A target-aware, direction-oriented twin structure interaction method, system and application

Abstract

The application relates to a target-sensing-oriented twin substructure interaction method, system and application, which comprises the following steps: acquiring multi-element monitoring data and finite element influence line data of a main structure; based on the monitoring data and the finite element influence line data of a non-key area, solving boundary conditions of a key area by using an adaptive sparsity matching tracking algorithm to realize first-level data fusion; establishing a refined twin substructure finite element model considering the influence of structure degradation for the key area, and based on the monitoring data and the finite element influence line data of the key area, correcting material properties of the refined twin substructure finite element model to realize second-level data fusion; and based on the boundary conditions and the material properties of the key area, calculating a correction force, and taking the correction force as an equivalent external load acting on nodes of a global finite element model to realize the interaction between the substructure and the main structure. The application realizes the mutual fusion and commonality of monitoring data and a finite element model and the interaction between each substructure and the main structure.

Description

Technical Field

[0001] This invention relates to the fields of structural safety assessment and data processing technology, and in particular to a targeted perception-guided twin structure interaction method, system, and application. Background Technology

[0002] Bridge structural health monitoring and detection technologies can provide timely and effective information on structural defects, and many scholars have conducted extensive research based on the direct inversion of structural conditions using monitoring data. However, for structures with large dimensions and complex stress states, it is difficult to accurately and effectively analyze and evaluate structural service performance using only structural inspection and monitoring data. The rapid development of finite element theory has facilitated bridge structural analysis, and finite element enhancement methods using monitoring data have been widely studied. However, existing methods still have the following problems:

[0003] (1) Based on existing finite element model improvement methods, it is difficult to fully integrate the internal and external structural damage in key areas and to fully integrate the monitoring data to carry out structural analysis and evaluation.

[0004] (2) The actual structural dimensions are large and the stress state is complex. Conducting detailed analysis on the entire structure can easily lead to low analysis efficiency. Therefore, if bridge inspection and monitoring information can be fully utilized in the simulation and analysis process, and the combination of structural mechanics model analysis (forward modeling) and inspection and monitoring information fusion (inverse modeling) can be used, it is hoped that the bottleneck of bridge structural state analysis and evaluation can be broken.

[0005] Finite element model (FEM) correction has been widely researched and applied in civil engineering, serving as an effective method for integrating monitoring data with FEM models. However, FEM correction techniques primarily achieve consistency between theoretical and monitoring values ​​by modifying internal model parameters. The corrected FEM parameters often reflect the overall structural properties, but their application and analysis of monitoring data are insufficient. In contrast, hybrid simulation methods in earthquake engineering achieve synchronous coupling of numerical substructure analysis and experimental substructure dynamic loading through interactive technology, truly realizing a deep integration of experimental and numerical analysis. Inspired by hybrid simulation, the field of structural health has proposed the concept of hybrid monitoring, combining bridge monitoring data with finite element models to achieve rapid reconstruction of structural responses.

[0006] Integrating monitoring data for detailed analysis of critical components is more suitable for meeting the needs of bridge structural health systems and structural safety assessments. However, existing structural health monitoring systems typically deploy sensors according to economic and rational principles, placing more sensors in key structural areas such as those experiencing significant stress, deformation, or defects, while fewer sensors are deployed in non-key areas.

[0007] However, most existing methods do not consider the interaction between global and local models, and the bottleneck problem of the difficulty in integrating and communicating monitoring data with finite element models still exists.

[0008] In summary, the existing finite element analysis method used in structural safety assessment has very limited integration of detection and monitoring data, and there are problems such as inaccurate assessment when conducting structural analysis and evaluation of existing structures that are large in size and have complex stress states. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a targeted perception-guided twin structure interaction method, system and application, so as to overcome or partially overcome the bottleneck problem that monitoring data and finite element models are difficult to integrate and communicate.

[0010] The objective of this invention can be achieved through the following technical solutions:

[0011] One aspect of the present invention provides a targeted perception-guided twin structure interaction method, comprising the following steps:

[0012] Obtain multi-dimensional monitoring data and finite element influence line data of the main structure, wherein the main structure is divided into key areas and non-key areas;

[0013] Based on monitoring data and finite element influence line data from non-key areas, the boundary conditions of key areas are solved using an adaptive sparsity matching tracking algorithm to achieve the first level of data fusion.

[0014] A refined twin substructure finite element model considering the impact of structural degradation was established for the key area. Based on the monitoring data and finite element influence line data of the key area, the material properties of the refined twin substructure finite element model were corrected to achieve the second level of data fusion.

[0015] The correction force is calculated based on the boundary conditions and material properties of the key region. The correction force is then applied as an equivalent external load to the nodes of the global finite element model, thus completing the interaction between the refined twin structure finite element model and the global finite element model.

[0016] As a preferred technical solution, the process of solving the boundary conditions of the key region includes:

[0017] Establish mathematical equations for detection data and finite element influence line data in non-key areas;

[0018] The mathematical equations are transformed into NP-hard nonconvex combinatorial optimization problems, which are then solved using the adaptive sparse matching and tracking algorithm.

[0019] As a preferred technical solution, the adaptive sparse matching tracking algorithm includes the following steps:

[0020] Influence line matrix is ​​constructed based on finite element influence line data and singular value decomposition is performed. Multivariate detection data are projected onto the subspace formed by the column tensors of the influence line matrix.

[0021] Based on the projection of multivariate detection data onto the subspace formed by the column tensor of the influence line matrix, the sparsity of the term to be solved is changed by iterative calculation, and the sparsity with the highest solution accuracy after multiple iterations is taken as the sparsity of the term to be solved.

