual traffic conditions. A truck was driven at 40 km / h and eventually stopped in lane 3 in the middle of the bridge span. The second phase involved an impact vibration test, using an exciter to apply excitation at designated locations (excitation points 18, 19, and 20) to simulate the impact vibration conditions that the bridge might encounter.

[0140] like Figure 10 As shown in the figure, based on the deep learning model, the crack images collected in the experiment are input to achieve automatic segmentation of cracks. Figure 10 The paper presents an original image of a crack in a frame structure and its automatic segmentation using a deep learning model. The model is able to simultaneously identify both high-level global semantic features and low-level details of the crack, thereby improving the accuracy and efficiency of crack segmentation. Furthermore, image processing methods are used to perform pixel-level threshold segmentation of the cracks, determine the total number of crack pixels, and combine area calculation with the medial axis method to extract the crack skeleton, thereby achieving automated measurement of the average crack width.

[0141] Based on the existing crack width and the corresponding element properties of the weakened constitutive relation, the cracks are taken into account in the refined substructure model using the weakened element method. The overall size of the substructure is set to 0.5m, while the mesh in the critical area is refined to 0.1m, and the geometric nonlinear analysis option is enabled. Figure 11 As shown in step S4, the boundary conditions of the substructure are solved based on the mathematical and physical model. Finally, the SPI-EASG algorithm is used to complete the identification of the key parameters of the substructure, including the update of the elastic modulus of concrete and the adjustment of the elastic modulus and yield strength of the steel bars. The results of parameter identification are shown in Figure 12 .

[0142] like Figure 13 As shown in the figure, the strain and stress results of the substructure under impact vibration and vehicle dynamic load are displayed. Through the proposed substructure interaction method, key areas can be analyzed independently, which is convenient for considering actual monitoring data, and then the local stress state can be evaluated more accurately and the results can be fed back to the main structure, finally realizing the information transmission between "overall large-scale model-local fine model".

[0143] like Figure 14As shown in the figure, the strain response results of the structure under the impact vibration load are displayed. By selecting the strain sensor signals corresponding to the positions of acceleration sensors A18 and A19 for comparison, the effectiveness of the method proposed in this paper is verified. Under the action of impact vibration, compared with the simple model updating method, the substructure dynamic interaction method proposed in this paper is closer to the measured strain response and can effectively evaluate the stress state of the structure.

[0144] like Figure 15 As shown in the figure, the structural strain and displacement response results under the action of vehicle dynamic load are displayed. By selecting the measurement signals of acceleration sensors A2, A18, and A19 for comparison, the structural response calculated by the method proposed in this paper is closer to the measured response, which can more accurately reflect the dynamic response of the structure and is expected to improve the accuracy of structural safety assessment.

[0145] The detailed experimental design and results analysis demonstrated the effectiveness of the hybrid substructure bridge dynamic interaction analysis method proposed in this paper, which integrates inspection and monitoring data. This method not only addresses the difficulty of integrating inspection and monitoring data with finite element models, but also significantly improves the accuracy of structural safety assessments. Furthermore, it demonstrates potential for widespread application in the safety assessment of large, complex structures.

[0146] In summary, the successful demonstration of the specific embodiment demonstrates the feasibility and effectiveness of the proposed solution. Experiments have shown that the finite element model established by integrating inspection and monitoring data can significantly improve the efficiency and accuracy of overall structural analysis, which is of great significance for simulating the stress conditions of existing structures, especially in improving the accuracy of performance analysis of large structures under complex stress conditions.

[0147] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should all be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A hybrid substructure bridge dynamic interaction analysis method integrating detection and monitoring data, characterized by: The steps include: S1. Select key areas as substructures based on the structural stress characteristics and use refined finite element modeling; S2, automatically segmenting cracks and calculating crack width based on deep learning models; S3. Based on the above crack width data and corresponding damage mechanism, the weakened element method is used to consider the existing damage in the substructure; S4. Establishing the monitoring data of other parts except the substructure and the mathematical equations of the large-scale finite element model and solving the dynamic boundary conditions of the substructure; S5. Based on the substructure monitoring data, the expectation-guided adaptive sparse Gaussian process method is used to identify the substructure parameters, thereby achieving independent calculation of the substructure; S6. A dynamic interaction analysis method between the substructure and the main structure is proposed. The dynamic response of the substructure interface is converted into an equivalent external dynamic load of the main structure, thereby achieving dynamic coupling between the two and enabling the substructure information to be transmitted to the main structure. The specific steps include the following: S61. Take out a certain part of the large-scale structure to establish a substructure model, and simultaneously establish a main structure model; S62. Achieve coordination of displacement boundaries and force balance between the main structure and the substructure by introducing nonlinear correction forces; S63, the substructure is modeled using solid elements, the main structure is modeled using beam elements, and the two are coupled through multi-point constraints; S64, nonlinear analysis of the substructure and linear elastic analysis of the main structure; S65. Determine the material properties through the substructure monitoring data, and adjust the material constitutive parameters to update the corresponding parts of the main structure; S66, apply the nonlinear correction force as an external load to the main structure; S67. Ensure the displacement coordination between the main structure and substructure by deriving simplified bridge structure formulas; S68. Solve the equilibrium equations between the main structure and the substructure by using a non-iterative solution method; S7, feeding back the results of the substructure nonlinear analysis to the main structure to achieve equivalent dynamic linear analysis; In step S6, the implementation of the interaction between the substructure and the main structure specifically includes the following steps: SA, extract the motion displacement of the control nodes C and D in the nth time step of the main structure isolation area and transmit it to the substructure, the motion displacement of the control nodes C and D in the nth time step of the main structure isolation area is used as the first interface physical quantity, and the first interface physical quantity is used as the displacement w' of the substructure control nodes C' and D' (m,n) Iteration initial value; After receiving the above-mentioned first interface physical quantity, the SB,substructure performs a dynamic analysis and transmits the calculated reaction force R' of the substructure control nodes C', D' back to the main structure; SC and the main structure receive the reaction force R', calculate the external force and load it to the main structure control nodes C and D, and perform a dynamic analysis; SD, extract the displacement w of the main structure control nodes C and D c,(m,n) And transmit it to the substructure. According to the displacement coordination, the motion amount is the displacement w' of the substructure control node C', D' (m+1,n) , the substructure controls the displacement w' of nodes C', D' (m+1,n) As the second interface physical quantity; After receiving the above-mentioned second interface physical quantity, the SE,substructure performs a dynamic analysis and transmits the calculated reaction force R of the substructure control nodes C', D' back to the main structure; SF and the main structure receive the reaction force R, calculate the external force and load it to the main structure control nodes C and D, and then perform a dynamic analysis; SG, extract the displacement w of the main structure control nodes C and D c,(m+1,n) , and transfer it to the substructure.

