Quantitative analysis method for influence of bridge diseases on structural performance

Abstract

The invention relates to a quantitative analysis method for influence of bridge diseases on structural performance, which comprises the following steps of: acquiring bridge information of bridges of the same type in a certain area, and determining an inspection report; constructing a bridge structure analysis model according to the bridge information; disease information is extracted from a fixed inspection report of a bridge, diseases are divided into size attenuation type diseases, rigidity reduction type diseases and non-structural diseases, and the disease information comprises disease types, positions, sizes and technical conditions; meanwhile, according to the disease position and the node coordinates of the divided structure analysis units, the structure analysis units influenced by the disease are determined; according to different types of diseases, utilizing the correction model to calculate related parameters after the influence of the diseases; and correcting the element stiffness matrix by using the related parameters after the influence of the diseases, and integrating to obtain a global stiffness matrix S so as to obtain a strain matrix d of the bridge. The problem of disjunction of disease information and structural analysis is solved, and the blank between traditional disease qualitative evaluation and SHM refined analysis is filled up.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering structure health monitoring and assessment, and in particular to a quantitative analysis method for the influence of bridge diseases on structural performance, which is used to quantify the influence of bridge diseases on structural performance and realize the reuse of regular inspection data. Background Art

[0002] As the service life of bridges increases, bridge diseases gradually emerge and become an important factor threatening the safety of infrastructure. Bridge condition monitoring and health assessment have always been the focus of research at home and abroad.

[0003] Currently, the regular inspection system is the main method of bridge maintenance management worldwide. This method records and evaluates bridge defects through manual inspections or equipment such as drones, and rates the defects according to standardized guidelines (such as AASHTO, the UK's "Guidelines for Highway Bridge Inspection", and China's "Technical Specifications for Highway Bridge and Culvert Maintenance") to assess the health of the bridge. However, the main problems of the regular inspection system include: (1) strong subjectivity of data: the evaluation process is highly dependent on the experience of the inspectors, and the level of defect quantification is low; (2) lack of quantitative correlation between defects and performance: defect information is more used for grade assessment rather than directly analyzing its actual impact on structural stiffness and strength; (3) low data utilization efficiency: inspection data is usually not effectively integrated into the bridge information model, nor combined with structural analysis.

[0004] At the same time, the current mainstream analysis methods for scheduled inspection diseases are limited to qualitative analysis based on experience, including:

[0005] (1) Status grade assessment: Disease status is usually scored using empirical formulas or rating tables, such as disease severity grades 1 to 5, for reference in maintenance decisions;

[0006] (2) Defect distribution statistics: Defect records are used to generate statistical reports to evaluate the overall condition of the bridge;

[0007] (3) Disease trend prediction: Use historical disease data to simulate disease development trends and make condition predictions;

[0008] (4) Repair priority sorting: Determine the bridges and components that need to be repaired first based on the damage score and bridge importance.

[0009] Another method for bridge maintenance management during the operation and maintenance phase is bridge health monitoring (SHM) technology. This technology uses sensors to collect structural responses (such as displacement, stress, and vibration) in real time to assess the health status of bridge structures. Although the SHM system provides high-precision real-time data, the following issues limit its widespread application: (1) Limited coverage: Due to the high cost of sensor equipment, only a small number of important bridges in the world have deployed SHM systems, and ordinary bridges still rely mainly on manual inspections; (2) Data integrity issues: Sensor failures, environmental interference, etc. lead to data loss or distortion, affecting the accuracy of the assessment; (3) The characteristics of the disease are disconnected from the structural performance: SHM data cannot directly reflect the characteristics of the disease, such as crack width and spalling range.

[0010] In recent years, domestic and foreign scholars have conducted many studies in this field, mainly focusing on the integration of disease information, that is, supporting the standardized integration of disease data through IFC standards and extended entities. Some scholars also use finite element analysis to accurately model the impact of diseases, but this requires high-cost professional analysis software support and is difficult to extend to ordinary bridges. Therefore, there is still a lack of effective conversion methods from inspection data to structural analysis models, especially in the quantitative analysis of the impact of diseases on structures. There is a technical bottleneck. In view of this, there is an urgent need for a quantitative analysis method for the impact of bridge diseases on structural performance to overcome the existing deficiencies. Summary of the invention

[0011] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a quantitative analysis method for the impact of bridge defects on structural performance, solve the problem of disconnection between defect information and structural analysis, fill the gap between traditional qualitative defect assessment and SHM refined analysis, and provide an efficient and scalable solution for bridge maintenance management.