[0022] As a preferred technical solution, the adaptive sparse matching tracking algorithm specifically includes:

[0023] Step 1: Input the influence line matrix A and monitor the data Y;

[0024] Step 2: Perform singular value decomposition on the influence line matrix, projecting the monitoring data Y onto the subspace spanned by the column vectors of the influence line matrix A, i.e., y = Proj A (Y);

[0025] Step 3, initialize r0 = y, Λ0 = φ, t = 1;

[0026] Step 4: Calculate the correlation coefficient u = abs[A T r t-1 Select the 2K maximum values ​​in u, and form a column index set J0 with the column index j of A corresponding to the maximum value;

[0027] Step 5, let Λ t =Λ t-1 ∪J0, A t =A t-1 ∪a j (j∈J0);

[0028] Step 6, Calculation

[0029] Step 7, A t The corresponding K term is denoted as A. tK The column number corresponding to A is denoted as Λ. tK Update set Λ t =Λ tK ;

[0030] Step 8: Calculate and update the error

[0031] Step 9, t = t + 1, if t ≤ 2K, return to Step 2 to continue iterating, otherwise proceed to Step 10;

[0032] Step 10, update the sparsity K = K + ceil(0.02 * size(A,2));

[0033] Step 11: If the sparsity exceeds K = size(A,2) * 0.5 or the error is less than the preset threshold Threshold, output the equivalent nodal force F = θ^_tK as the boundary condition for the key region; otherwise, proceed to Step 4.

[0034] Where t is the number of iterations. Let J0 be an empty set, and J0 represent the index obtained in each iteration. t Let represent the set of indices for the t-th iteration, and ∧ t The number of elements is L t a j Let A be the j-th column of the influence line matrix A. t ={a j}(j∈∧ t ) indicates the set by index ∧ t The set of columns of the selected influence line matrix A, θ t For L t A column vector of size ×1, where the symbol ∪ represents the set union operation.

[0035] As a preferred technical solution, the structural deterioration effects include the effects of external cracking and internal corrosion.

[0036] As a preferred technical solution, the process of constructing the refined twin structure finite element model considering the effects of structural degradation includes:

[0037] The crack weakening element method is used to establish the first reduction relationship between the width of external cracks in the main structure and the stiffness of the weakening element, so as to realize the modeling of the impact of external crack defects.

[0038] Based on the material parameters inside the steel corrosion and deterioration constitutive model, a second reduction relationship between the steel corrosion rate inside the main structure and the structural deterioration constitutive model is established to model the impact of internal corrosion damage.

[0039] A refined twin structure finite element model is constructed based on the first reduction relationship and the second reduction relationship.

[0040] As a preferred technical solution, the key areas are the areas with multiple defects discovered during the inspection process or the vulnerable areas identified by mechanical analysis, and the non-key areas are the other parts of the main structure excluding the key areas.

[0041] As a preferred technical solution, the multi-dimensional monitoring data includes nodal displacement values, nodal rotation values, and strain displacement values, and the material properties include at least one of the constitutive parameters of concrete, steel reinforcement, and steel.

[0042] Another aspect of the present invention provides an application of the aforementioned target-sensing-guided twin structure interaction method, comprising the following steps:

[0043] The interaction between the refined twin structure finite element model and the global finite element model is accomplished using the target perception-guided twin structure interaction method.

[0044] The theoretical displacement of nodes is calculated using an interactive global finite element model.

[0045] Obtain the measured displacement of the node;

[0046] The bearing capacity of the main structure is assessed for safety based on the theoretical and measured displacements.

[0047] Another aspect of the present invention provides a target-sensing-guided twin structure interaction system, comprising:

[0048] The finite element information extraction module is used to obtain finite element influence line data;

[0049] The boundary condition solution module is used to solve the boundary conditions of the key area based on the finite element influence line data of the non-key area of ​​the main structure and the acquired multi-element detection data, through a preset storage medium. The storage medium includes instructions for implementing the adaptive sparse matching tracking algorithm.

[0050] The twin structure fine identification module is used to establish a fine twin structure finite element model that takes into account the effect of structural degradation for key areas of the main structure;

[0051] The correction feedback module is used to correct the material properties of the refined twin structure finite element model based on the monitoring data and finite element influence line data of the key area, calculate the correction force based on the boundary conditions of the key area and the material properties, and apply the correction force as an equivalent external load to the nodes of the global finite element model.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] (1) Achieving effective integration and communication between monitoring data and finite element model: To address the bottleneck problem of the difficulty in integrating monitoring data and finite element model, this application uses the adaptive sparsity matching tracking algorithm to solve the boundary conditions of key areas based on monitoring data and finite element influence line data of non-key areas, thereby achieving the integration of the first-level monitoring data and finite element model; based on monitoring data and finite element influence line data of key areas, the material properties of the refined twin structure finite element model are corrected, thereby achieving the integration of the second-level monitoring data and finite element model. Through the interaction of the two levels, the information between the global model and the local model of the key areas that need to be finely identified is realized, which effectively improves the modeling and analysis accuracy of the finite element model of key parts of the structure, and can be widely used in application scenarios such as safety assessment.

[0054] (2) Convenient solution for boundary conditions in key areas: Addressing the problem that it is difficult to deploy sensors to measure the physical conditions of the boundary in key areas of some structures, this invention establishes a mathematical equation relating the monitoring data from non-key areas to the finite element influence lines. An adaptive sparse matching tracking algorithm is then used to achieve high-precision solution for the boundary conditions in key areas. This method requires only a small amount of monitoring data from non-key areas to effectively solve the boundary conditions in key areas, solving the problem of difficult boundary measurement due to the difficulty in deploying sensors on complex structural boundaries.