2. The hybrid substructure bridge dynamic interaction analysis method based on fusion detection and monitoring data according to claim 1 is characterized in that: Step S2 specifically includes the following features: A deep learning model is used to automatically segment crack images. The model adopts a U-shaped structure consisting of an encoder and a decoder, and introduces a group multi-axis Hadamard product attention module to enhance segmentation capabilities. This deep learning model introduces a cluster aggregation bridge module at each stage between the encoder and decoder. It fuses and processes multi-scale features by introducing mask information and uses dilated convolution to better extract rich feature information from different resolutions, further enhancing the model's segmentation capabilities. The loss function consists of binary cross entropy and Dice loss, which is used to optimize model training and ensure the best effect in the segmentation task.

3. The hybrid substructure bridge dynamic interaction analysis method based on fusion detection and monitoring data according to claim 1 is characterized in that: In step S3, the weakened element method includes the following sub-steps: S31, determining the weakened tensile properties based on the bilinear mode I stress-crack width relationship, and constructing a stress-strain relationship applicable to the weakened unit by calculating the area under the ultimate strain-maintaining stress-strain curve; S32, by not imparting tensile properties to finite elements with pre-existing crack overlap based on an assumed bilinear mode I stress-crack width relationship; S33. Based on the measured crack width, a bilinear model is used to derive the tensile strength and the residual properties of the crack under stress-free conditions; S34. In the finite element model of concrete, there are cracked areas. The residual properties derived above are introduced to consider the impact of cracks on the overall performance of the structure.

4. The hybrid substructure bridge dynamic interaction analysis method based on fusion detection and monitoring data according to claim 1 is characterized in that: In step S4, the monitoring data of other parts include displacement, strain and inclination.

5. The hybrid substructure bridge dynamic interaction analysis method based on fusion detection and monitoring data according to claim 1 is characterized in that: Specific implementation of step S4: S41. A mathematical model is established based on the overall measurement data and the mechanical information of the large-scale finite element model. In dynamics, Where F represents the external load, M and C are the structural mass and structural damping respectively, and x represents the structural displacement vector; S42. The boundary conditions of the substructure are solved by the adaptive sparsity matching pursuit method, where the boundary is the dynamic boundary.

6. The hybrid substructure bridge dynamic interaction analysis method based on fusion detection and monitoring data according to claim 1 is characterized in that: In step S5, the expectation-guided adaptive sparse Gaussian process substructure parameter identification method includes: S51, based on the optimal Latin hypercube design, by randomly generating a set of sample points p (1) ,p (2) ,…,p (N) Cover the entire design space; the initial sample points are around the current optimal point P optimal,t-1 Update within the range of ±20% to ensure that the sampling points cover the entire design space while focusing on the local area near the optimal point; S52, applying time history displacement u PS,0-t , calculate the parameter set formed by the above sample points and obtain the corresponding response data S53, calculating the error index R based on the current response data, and constructing a sparse Gaussian process model based on these data; S54, calculating the expected value EI of the candidate sampling points, and selecting the point with the largest expected improvement as the next sampling point; S55, updating the sparse Gaussian process model based on the updated sampling points; S56, repeat the above steps until the convergence criterion is met; S57 , when the maximum value of the expected value EI of the candidate sampling point approaches zero or reaches a preset upper limit of the sampling point, the optimization is stopped.

7. The hybrid substructure bridge dynamic interaction analysis method based on fusion detection and monitoring data according to claim 5 is characterized in that: The specific steps of the adaptive sparse Gaussian process substructure parameter identification method and the dynamic calculation time history t are: (1) Solve the structural dynamics at time t and convert the corresponding displacement u NS,t Send to NS; (2) Read the structural response R at the tth time step PS Send to SPI-EASG module; (3) Based on P optimal,t-1 Calculate the error NRMSE (R TS,0-t ,R PS,0-t ), and determine whether it exceeds the tolerance; if it exceeds the tolerance, calculate And obtain the new optimal parameter P from the SPI-EASG module optimal,t Otherwise, let P optimal,t =P optimal,t-1 ; (4) Change NS from u NS,t-1 Load to u NS,t , and use P optimal,t-1 Calculate the response R NS,t ; (5) Repeat steps 1 to 4 until the convergence criterion is met; (6) When the convergence criterion is reached, SPI-EASG will output the optimal parameter combination P optimal , as the final parameter identification result of the finite element model.

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