[0012] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0013] In a first aspect, the present invention provides a quantitative analysis method for the impact of bridge defects on structural performance, the quantitative analysis method comprising the following steps:

[0014] Step 1: Obtain bridge information and regular inspection reports of bridges of the same type in a certain area to form a disease impact coefficient optimization analysis database A;

[0015] Step 2: According to the bridge information, a bridge structure analysis model is constructed based on Matrix-based methods, the structural analysis unit is divided according to the preset division rules, the node matrix and unit matrix are obtained, and the unit stiffness matrix of the structural analysis unit is calculated;

[0016] Step 3: Extracting damage information from the regular inspection report of the bridge, and classifying the damage into three categories: size attenuation type damage, stiffness reduction type damage, and non-structural damage. The damage information includes the type, location, size, and technical status of the damage;

[0017] According to the type of damage, identify how it affects the structural performance, i.e., dimensional degradation, stiffness degradation, or no structural impact;

[0018] At the same time, the structural analysis units affected by the disease are determined according to the disease location and the node coordinates of the divided structural analysis units, and the effects of different diseases on the structural analysis units on their structural characteristics are calculated;

[0019] Use the modified model according to different types of diseases to calculate the relevant parameters after the disease impact;

[0020] The unit stiffness matrix is ​​modified by the relevant parameters after the damage, and the global stiffness matrix S is obtained by integrating the node matrix with the modified unit stiffness matrix.

[0021] Step 4: Obtain the load condition P of the bridge, and use P = S·d to obtain the strain matrix d of the bridge. d is the calculation result of the structural response under the influence of the disease. Based on d, further obtain the relevant indicators of the bridge structure response under the disease condition.

[0022] For size attenuation type damage, the cross-sectional distance of the component after the reduction of the relevant structural analysis unit affected by the damage and the relevant parameters after the damage are calculated using the following formula:

[0023] I i，z,def =I i，z -∑ M y m，def 2 ·A m,def

[0024] Among them, i i，z,def I is the section distance of the z-axis of the component of the structural analysis unit i after the influence of the size attenuation type disease is combined; i，z is the original z-axis section distance of the structural analysis unit i, M is the number of size attenuation type defects on the structural analysis unit i; y m,def A is the distance between the centroid of the disease m and the structural analysis unit i; m,def is the area of ​​disease m;

[0025] Non-structural defects have no significant impact on the overall performance of the structure, and no relevant parameter correction is performed.

[0026] For the stiffness reduction type of damage, the stiffness of the components after the reduction of the relevant structural analysis units affected by the damage and the calculation formula of the relevant parameters after the damage are as follows:

[0027]

[0028] Among them, k def k is the stiffness of the component after damage; origin is the original stiffness of the component; β n，j is the reduction coefficient of the structural stiffness of the jth technical condition of the nth stiffness reduction defect, j = 1~5; N is the number of all stiffness reduction defects on the component.

[0029] For stiffness reduction type diseases, the stiffness reduction factor β n，j The determination process is:

[0030] Obtain the load conditions of a number of bridges of the same type in a certain area and the strain-related data monitored by SHM sensors.

[0031] At the same time, the locations of all stiffness reduction defects on each bridge and the technical conditions of different defects are obtained, and then the distribution data of the stiffness reduction coefficient of the defect is obtained;

[0032] The above data is used to form a reinforcement learning training database B; a reinforcement learning network is constructed;

[0033] Set the reinforcement learning action rule, i.e., β nj The value selection rule of , where j represents the technical condition level of the disease, taking an integer between 1 and 5, and n takes an integer between 1 and N, where N is the number of all stiffness reduction diseases; β n，j The value range is 0.25~1, set the interval step Δ, the initial state β n，j The value is 0.25, and each action changes β n，j =β n，j +Δ;

[0034] Each action causes the global stiffness matrix S to change, and the strain matrix d of the bridge under the corresponding load condition is obtained according to P = S·d, which is the calculation result of the structural response under the influence of the disease;

[0035] Set the reinforcement learning reward, and calculate the reward by the absolute value of the difference between the calculated result d of the structural response under the influence of the disease and the strain-related data d0 monitored by the SHM sensor, which includes two parts:

[0036] 1) Process reward: Under the current action, Than the previous action Small, reward +1;

[0037] 2) Instant reward: Under the current action, When the error is less than 1%, the reward is +10;

[0038] a t represents the action performed at time t, that is, at time t {βn，j}List value; is the absolute value of the difference corresponding to the action performed at time t;

[0039] Finally, traverse N, j to obtain the optimal {β n，j} combination, so that {β n，j} is combined as the stiffness reduction coefficient in the modified model of stiffness reduction type damage in step 3, and then the strain matrix d of the bridge is calculated.

[0040] Furthermore, the types of stiffness reduction defects include at least one of structural cracks, steel bar corrosion, interlayer delamination, and deflection.

[0041] Furthermore, the calculation formula of the global stiffness matrix is:

[0042]

[0043] Where S is the global stiffness matrix, T i is the transformation matrix from structural analysis unit to global coordinates, k i is the unit stiffness matrix of structural analysis unit i; T i T Indicates T i The transposed matrix of ; L is the number of structural analysis units.