[0055] (3) Improve the analysis efficiency of complex structures: In response to the problem of low efficiency in structural analysis of large-scale structures with complex stress states, this invention transforms the time-consuming analysis of establishing a refined finite element model of the whole structure with global damage into nonlinear analysis of key areas and equivalent linear elastic analysis of the simplified main structure. It is not necessary to establish a refined model of the entire structure. The analysis and evaluation of the structure can be completed through the interactive analysis of key areas and the main structure. It has the advantages of fast analysis speed and accurate analysis. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the target-aware guided substructure interaction method in Example 1;

[0057] Figure 2 This is a flowchart illustrating the process of refined identification of key structural areas through two-level interaction between monitoring data and finite element model in Example 1.

[0058] Figure 3 This is a schematic diagram illustrating the interactive analysis principle between the nonlinear analysis of the substructure and the equivalent linear elastic analysis of the main structure in Example 1.

[0059] Figure 4 The flowchart of the adaptive sparse matching tracking algorithm in Example 1 is shown below;

[0060] Figure 5 This is a schematic diagram of the interaction process between the main structure and the substructure in Example 1;

[0061] Figure 6 This is a flowchart of the structural safety assessment method based on the finite element model of the main structure in Example 1;

[0062] Figure 7 This is a schematic diagram of the analysis and calculation system that integrates monitoring data with the finite element model in Example 3;

[0063] Figure 8 This is a diagram showing the experimental setup and sensor layout in Example 1;

[0064] Figure 9 This is a schematic diagram of the fine identification of substructures through the two-level fusion of monitoring data and finite element model in Example 1;

[0065] Figure 10 This is a diagram showing the results of a detailed structural analysis and evaluation of key areas in Example 1;

[0066] Figure 11 The following diagram shows the evaluation results of the main structure in Example 1, where (a) is the result of displacement gauge 3 and (b) is the result of displacement gauge 4.

[0067] Figure 12 This is a diagram showing the experimental setup and sensor layout in Example 2;

[0068] Figure 13 This is a flowchart of the target perception-guided substructure interaction method in Example 2;

[0069] Figure 14 The following diagram shows the substructure analysis results in Example 2, where (a) is the result of condition 1, (b) is the result of condition 2, (c) is the result of condition 3, and (d) is the result of condition 4.

[0070] Figure 15 The diagram shows the results of the main structure interaction analysis in Example 2, where (a) is the result of working condition 1, (b) is the result of working condition 2, (c) is the result of working condition 3, and (d) is the result of working condition 4.

[0071] The components include: 1. Finite element information extraction module; 2. Mathematical equation construction module; 3. Boundary condition solution module; 4. Twin structure fine identification module; 5. Correction feedback module; 6. Loading device; 7. Camera; 8. Reinforced concrete beam; 9. Displacement gauge; and 10. Calibration plate. Detailed Implementation

[0072] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0073] The definitions involved in this application are as follows:

[0074] Key areas and non-key areas: The main structure consists of two parts: key areas and non-key areas. Key areas are the parts that require special attention and detailed identification, and are selected according to the specific application scenario.

[0075] Substructure and main structure: The main structure is the overall structure of the target building, and the substructure is the structure corresponding to the aforementioned key areas.

[0076] Twin substructure finite element model and global model: The twin substructure finite element model is a finite element model established for the aforementioned substructure, and the global model is a finite element model established for the aforementioned main structure. Compared with the global model, the twin substructure finite element model is established using solid elements with higher precision, and the finite element model includes multiple set nodes.

[0077] Example 1

[0078] To address the problems of the aforementioned existing technologies, this embodiment provides a targeted perception-guided twin substructure interaction method to achieve accurate analysis of critical areas and rapid assessment of the overall main structure. First, addressing the bottleneck problem of difficulty in integrating monitoring data and finite element models, this method achieves two-level fusion of monitoring data and finite element models through techniques such as mathematical equations, mathematical equation solving, and substructure model enhancement, enabling refined analysis of key structural areas. Then, addressing the issue of low efficiency in structural analysis of large-scale structures with complex stress states by establishing a refined overall finite element model, this method provides an interactive analysis theory that combines nonlinear analysis of substructures with equivalent linear elastic analysis of the main structure.

[0079] See Figure 1 This method mainly consists of two parts: the method of fusing measured data with finite element analysis and the method of interactive analysis between substructure and main structure. The method of fusing measured data with finite element analysis corresponds to steps S1-S7, and the method of interactive analysis between substructure and main structure corresponds to steps S8-S10.

[0080] See Figure 2 The target-aware-guided twin structure interaction method includes the following steps:

[0081] Step S1: Divide the main structure to be analyzed into key areas and several non-key areas. The key areas can be divided based on the areas with multiple defects found during the structural inspection process or the vulnerable areas (usually the parts with the greatest stress) identified in the mechanical analysis.

[0082] Step S2: Extract multi-source monitoring data and finite element influence line information (i.e., from key areas and non-key areas respectively) Figure 1 (Overall measured data in the middle).

[0083] Step S3, Integration of Monitoring Data from Non-Key Areas with the Finite Element Model at the First Level: Establishing mathematical equations for the limited monitoring data from non-key areas and the finite element influence line information, corresponding to... Figure 1 This involves constructing a mathematical model of the overall data and mechanical information. This step includes the following sub-steps:

[0084] Step S31: Perform influence line analysis on the simplified global model (i.e., the large-scale finite element model) to obtain the influence lines of its physical parameters such as rotation angle, displacement, and strain, and construct its influence line matrix W = [D n×n ;R n×n E n×n ]. Among them, D n×n R n×n and E n×n These represent the displacement influence line matrix, rotation influence line matrix, and strain influence line matrix, respectively.

[0085] Step S32: Establish a mathematical model of the relationship between structural response (rotation, displacement, strain, etc.), influence line matrix, and load:

[0086] [x n×1 ;θ n×1 ε n×1 ] = [D n×n ;R n×n E n×n ]·F n×1

[0087] In the formula, x n×1 Let θ be the nodal displacement value. n×1 ε is the node rotation value. n×1 F is the strain displacement value. n×1 For nodal loads.

[0088] Step S33: Only the row and column data related to non-key areas in the above mathematical equations are retained. Based on this, the influence of multiple monitoring data on the equation solution is eliminated through normalization coefficients (α, β, γ are normalization coefficients), ultimately establishing a mathematical equation that integrates the monitoring data of non-key areas with the finite element model:

[0089]

[0090] In the formula, For M displacement monitoring data points in non-critical areas of the structure, This refers to monitoring data at N corners in non-critical structural areas. The data consists of L strain monitoring data points for non-critical areas of the structure.

[0091] Step S4, as follows Figure 3 As shown, based on the constructed mathematical equations, the above mathematical equations are transformed into an NP-hard non-convex combinatorial optimization problem through mathematical derivation.

[0092] Considering that solving the above underdetermined equations usually involves a certain error, a small error term is introduced, further expressing the mathematical equation as: Y = AF + E. Where, A=[αD M×n ;βR N×n γE L×n E represents the small error term in solving the underdetermined equation.

[0093] Singular value decomposition of the influence line matrix A can be expressed as: A = UΣV H By using singular value decomposition, the (M+N+L)*n matrix A is decomposed into a (M+N+L)*(M+N+L) unitary matrix U, a (M+N+L)*n diagonal matrix Σ, and the conjugate transpose of an n*n unitary matrix V. H ;

[0094] Projecting the multi-source monitoring data Y onto the subspace span(A) spanned by the column vectors of the influence line matrix A can be represented as follows:

[0095] Proj A (Y)=U(U H U) -1 U H Y;

[0096] After the above steps, the original mathematical equation Y = AF + E can be transformed into: Proj A (Y)=Proj A (AF)+Proj A (E), expanded as follows:

[0097] U(U H U) -1 U H Y = U(U H U) -1 U H AF+U(U H U) -1 U H E = AF + U(U H U)-1 U H E

[0098] Let y = U(U H U) -1 U H Y,e=U(U H U) -1 U H E, then the original mathematical equation Y = AF + E can be expressed as: y = AF + e. Since ||U(U H U) -1 U H If E‖≤‖E‖, it means that after projection, errors caused by noise can be effectively suppressed, further improving the accuracy of solving the equation.

[0099] Because F is sparsity, the above mathematical equation has a unique solution and can be transformed into a minimum l0 norm optimization problem: min‖F‖0, st‖y-AF‖<<e. Since this problem is an NP-hard nonconvex combinatorial optimization problem, it can be solved using the adaptive sparseness matching and tracking algorithm provided later.

[0100] Step S5, as follows Figure 4 As shown, the above mathematical equations are solved based on the adaptive sparse matching tracking algorithm provided by this invention, thereby realizing the boundary conditions of the key region (i.e. Figure 1 High-precision solution for neutron structure boundary conditions. The algorithm includes the following steps:

[0101] Step S501, Input: Influence line matrix A, monitoring data Y;

[0102] Step S502: Perform singular value decomposition on the influence line matrix, and project Y onto the subspace spanned by the column vectors of matrix A, i.e.: y = Proj A (Y);

[0103] Step S503, initialize r0 = y, Λ0 = φ, t = 1, error max =1E-2,K=size(A,2)*0.1;

[0104] Step S504, calculate the correlation coefficient u = abs[A T r t-1 Select the 2K maximum values ​​in u, and form a set J0 (column number set) with the corresponding column numbers j of a;

[0105] Step S505, let Λ t =Λ t-1 ∪J0, A t =A t-1 ∪a j(j∈J0);

[0106] Step S506, find y = A t θ t ,Right now

[0107] Step S507, A t The corresponding K term is denoted as a. tK The column number corresponding to 'a' is denoted as Λ. tK Update set Λ t =Λ tK ;

[0108] Step S508, calculate and update the error:

[0109] Step S509, t = t + 1, if t ≤ 2K, return to step S503 to continue iteration;

[0110] Step S510: If the condition t≤2K is not met, update the sparsity K=K+ceil(0.02*size(A,2)), where size(A,2) refers to the number of columns of matrix A.

[0111] In step S511, if the sparsity exceeds K = size(A,2)*0.5 or the error is less than the threshold Threshold, proceed to step S6; otherwise, return to step S503.

[0112] In the formula, t is the number of iterations. Let J0 be an empty set, and J0 represent the index found in each iteration. t Let L be the set of indices for the t-th iteration (with L elements). t ), a j Let A be the j-th column of the influence line matrix A. t ={a j}(j∈∧ t ) indicates the set by index ∧ t The set of columns of the selected influence line matrix A, θ t For L t A column vector of size ×1, where the symbol ∪ represents the set union operation.

[0113] Conventional matching-tracking algorithms require prior knowledge of the sparsity of the terms to be solved. However, in practical applications, sparsity is often difficult to obtain and needs to be estimated. If the value of K is underestimated, the algorithm's ability to solve the problem accurately will decrease or even disappear, causing the algorithm to fail to converge. If the value of K is overestimated, both the robustness and accuracy of the algorithm will decrease, increasing the solution error. To address this problem, this embodiment provides an adaptive sparsity method. By iteratively changing the sparsity of the terms to be solved, the sparsity with the highest accuracy obtained through multiple iterations is used as the sparsity of the terms to be solved, effectively avoiding the need to know the sparsity in advance and achieving accurate solutions to mathematical equations.

[0114] Step S6: In the finite element analysis software, a finite element model of the twin structure corresponding to the key area is established using higher-precision solid elements, and a more refined finite element mesh is generated according to the actual analysis needs.