[0044] Furthermore, the bridge information is presented in the form of a BIM model; the node matrix size is 1*NDOF, where NDOF is the degree of freedom of the entire bridge after dividing the structural analysis unit.

[0045] Furthermore, the construction of the bridge structure analysis model based on Matrix-based methods includes the following steps:

[0046] 1) Extract and analyze the global coordinates and relative positioning of bridge components in the bridge information, decompose the components according to the preset unit size, generate a number of structural analysis units, complete the coordinate conversion, divide adjacent structural analysis units by nodes, generate a node matrix with the coordinates of all nodes, and all structural analysis unit numbers and corresponding nodes constitute a unit matrix;

[0047] 2) Extract component geometry information and obtain the key geometric characteristics of each structural analysis unit, including cross-sectional area, moment of inertia, section distance, length, and geometric center position. The component is composed of several structural analysis units, and the mapping between the key geometric characteristics of the structural analysis unit and the unit matrix is ​​established;

[0048] 3) Extract the material information of the component and obtain the material elastic modulus E of each structural analysis unit eff Mapping relationship with the unit matrix;

[0049] 4) Extract support information, determine the node position coordinates corresponding to the support and the constraint type of the support (one-way or two-way), and establish the mapping relationship between the support and the node matrix;

[0050] 5) Combined with the mapping relationship between the support and the node matrix, the constraint type of the structural analysis unit is determined, and then the matrix-based methods are used to establish the unit stiffness matrix using the elastic modulus and geometric information of the material information. One structural analysis unit has one unit stiffness matrix.

[0051] In a second aspect, the present invention provides a quantitative analysis system for the impact of bridge defects on structural performance, and the system executes the steps of the analysis method.

[0052] Furthermore, the analysis system includes a disease influence coefficient optimization analysis database A, a reinforcement learning training database B, a bridge structure analysis model building block, a stiffness reduction coefficient optimization module, and a strain matrix calculation module.

[0053] The disease impact coefficient optimization analysis database A is connected with the BIM model, the SHM sensor data module, and the scheduled inspection report data module;

[0054] The bridge structure analysis model building block divides the structure analysis units based on the disease influence coefficient optimization analysis database A to establish the bridge structure analysis model;

[0055] The reinforcement learning training database B is used to store the strain data in the SHM sensor data module, the load data in the regular inspection report data module, and the disease stiffness reduction coefficient distribution data obtained in the bridge structure analysis model building block;

[0056] The stiffness reduction coefficient optimization module uses the reinforcement learning training database B to obtain the optimal stiffness reduction coefficient combination based on reinforcement learning supervised training;

[0057] The strain matrix calculation module uses the optimal stiffness reduction coefficient combination output by the stiffness reduction coefficient optimization module to correct the unit stiffness matrix in the structural analysis unit of the bridge structure analysis model, and further calculates the strain matrix d of the bridge.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1) The analysis method of the present invention can scientifically quantify the impact of defects on bridge structural performance, effectively solve the problem of disconnection between defect information and structural analysis, fill the gap between traditional qualitative defect assessment and SHM refined analysis, and provide an efficient and scalable solution for bridge maintenance management. The traditional regular inspection system relies on expert experience to rate defects, and although sensor-based health monitoring technology can obtain bridge response data in real time, its coverage and applicability are limited and it is only applicable to a few high-value bridges.

[0060] 2) The present invention integrates regular inspection data, combines it with the bridge structure analysis model, converts the damage information into a quantifiable stiffness reduction coefficient or cross-sectional characteristic change in the structural analysis model, and optimizes the coefficient of the stiffness reduction of the damage through reinforcement learning technology, so that these data are no longer just used for damage grade assessment, but become an important input for directly driving structural performance evaluation, and quantify the impact of the damage on the bridge geometry and material properties as changes in structural stiffness and internal forces, greatly improving the value of existing regular inspection data. This method not only avoids the error of subjective evaluation, but also can dynamically reflect the specific impact of the location, size and type of the damage on the overall structural performance.

[0061] 3) In addition, this method supports the automatic extraction of key information such as geometry, materials, supports and loads from the bridge BIM model, reducing manual operations in the process of establishing the analysis model and ensuring the integrity and consistency of the data. At the same time, by adopting the matrix analysis method, efficient structural performance calculation is achieved, providing engineers with key performance indicators including bridge displacement, internal force distribution and stress concentration in the damaged area, which intuitively reflects the impact of the disease on the safety and durability of the bridge, helps engineers quickly identify potential danger areas, and formulate scientific maintenance and reinforcement plans. The method of the present invention is more efficient and more applicable, and is particularly suitable for the health status assessment needs of large-scale bridge networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a structural schematic diagram of a bridge BIM model and a structural analysis unit of an embodiment. DETAILED DESCRIPTION

[0063] The present invention is described in detail below with reference to the accompanying drawings and embodiments, but they are not intended to limit the protection scope of the present application.

[0064] The quantitative analysis method of the influence of bridge defects on structural performance of the present invention comprises the following steps:

[0065] Step 1: Obtain bridge information, regular inspection reports and SHM sensor data of bridges of the same type in a certain area to form a disease impact coefficient optimization analysis database A;

[0066] The same type refers to the same materials and structural systems of the main components of the bridge.

[0067] Step 2: According to the bridge information, a bridge structure analysis model is constructed based on Matrix-based methods, the structural analysis unit is divided according to the preset division rules, the node matrix and unit matrix are obtained, and the unit stiffness matrix of the structural analysis unit is calculated;

[0068] Step 3: Extract the damage information from the regular inspection report of the bridge, and classify the damage into three categories: size attenuation type damage, stiffness reduction type damage, and non-structural damage. The damage information includes the type, location, size, and technical condition of the damage (the score of each damage), etc.

[0069] According to the type of damage, identify how it affects the structural performance, i.e., dimensional degradation, stiffness degradation, or no structural impact;

[0070] At the same time, the structural analysis units affected by the disease are determined according to the disease location and the node coordinates of the divided structural analysis units, and the effects of different diseases on the structural analysis units on their structural characteristics are calculated;

[0071] Use the modified model according to different types of diseases to calculate the relevant parameters after the disease impact;

[0072] The unit stiffness matrix is ​​modified by the relevant parameters after the damage, and the global stiffness matrix S is obtained by integrating the node matrix with the modified unit stiffness matrix.

[0073] Step 4: Obtain the load condition P of the bridge, and use P = S·d to obtain the strain matrix d of the bridge. d is the calculation result of the structural response under the influence of the disease. Based on d, further obtain the relevant indicators of the bridge structure response under the disease condition.

[0074] Among them, relevant indicators are used for structural performance evaluation and maintenance decisions, and are integrated into the original bridge BIM model.

[0075] In step 3, the disease type information extracted from the scheduled inspection report is divided into the following three categories according to the existing rules:

[0076] 1) Dimensional attenuation type defects: This type of defect mainly leads to a reduction in the effective size of structural components, such as spalling and exposed steel bars. The analysis model adopts cross-sectional remodeling technology and uses OpenCV tools to calculate the changes in effective cross-sectional area and moment of inertia to form new cross-sectional characteristics.

[0077] 2) Stiffness reduction type defects, which will weaken the material stiffness or the overall component stiffness, such as cracks and local damage. The analysis model introduces a stiffness reduction coefficient to quantify the effect of crack width and depth on component stiffness;

[0078] 3) Non-structural defects: This type of defect has no significant impact on the overall performance of the structure, such as surface weathering and slight rust. Only the location, size and shape of the defect are recorded as visual information to assist engineering decision-making.

[0079] According to the description of the location of the defect, the structural analysis unit affected by it and its position in the structural analysis unit are converted, the impact of the defect on the structural analysis unit is calculated, and the structural characteristics of the unit (such as section distance and stiffness) are corrected. According to different types of defects, the corresponding correction model of structural impact is selected. Among them, for size attenuation type defects, the section distance of the component after the reduction of the related units affected by it and the related parameters after the damage are calculated using the following formula:

[0080] I i，z,def =I i，z -∑ M y m,def 2 ·A m，def

[0081] Among them, I i，z,def I is the section distance of the z-axis of the component of the structural analysis unit i after the influence of the size attenuation type disease is combined; i，z is the original z-axis section distance of the structural analysis unit i, M is the number of size attenuation type defects on the structural analysis unit i; y m,def A is the distance between the centroid of the disease m and the structural analysis unit i; m,def is the area of ​​disease m;

[0082] The non-structural defects are integrated into the bridge information model as additional information and serve as a reference for structural performance evaluation, but do not participate in the calculation of stiffness or strength.

[0083] For the stiffness reduction type of damage, the stiffness of the components affected by the relevant units after reduction, and the calculation formula of the relevant parameters after the damage is:

[0084]

[0085] Among them, k def k is the stiffness of the component after damage; origin is the original stiffness of the component; β n，j is the reduction coefficient of the structural stiffness of the jth technical condition of the nth stiffness reduction defect, j = 1~5; N is the number of all stiffness reduction defects on the component.

[0086] Summarize the element stiffness matrix after the correction of the disease effect, and automatically calculate the global stiffness matrix of the whole bridge. The calculation formula of the global stiffness matrix is:

[0087]

[0088] Where S is the global stiffness matrix, T i is the transformation matrix from the structural analysis unit to the global coordinates (obtained using the node matrix transformation), k i is the unit stiffness matrix of structural analysis unit i; T i T Indicates T i The transposed matrix of ; L is the number of structural analysis units.

[0089] For stiffness reduction type diseases, the stiffness reduction factor β n，j The determination process is:

[0090] Obtain the load conditions of a number of bridges of the same type in a certain area and the strain-related data monitored by SHM sensors.