[0115] Step S7: Integration of key area monitoring data with the second level of the finite element model: Establishing the reduction relationship between monitoring data such as external crack width and internal steel corrosion rate and structural deterioration constitutive model; considering existing cracks, corrosion, and other structural defects in key areas through the crack weakening element method and steel corrosion deterioration constitutive model, thus constructing a twin substructure; conducting preliminary calculations of the substructure based on the boundary conditions obtained in Step S5; and further, using the densely distributed strain, displacement, and other monitoring data in key areas, employing model correction methods to correct the material properties of key areas, achieving refined modeling of the substructure considering cracks and corrosion. This step corresponds to... Figure 1 Based on local measured data, a reduction relationship is constructed for intelligent crack detection. This step includes the following sub-steps:

[0116] Step S71: Establish the reduction relationship between the detection data such as the corrosion rate of the internal steel bars and the structural deterioration constitutive model. Based on the deterioration constitutive model of the corroded steel bars and the corrosion rate detection data, reduce the material parameters such as the interface area, yield strength, tensile strength and elastic modulus of the internal steel bars to account for the influence of the internal steel bars on the structure.

[0117] Step S72 establishes the reduction relationship between external crack width and other detection data and the stiffness of weakened elements. Based on specifications and crack width, the reduction coefficient of the weakened elements is calculated, and cracks are simulated at the crack locations using the weakened element method. It is worth noting that the CDP model is still used to simulate the plastic behavior of the concrete beam. The Young's modulus and tensile strength in the crack region are both weakened material properties, while the concrete compressive strength remains unchanged. The reduction relationship refers to the material properties of the structure, the material constitutive model, and the material constitutive parameters required for finite element calculations.

[0118] Step S73: Based on this, using the densely distributed strain, displacement and other monitoring data in the key areas, the material properties of the key areas are corrected using existing model correction methods.

[0119] The substructure model update method provided in this embodiment can meet the needs of various structural analyses. This method fully considers measured data such as structural corrosion rate and crack width, and realizes the update of material parameters of structural defects by integrating the structural deterioration mechanical model.

[0120] Step S8: Based on the above steps, a refined analysis of the structural mechanical state of the key areas is achieved by establishing a twin substructure finite element model that considers the impact of structural degradation.

[0121] Step S9: Use the refined twin substructure finite element model to obtain the boundary node reaction force and material properties of the key area, and transfer the boundary reaction force and material properties back to the main structure.

[0122] like Figure 5 As shown, the interaction analysis between the substructure and the main structure is performed in steps S9-S10.

[0123] Step S10: Calculate the correction force and apply it as an equivalent external load to the corresponding nodes of the main structure to complete the displacement coordination between the main structure and the substructure. This completes the interaction between the refined twin substructure finite element model and the global model, realizing the equivalent linear elastic analysis of the main structure (corresponding to the global finite element model).

[0124] The theoretical derivation process for steps S9-S10 is as follows:

[0125] For the element integration region of the structure, the principle of virtual work can be obtained as follows:

[0126]

[0127] In the formula, σ is the Cauchy stress tensor, ε is the Green strain tensor, b is the body force, ρ is the material density, η is the damping coefficient, u is the displacement, and Ω is the displacement. e Let S be the integral field Ω. e The boundary, where t is the boundary force.

[0128] Add to both sides of the above equation (D e (where the material stiffness is), then:

[0129]

[0130] In local coordinates, the relationship between the displacement of any point within an element and the displacement of the element nodes is expressed as: u = N e v e The relationship between strain and element nodal displacement is: ε = Be v e In the formula, u is the displacement of any point within the element, and N... e For shape functions, v e Let B be the displacement in local coordinates, ε be the strain at any point within the element, and B be the displacement in local coordinates. e Here is the strain matrix.

[0131] Therefore, the equivalent motion equations of the element in global coordinates can be obtained:

[0132] k e u e =f e +f e c

[0133] f e c =k e u e -r e

[0134] In the formula, u e , and Let m represent nodal displacement, velocity, and acceleration, respectively. e c e k e f e f e c and r e This represents the element's mass matrix, damping matrix, initial stiffness matrix, external forces, nonlinear correction forces, and internal forces. Specifically:

[0135] By integrating the equivalent motion equations of all elements, the equivalent linear elasticity analysis governing equations of the overall structure in global coordinates can be obtained:

[0136] K0U=F+F c

[0137]

[0138] In the formula, U represents the nodal displacement of the structure, K0 and F are the stiffness matrix and external force of the structure, respectively, and N e N represents the total number of units in the structure. s This represents the number of nonlinear units. Specifically, it can be expressed as:

[0139] The following describes the solution of this embodiment by constructing an experimental scenario.

[0140] like Figure 8As shown, the experiment utilized an MTS hydraulic loading device to conduct a four-point bending test on a reinforced concrete beam coupled with cracks and corrosion. The experiment employed a two-point loading method to ensure pure bending deformation at mid-span, and the flexural bearing capacity of the beam was evaluated by fusing detection and monitoring data. Load control was used during the loading process, recording crack propagation every 5 kN, and simultaneously recording data from long-gauge fiber optic sensors, strain gauges, and displacement gauges. (a) shows a schematic diagram of the constructed scenario, and (b) shows a cross-sectional schematic diagram of the reinforced concrete beam.

[0141] Sensor deployment follows the principle of regional distribution, meaning that sensors are densely deployed in key areas, while fewer sensors are deployed in non-key areas. Sensor deployment is as follows: Figure 8 As shown, a total of 12 long-gauge fiber optic sensors are deployed, namely LS1-LS12 in the figure. To monitor the deformation of the reinforced concrete beam, five displacement gauges are installed at the bottom of the beam, namely displacement gauges 1#-5# in the figure. To focus on monitoring the performance of the corrosion elements, five strain gauges are installed on one side of the beam, spaced 40cm apart. The industrial camera is mainly used to record the crack development in the reinforced concrete beam.