[0091] At the same time, the location and technical status of all stiffness reduction defects on each bridge are obtained, and then the distribution data of the stiffness reduction coefficient of the defect is obtained;

[0092] The above data is used to form a reinforcement learning training database B; a reinforcement learning network is constructed;

[0093] Set the reinforcement learning action rule, i.e., β n，j The value selection rule of , where j represents the technical condition level of the disease, taking an integer between 1 and 5, and n takes an integer between 1 and N, where N is the number of all stiffness reduction diseases; β n，j The value range is 0.25~1, set the interval step Δ, the initial state β n，j The value is 0.25, and each action changes β n，j =β n，j +Δ;

[0094] Each action causes the global stiffness matrix S to change, and the strain matrix d of the bridge under the corresponding load condition is obtained according to P = S·d, which is the calculation result of the structural response under the influence of the disease;

[0095] Set the reinforcement learning reward, and calculate the reward by the absolute value of the difference between the calculated result d of the structural response under the influence of the disease and the strain-related data d0 monitored by the SHM sensor, which includes two parts:

[0096] 1) Process reward: Under the current action, Than the previous action Small, reward +1;

[0097] 2) Instant reward: Under the current action, When the error is less than 1%, the reward is +10;

[0098] a t represents the action performed at time t, that is, at time t {βn，j , n=1,2,...,N,j=1,2,...,5} list; is the absolute value of the difference corresponding to the action performed at time t;

[0099] Finally, traverse N, j to obtain the optimal {β n，j} combination, so that {β n，j} is combined as the stiffness reduction coefficient in the modified model of stiffness reduction type damage in step 3, and then the strain matrix d of the bridge is calculated.

[0100] The reinforcement learning network, in which the environment is the bridge structure analysis model of all bridges in the disease influence coefficient optimization analysis database A, and the action is the attenuation coefficient β for different types of stiffness reduction diseases and different disease technical status levels on the same bridge n，j The value group of n，j} list, using Matrix-based methods to calculate the bridge structure response under disease conditions, the reward is set according to the absolute value of the difference between the calculated structural response result d under the influence of the disease and the structural response d0 monitored by the SHM sensor. Through supervised learning, the optimized {β n，j}combination.

[0101] The bridge structure analysis model constructed based on Matrix-based methods includes the following steps:

[0102] 1) Extract and analyze the global coordinates and relative positioning of bridge components in the bridge information, decompose the components according to the preset unit size, generate a number of structural analysis units, complete the coordinate conversion, divide adjacent structural analysis units by nodes, generate a node matrix with the coordinates of all nodes, and all structural analysis unit numbers and corresponding nodes constitute a unit matrix;

[0103] 2) Extract component geometry information and obtain the key geometric characteristics of each structural analysis unit, including cross-sectional area, moment of inertia, section distance, length, and geometric center position. The component is composed of several structural analysis units, and the mapping between the key geometric characteristics of the structural analysis unit and the unit matrix is ​​established;

[0104] 3) Extract the material information of the component and obtain the material elastic modulus E of each structural analysis unit eff Mapping relationship with the unit matrix;

[0105] 4) Extract support information, determine the node position coordinates corresponding to the support and the constraint type of the support (one-way or two-way), and establish the mapping relationship between the support and the node matrix;

[0106] 5) Combined with the mapping relationship between the support and the node matrix, the constraint type of the structural analysis unit is determined, and then the matrix-based methods are used to establish the unit stiffness matrix using the elastic modulus and geometric information of the material information. One structural analysis unit has one unit stiffness matrix.

[0107] In step 2, the bridge information is presented in the form of a BIM model. The conversion process from the bridge BIM to the structural analysis model includes extracting geometric information, material properties, and support conditions from the bridge BIM model, and converting the information into a structural analysis model through the IFC standard. The IFC file of the bridge BIM model is parsed using the Ifcopenshell tool, including:

[0108] (1) Coordinate transformation: The bridge components (IfcElement) in IfcBridge are extracted through the spatial containment relationship. The global coordinates and relative positioning of the components are analyzed using IfcLocalPlacement and IfcAxis2Placement3D. The components are decomposed according to the pre-set unit size to generate structural analysis units, which are stored using IfcStructuralMember entities.

[0109] (2) Extraction and conversion of geometric information: For solid modeled components, the specific cross-sectional shape and size of the components are obtained by extracting the IfcExtrudedAreaSolid entity. The cross-sectional geometric information is converted into cross-sectional characteristics required for engineering analysis (such as cross-sectional area, moment of inertia, cross-sectional distance, length, and geometric center position, which are key geometric characteristics) using the OpenCV tool and stored in the structural analysis model. The calculation method of the moment of inertia is as follows:

[0110] I z =∫ A y 2 dA

[0111] I y =∫ A z 2 dA

[0112] Among them I z , I y Corresponding to the moments of inertia about the z-axis and y-axis respectively.