[0142] like Figure 9 As shown, according to the structural key area refinement analysis method of two-level fusion of inspection and monitoring data and finite element model disclosed in this invention, the following steps were carried out on the reinforced concrete structure in Example 1: (1) Based on the inspection results of the reinforced concrete beam, the structure to be analyzed was divided into key areas (substructures) and several non-key areas; (2) Multi-source monitoring data and finite element influence line information of key areas and non-key areas were extracted respectively; (3) Mathematical equations of a small amount of monitoring data and finite element influence line information of non-key areas were established; (4) Based on the derivation of the mathematical equations, the above mathematical equations were transformed into an NP-hard non-convex combinatorial optimization problem; (5) The above were implemented based on the adaptive sparsity matching tracking algorithm provided in this embodiment. (6) Solve the mathematical equations to achieve high-precision solution of boundary conditions in key areas; (7) Use higher precision solid elements to establish a refined finite element model of key areas and divide a more refined finite element mesh according to actual analysis needs; (8) Establish the reduction relationship between the detection data such as the width of external cracks and the corrosion rate of internal steel bars and the structural deterioration constitutive model, and consider the existing cracks, corrosion and other structural defects in key areas through the crack weakening element method and the steel bar corrosion deterioration constitutive model to establish a twin substructure; on this basis, based on the densely distributed strain, displacement and other types of monitoring data in key areas, use the model correction method to correct the material properties of key areas; (9) Based on the above steps, realize the refined analysis of the mechanical state of the key area structure.

[0143] like Figure 10As shown, the finite element model of the key area established by the method disclosed in this invention has high accuracy, and the analysis results of the key area are basically consistent with the experimental structure. Furthermore, the crack distribution in the finite element model of the key area is basically consistent with the experimental distribution. The cracks are mainly distributed in the mid-span, and obvious main cracks appear near the corrosion, where the element stiffness and bearing capacity are significantly reduced.

[0144] Following the method disclosed above, the results of the main structure interactive analysis are as follows: Figure 11 As shown in the figure, the load-displacement curves of the nonlinear analysis of the substructure and the equivalent linear elastic analysis of the main structure revealed in this embodiment are basically consistent with the measured load-displacement curves and the load-displacement curves of the refined finite element model, verifying the accuracy of the load-bearing capacity assessment results of the concrete beams by the method proposed in this paper. Regarding analysis efficiency, the global refined finite element model requires 15 minutes and 53 seconds to complete one analysis, while the substructure interactive analysis method proposed in this paper only requires 9 minutes and 3 seconds, greatly improving the efficiency of analysis and evaluation.

[0145] This method has the following advantages:

[0146] (1) To address the bottleneck issue of the difficulty in integrating and communicating inspection and monitoring data with finite element models, this paper proposes methods such as establishing mathematical equations to establish the intrinsic connection between non-critical areas and the influence line model of the finite element model, and improving the finite element model of critical areas considering internal corrosion and external cracks. Through two-level integration of inspection and monitoring data and finite element models, the modeling and analysis accuracy of finite element models for key structural parts is effectively improved. The analysis accuracy of key structural areas is improved by more than 10% through the integration of inspection and monitoring data.

[0147] (2) When it is difficult to deploy sensors to measure the physical conditions of the boundary in key areas of some structures, this invention establishes a mathematical equation relating the monitoring data of non-key areas to the finite element influence lines, and achieves high-precision solution of the boundary conditions in key areas through an adaptive sparse matching tracking algorithm. This method only requires a small amount of monitoring data from non-key areas to effectively solve the boundary conditions in key areas, solving the problem that it is difficult to deploy sensors to measure the boundary of complex structures.

[0148] (3) To address the problem of low efficiency in structural analysis of large-scale structures with complex stress states by establishing a comprehensive refined finite element model, this invention innovatively proposes an interactive analysis method combining refined analysis of key areas and equivalent linear elastic analysis of the main structure. This transforms the time-consuming analysis of establishing a comprehensive refined model of the entire structure with global damage considerations into nonlinear analysis of key areas and simplified equivalent linear elastic analysis of the main structure. This method eliminates the need to establish a comprehensive refined model of the entire structure. Through interactive analysis of key areas and the main structure, the analysis and evaluation of the structure can be completed. It has advantages such as fast analysis speed and accuracy, improving efficiency by more than 15%.

[0149] Example 2

[0150] To further verify the feasibility of the method in steel structures, this paper conducts experimental research using a scaled-down model of a tied-arch steel bridge. Figure 12 As shown, the arch bridge is approximately 5.2m long, 0.875m wide, and 0.646m high. The main material of the arch bridge is Q235 steel, and it mainly includes tie beams, crossbeams, arch ribs, and supports. The tie beams have an H-section of 125*125*6.5*9 mm, the crossbeams have an H-section of 100*100*6*8 mm, and the arch ribs have a cross-section of... The strain sensors are mainly deployed using a regional distribution method, with densely packed sensors in key areas at the mid-span and fewer sensors in other areas. The strain sensors are long-gauge fiber optic sensors with a gauge length of 0.26m. Nine displacement and rotation measurement points are placed at the beam nodes. Loading devices are used in the middle of beams P4 and P7, with four load levels set: 20kN, 40kN, 52kN, and 63kN.