[0113] (3) Extraction and conversion of material information: Use the IfcRelAssociatesMaterial entity to find the material information related to the component, and clarify key parameters such as material strength (such as concrete C55, steel strength grade), elastic modulus, Poisson's ratio, etc. based on rules. For components with multiple materials (such as prestressed beams), the effective material stiffness is comprehensively calculated and the information is stored in the structural analysis model.

[0114] (4) The support position is located through the IfcElementAssembly entity related to the foundation, and the support constraint type (such as fixed, hinged or sliding) is resolved using the IfcRelConnectsStructuralMember entity, and the support condition is converted into the input of the node constraint matrix.

[0115] For the structural analysis units of the bridge divided above, the unit stiffness matrix is ​​established by using the matrix method in combination with the geometric information and material information.

[0116] Example 1

[0117] See also Figure 1 , the bridge structure analysis unit of the embodiment is divided as follows Figure 1 As shown. The case bridge is a five-span continuous rigid frame bridge with a span arrangement of 58+182+265+194+70 meters. The main span crosses the Red River. The bridge deck width is 22.5 meters and a variable-section box girder design is adopted. The upper structure material is C55 concrete and 15.24mm prestressed steel strands. The bearing types include fixed bearings and sliding bearings. The bridge has been inspected regularly every year since 2013, and the traditional manual recording of disease information (such as crack width, length, etc.) is adopted.

[0118] Step 1: Collect regular inspection reports and SHM sensor data of the same type of bridges in the area to build a disease impact coefficient optimization analysis database A. In the embodiment, relevant data of all prestressed concrete elevated continuous beam bridges in XX Province are selected to build a disease impact coefficient optimization analysis database.

[0119] Step 2: According to the bridge information BIM model, a bridge structure analysis model is constructed based on Matrix-based methods.

[0120] In this embodiment, the main body of the bridge is a variable-section box girder. According to the variable-section characteristics, the girder is divided into structural analysis units of varying lengths of 2-5m; the pier of the bridge substructure is divided into structural analysis units of 5m each, and the node coordinates (x, z) of each structural analysis unit are written into the node matrix.

[0121] In the embodiment, for geometric information, the bridge body adopts Brep geometric model (Boundary Representation), which is a common model for describing complex bridge sections in the IFC standard. Its characteristic is that the geometric shape is defined by the boundary information of vertices, edges and faces. Specifically, the first step is to parse the geometric data. First, the IfcFacetedBrep entity associated with the bridge component is obtained from IfcShapeRepresentation, and its boundary definition is parsed; secondly, the IfcFace under IfcClosedShell is traversed to extract the planes or curved surfaces that make up the section in turn; finally, the three-dimensional coordinates of the vertex are obtained using IfcCartesianPoint, and the polygon of the section boundary is formed through IfcPolyLoop. The second step is to project the section contour. First, the local coordinate system where the section is located is determined according to IfcLocalPlacement; secondly, the section boundary of Brep is projected onto the cross-sectional plane to form a two-dimensional plane contour; finally, the closed path of the section projection (such as a polygon or a complex curve) is determined. In this embodiment, multiple IfcFaces of the bridge box girder are extracted and the inner and outer contours (such as the outer frame and holes of the box girder) are distinguished, and a contour hierarchy is established. In the third step, the key characteristics of the cross section are obtained through geometric calculation, including geometric information such as cross-sectional area, moment of inertia, and geometric center position.

[0122] In the embodiment, for the extraction and conversion of material information, the material entity related to the component (such as IfcMaterial or IfcMaterialLayerSetUsage) is first found through the IfcRelAssociatesMaterial entity, wherein for a single material (such as IfcMaterial), its associated IfcMaterialProperties are directly extracted, including elastic modulus (E), Poisson's ratio (ν), density (ρ), etc.; for composite materials (such as prestressed reinforced concrete), the order, thickness and properties of the material layer are obtained from IfcMaterialLayerSetUsage, and the equivalent material elastic modulus E is calculated in combination with the thickness ratio of the material layer. eff :

[0123]

[0124] Among them, E i and h i are the elastic modulus and thickness of the i-th layer of material, and n is the number of layers of the composite material. eff (or equivalent material properties for composite materials) associated to the structural analysis elements.

[0125] Combined with the mapping relationship between the support and the node matrix, the constraint type of the structural analysis unit is determined. Then, the matrix-based methods are used to establish the unit stiffness matrix using the elastic modulus and geometric information of the material information. One structural analysis unit has one unit stiffness matrix.

[0126] In this embodiment, for a structural analysis unit with two non-fixed nodes (i.e., both ends of the structural analysis unit are not constrained by supports), the calculation formula of the unit stiffness matrix k is as follows:

[0127]

[0128] Among them, E, I, and L are the elastic modulus, cross-sectional distance, and length of the structural analysis unit, respectively.