[0151] like Figure 13 As shown, based on the aforementioned targeted perception-guided twin substructure interaction method, the following steps are performed: (1) Establish a mathematical model of bridge monitoring data and large-scale finite element model mechanical information; (2) Integrate singular value decomposition (SVD) and adaptive sparsity to propose a new compressed sampling matching pursuit algorithm to accurately solve the underdetermined equation, achieving high-precision solution of structural boundary displacement; (3) Based on large-scale finite element simulation, combined with the characteristics of structural stress analysis and structural detection results, select key areas of structural focus and establish a refined substructure model using solid elements, and conduct independent analysis of the refined substructure model based on the boundary conditions obtained above; (4) Transform the boundary reaction force of the substructure into the equivalent external load of the main structure and conduct equivalent linear elastic analysis of the main structure to achieve interactive analysis between the substructure and the main structure. Finally, the information transfer between "monitoring data - finite element model" and "overall large-scale model - local refined model" is realized.

[0152] like Figure 14 As shown, the proposed method can effectively analyze the stress state of the substructure, and the strain under four different loading conditions agrees well with the experimental results. Figure 15As shown, based on the numerical substructure theory, the boundary forces of the substructure are transformed into equivalent external loads of the main structure, and an equivalent linear elastic analysis is performed on the main structure. A global analysis is then conducted based on the modified finite element model of the main structure, yielding its displacement response curve. The load-displacement curve based on the large-scale finite element model is basically consistent with the measured load-displacement curve. Specifically, under the maximum load (63kN), the measured displacement of the bridge structure is less than the theoretically calculated displacement, the structural verification coefficient η<1, the structural bearing capacity meets the requirements, and the structure is safe. This embodiment further verifies the effectiveness of the proposed method in the field of steel structures.

[0153] This invention employs the concept of "targeted perception," which, based on the characteristics of existing structural health monitoring systems, specifically divides key areas of interest into numerical substructures. This allows for the construction of twin substructure finite element models using more refined solid elements, enabling "secondary analysis." During this "secondary analysis," on the one hand, corresponding structural damage mechanisms can be introduced to address specific defects, allowing for focused analysis of key areas. On the other hand, monitoring data from these key areas can be fully utilized through mathematical equations and model enhancement methods to achieve comprehensive integration of the monitoring data with the finite element model, enabling refined analysis of the mechanical properties of the substructures in these key areas.

[0154] Example 3

[0155] Compared to Example 1 or Example 2, the main structure in this example has no defects, therefore only the substructure material is updated based on step S73. This method has good practicality and is suitable for modifying various structural models such as concrete and steel structures.

[0156] Example 4

[0157] Building upon the aforementioned embodiments, this embodiment provides a method for assessing load-bearing capacity safety using the aforementioned targeted perception-guided twin substructure interaction method. Utilizing the analysis method for interaction between the substructure and the main structure in the aforementioned embodiments, monitoring data is indirectly integrated into the main structure through the interaction between the substructure and the main structure. Structural safety assessment based on the established finite element model of the main structure achieves both accuracy and efficiency. Figure 6 As shown, the sub-evaluation method based on the finite element model of the main structure is specifically reflected in: obtaining the theoretically calculated displacement U through the finite element model of the main structure. C The measured displacement U obtained through the actual structural health monitoring system T Displacement U calculated using comprehensive theoretical methods C and measured displacement U T Calculate the verification coefficient A structural safety assessment is conducted based on this verification coefficient. If the verification coefficient η ≤ 1, the structure is determined to be in a safe operating condition; if the verification coefficient η > 1, the structural bearing capacity is determined to be insufficient.

[0158] Example 5

[0159] See Figure 1 This embodiment provides a targeted sensing-guided twin structure interaction system to achieve analysis and calculation by fusing detection and monitoring data with a finite element model. The system includes:

[0160] Automatic Finite Element Information Extraction Module: This module is used to perform influence line analysis on simplified finite element models. It mainly has functions such as batch modification of nodal loads, batch submission of jobs, and batch extraction of influence line information such as displacement, strain, and rotation angle. It helps to improve the efficiency of finite element information extraction and realize the automatic extraction of multi-source monitoring data and finite element influence line information.

[0161] The mathematical equation construction module is used to construct mathematical equations that relate to the monitoring data, finite mechanical information and nodal loads. The module is used to solve the above mathematical equations and obtain the boundary conditions of important regions (substructures). This module integrates singular value analysis, subspace projection and adaptive sparsity matching tracking algorithm to achieve accurate solution of the physical boundary of the substructure of the important region to be analyzed.

[0162] Boundary condition solution module: This module is used to construct mathematical equations relating monitoring data, finite element information, and nodal loads. It features automatic data reading, automatic construction of complete equations, and automatic construction of underdetermined equations, achieving the first level of fusion between monitoring data from non-key areas and the finite element model.

[0163] The twin substructure fine-grained identification module is used to create more accurate solid element models for key areas and to consider common structural defects such as cracks and corrosion. This module has functions such as fine-grained solid element modeling, crack and corrosion defect correction, and substructure finite element model correction, enabling fine-grained identification of key areas.

[0164] Correction Feedback Module: This module acquires the boundary node reactions and material properties of key areas and transmits these properties back to the main structure. It features automatic acquisition of node reactions and material properties, automatic calculation of correction forces, and real-time data transmission. The correction forces are then applied as equivalent external loads to the corresponding nodes of the main structure, enabling secondary analysis of the main structure.

[0165] The above modules organically integrate the aforementioned targeted perception-guided twin structure interaction method, achieving a high degree of automation and enabling rapid and efficient bridge analysis and evaluation based on monitoring data fusion.

[0166] Example 6

[0167] This embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores at least one program, the program including instructions for executing the target perception-guided twin structure interaction method as described in the foregoing embodiments.

[0168] Example 7

[0169] This embodiment provides a computer-readable storage medium including at least one program executable by at least one processor of an electronic device, the program including instructions for performing the target perception-guided twin structure interaction method of the foregoing embodiments.