[0129] Step 3: extracting damage information from the regular inspection report of the bridge, and classifying the damage into three categories: size attenuation damage, stiffness reduction damage, and non-structural damage. The damage information includes the type, location, size, and technical status of the damage;

[0130] According to the type of damage, identify how it affects the structural performance, i.e., dimensional degradation, stiffness degradation, or no structural impact;

[0131] At the same time, the structural analysis units affected by the disease are determined according to the disease location and the node coordinates of the divided structural analysis units, and the effects of different diseases on the structural analysis units on their structural characteristics are calculated;

[0132] Use the modified model according to different types of diseases to calculate the relevant parameters after the disease impact;

[0133] The unit stiffness matrix is ​​modified by the relevant parameters after the damage, and the global stiffness matrix S is obtained by integrating the node matrix with the modified unit stiffness matrix.

[0134] In this embodiment, disease type information is extracted from the scheduled inspection report, covering three categories:

[0135] 1) Dimensional attenuation defects, including spalling, exposed steel bars, and mesh cracks;

[0136] 2) Stiffness reduction damage, including structural cracks;

[0137] 3) Non-structural damage, including surface weathering.

[0138] In this embodiment, according to the location description of the disease, the structural analysis unit affected by the disease and its location in the structural analysis unit are calculated.

[0139] In this embodiment, the influence of various defects on the structural analysis unit is calculated, and the structural characteristics of the unit (such as section distance and stiffness) are corrected. Among them, for dimensional attenuation defects (such as spalling, exposed steel bars, and reticular cracks), the calculation formula for the reduced section distance of the relevant units affected by them is as follows:

[0140] I i，z,def =I i，Z -∑ M y m,def 2 ·A m，def

[0141] Among them, I i，z,def I is the section distance of the z-axis of the component of the structural analysis unit i after the influence of the size attenuation type disease is combined; i，z is the original z-axis section distance of the structural analysis unit i, M is the number of size attenuation type defects on the structural analysis unit i; y m，def A is the distance between the centroid of the disease m and the structural analysis unit i; m,def is the area of ​​disease m;

[0142] For stiffness reduction defects (such as structural cracks, steel bar corrosion, interlayer delamination, deflection, etc.), the calculation formula for the reduced component stiffness of the affected related units is as follows:

[0143]

[0144] Among them, k def k is the stiffness of the component after damage; origin is the original stiffness of the component; β n，j is the reduction coefficient of the structural stiffness of the jth technical condition of the nth stiffness reduction defect, j = 1~5; N is the number of all stiffness reduction defects on the component.

[0145] In this embodiment, there is a structural crack (tentatively n=1) with a technical condition level of 2 on the structural analysis unit (18). Accordingly,

[0146] k def (18) =k origin (18) ·(1-β 1，2 )

[0147] The unit stiffness matrix corrected by the effects of defects is summarized and the global stiffness matrix of the whole bridge is automatically calculated.

[0148] Step 4: Obtain the load condition P of the bridge, and use P=Sd to obtain the strain matrix d of the bridge. d is the calculation result of the structural response under the influence of the disease. Based on d, further obtain the relevant indicators of the bridge structure response under the disease condition.

[0149] In this embodiment, (1) a reinforcement learning environment is set up, i.e., all bridges in database A are analyzed for the optimization of the disease impact coefficient;

[0150] (2) Set the reinforcement learning action rules, i.e., β n，j The value selection rule of , where j represents the technical condition level of the disease, taking an integer between 1 and 5, and n takes an integer between 1 and N, where N is the number of all stiffness reduction diseases; β n，j The value range is 0.25~1, set the interval step Δ, the initial state β n，j The value is 0.25, and each action changes β n，j =β n，j +Δ;

[0151] (3) Calculate the initial state of the environment, that is, in the initial {β n，j}, calculate the initial structural response of the bridge. If there is load and load combination information in the bridge BIM model, use the Ifcopenshell tool to identify the IfcStructuralAction or IfcStructura1LoadGroup entity from the existing bridge BIM model, extract the defined load type and size (such as dead load, live load, wind load, etc.) and load combination rules; if the load-related information is not defined, manually input it through the interactive interface to finally form the load vector P.

[0152] Specifically, in this embodiment, the load types of the bridge are considered to be self-weight load and vehicle live load. Among them, the live load is set according to the Chinese Highway Bridge Design Code (JTG 3362-2018), simulating vehicle loads, including uniformly distributed loads of 10kN / m 2 Applied to the lane range, a concentrated load of 200kN acts on the maximum bending moment position of each span. The calculation method of the load condition is as follows:

[0153] Load condition = G + ψ·Q L

[0154] Where, is the deadweight constant load, ψ is the vehicle live load factor, Q L Vehicle live load.