[0170] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A targeted perception-guided twin structure interaction method, characterized in that, Includes the following steps: Obtain multi-dimensional monitoring data and finite element influence line data of the main structure, wherein the main structure is divided into key areas and non-key areas; Based on monitoring data and finite element influence line data from non-key areas, the boundary conditions of key areas are solved using an adaptive sparsity matching tracking algorithm to achieve the first level of data fusion. A refined twin substructure finite element model considering the impact of structural degradation was established for the key area. Based on the monitoring data and finite element influence line data of the key area, the material properties of the refined twin substructure finite element model were corrected to achieve the second level of data fusion. The correction force is calculated based on the boundary conditions and material properties of the key area. The correction force is then applied as an equivalent external load to the nodes of the global finite element model, thus completing the interaction between the refined twin structure finite element model and the global finite element model. The adaptive sparse matching tracking algorithm includes the following steps: Influence line matrix is ​​constructed based on finite element influence line data and singular value decomposition is performed. Multivariate detection data are projected onto the subspace formed by the column tensors of the influence line matrix. Based on the projection of multivariate detection data onto the subspace formed by the column tensor of the influence line matrix, the sparsity of the term to be solved is changed by iterative calculation, and the sparsity with the highest solution accuracy after multiple iterations is taken as the sparsity of the term to be solved. The adaptive sparse matching tracking algorithm specifically includes: Step 1: Input the influence line matrix A and monitor the data Y; Step 2: Perform singular value decomposition on the influence line matrix and project the monitoring data Y onto the influence line matrix. The subspace spanned by column vectors, i.e. ; Step 3, Initialization , , ; Step 4: Calculate the correlation coefficient ,choose middle There are a maximum value, and the maximum value is corresponding to... Column number Construct a set of column numbers ; Step 5, let , ; Step 6, Calculation ; Step 7, , corresponding Item is denoted as , The corresponding column number is denoted as Update the set ; Step 8: Calculate and update the error ; Step 9 ,if Otherwise, return to Step 2 to continue the iteration; Step 10, Update Sparsity ; Step 11: If the sparsity exceeds K = size(A,2) If the error is 0.5 or less than the preset threshold Threshold, then the equivalent nodal force is output. If the boundary condition is set as a key area, proceed to Step 4. in, For the number of iterations, It is an empty set. This represents the index obtained in each iteration. Indicates the first The set of indices for the next iteration, and The number of elements is , The influence line matrix A is the first List, Indicates a collection by index Selected Influence Line Matrix The set of columns, for Column vectors, symbols This indicates the set union operation.

2. The targeted perception-guided twin structure interaction method according to claim 1, characterized in that, The process of solving the boundary conditions of the key region includes: Establish mathematical equations for detection data and finite element influence line data in non-key areas; The mathematical equations are transformed into NP-hard nonconvex combinatorial optimization problems, which are then solved using the adaptive sparse matching and tracking algorithm.

3. The targeted perception-guided twin structure interaction method according to claim 1, characterized in that, The structural degradation effects include the effects of external cracking and internal corrosion.

4. The targeted perception-guided twin structure interaction method according to claim 3, characterized in that, The process of constructing the refined twin structure finite element model considering the effects of structural degradation includes: The crack weakening element method is used to establish the first reduction relationship between the width of external cracks in the main structure and the stiffness of the weakening element, so as to realize the modeling of the impact of external crack defects. Based on the material parameters inside the steel corrosion and deterioration constitutive model, a second reduction relationship between the steel corrosion rate inside the main structure and the structural deterioration constitutive model is established to model the impact of internal corrosion damage. A refined twin structure finite element model is constructed based on the first reduction relationship and the second reduction relationship.

5. The targeted perception-guided twin structure interaction method according to claim 1, characterized in that, The key areas are those with multiple defects discovered during the inspection process or those vulnerable areas identified through mechanical analysis. The non-key areas are the other parts of the main structure excluding the key areas.

6. The targeted perception-guided twin structure interaction method according to claim 1, characterized in that, The multivariate monitoring data includes nodal displacement values, nodal rotation values, and strain displacement values, and the material properties include at least one of the constitutive parameters of concrete, steel reinforcement, and steel.

7. An application method of the targeted perception-guided twin structure interaction method as described in any one of claims 1-6, characterized in that, Includes the following steps: The interaction between the refined twin structure finite element model and the global finite element model is accomplished using the target perception-guided twin structure interaction method. The theoretical displacement of nodes is calculated using an interactive global finite element model. Obtain the measured displacement of the node; The bearing capacity of the main structure is assessed for safety based on the theoretical and measured displacements.

8. A targeted perception-guided twin substructure interaction system, characterized in that, A method for implementing the target-aware guided twin structure interaction method as described in any one of claims 1-6 includes: The finite element information extraction module (1) is used to obtain finite element influence line data; The mathematical equation construction module (2) is used to construct mathematical equations relating monitoring data, finite mechanics information and nodal loads. Boundary condition solution module (3) is used to solve the boundary conditions of the key area based on the finite element influence line data of the non-key area of ​​the main structure and the obtained multivariate detection data, for the mathematical equation, through a preset storage medium, wherein the storage medium includes instructions for implementing the adaptive sparsity matching tracking algorithm; The twin substructure fine identification module (4) is used to establish a fine twin substructure finite element model that takes into account the structural degradation effect for key areas of the main structure; The correction feedback module (5) is used to correct the material properties of the refined twin structure finite element model based on the monitoring data and finite element influence line data of the key area, calculate the correction force based on the boundary conditions of the key area and the material properties, and apply the correction force as an equivalent external load to the nodes of the global finite element model.

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