[0155] After the load condition is determined, the following formula is used to perform the diseased structure analysis, and finally the strain matrix d of the bridge under the corresponding load condition is obtained.

[0156] P=S·d

[0157] Combined with the stiffness matrix k of each unit i And the strain matrix d can be further calculated to calculate the performance indicators such as the structural internal force at any position.

[0158] (4) Setting reinforcement learning rewards. In this embodiment, the reward is calculated based on the absolute value of the difference between the calculated result d of the structural response under the influence of the disease and the strain-related data d0 monitored by the SHM sensor, which includes two parts:

[0159] 1) Process reward: Under the current action, Than the previous action Small, reward +1;

[0160] 2) Instant reward: Under the current action, When the error is less than 1%, the reward is +10;

[0161] a t represents the action performed at time t, that is, at time t {β n，j}List value; is the absolute value of the difference corresponding to the action performed at time t;

[0162] (5) Training the reinforcement learning network. In this embodiment, a preset action is applied to the environment, and step (3) is repeated to calculate and update the environmental state, and rewards are issued based on the environmental state and the preset reward rules. After the training is completed, the optimal β combination is obtained and used for the quantitative analysis of the diseases of all similar bridges in the region. The output of the reinforcement learning process of this application is a set of {β n，j}List the stiffness reduction coefficients of all stiffness reduction defects on the bridge, and perform intensive training on the data of a bridge together. In this embodiment, β 1，2 The final optimized value is 0.6. In the actual case verification, the difference between the mid-span deflection measurement results of the bridge by the SHM sensor system and the damage analysis results is 0.6%.

[0163] In summary, the quantitative analysis method of the present invention can scientifically quantify the impact of defects on bridge structural performance, effectively solve the problem of disconnection between defect information and structural analysis, fill the gap between traditional defect qualitative assessment and SHM refined analysis, and provide an efficient and scalable solution for bridge maintenance management.

[0164] Any matters not described in the present invention are applicable to the prior art.

Claims

1. A quantitative analysis method for the impact of bridge defects on structural performance, characterized in that: The quantitative analysis method comprises the following steps: Step 1: Obtain bridge information and regular inspection reports of bridges of the same type in a certain area to form a disease impact coefficient optimization analysis database A; Step 2: Based on the bridge information, a bridge structure analysis model is constructed, the structural analysis unit is divided according to the preset division rules, the node matrix and the unit matrix are obtained, and the unit stiffness matrix of the structural analysis unit is calculated; Step 3: Extracting damage information from the regular inspection report of the bridge, and classifying the damage into three categories: size attenuation type damage, stiffness reduction type damage, and non-structural damage. The damage information includes the type, location, size, and technical status of the damage; According to the type of damage, identify how it affects the structural performance, i.e., dimensional degradation, stiffness degradation, or no structural impact; At the same time, the structural analysis units affected by the disease are determined according to the disease location and the node coordinates of the divided structural analysis units, and the effects of different diseases on the structural analysis units on their structural characteristics are calculated; Use the modified model according to different types of diseases to calculate the relevant parameters after the disease impact; The unit stiffness matrix is ​​modified by the relevant parameters after the damage, and the global stiffness matrix S is obtained by integrating the node matrix with the modified unit stiffness matrix. Step 4: Obtain the load condition P of the bridge, and use P = S·d to obtain the strain matrix d of the bridge. d is the calculation result of the structural response under the influence of the disease. Based on d, further obtain the relevant indicators of the bridge structure response under the disease condition.

2. The quantitative analysis method according to claim 1, characterized in that: For size attenuation type damage, the cross-sectional distance of the component after the reduction of the relevant structural analysis unit affected by the damage and the relevant parameters after the damage are calculated using the following formula: I i,z,def =I i,z -Σ M y m,def 2 ·A m,def Among them, I i,z,def I is the section distance of the z-axis of the component of the structural analysis unit i after the influence of the size attenuation type disease is combined; i,z is the original z-axis section distance of the structural analysis unit i, M is the number of size attenuation type defects on the structural analysis unit i; y m,def A is the distance between the centroid of the disease m and the structural analysis unit i; m,def is the area of ​​disease m; Non-structural defects have no significant impact on the overall performance of the structure, and no relevant parameter correction is performed.

3. The quantitative analysis method according to claim 1, characterized in that: For the stiffness reduction type of damage, the stiffness of the components after the reduction of the relevant structural analysis units affected by the damage and the calculation formula of the relevant parameters after the damage are as follows: Among them, k def k is the stiffness of the component after damage; origin is the original stiffness of the component; β n,j is the reduction coefficient of the structural stiffness of the jth technical condition of the nth stiffness reduction defect, j = 1~5; N is the number of all stiffness reduction defects on the component.

4. The quantitative analysis method according to claim 3, characterized in that: For stiffness reduction type diseases, the stiffness reduction factor β n,j The determination process is: Obtain the load conditions of a number of bridges of the same type in a certain area and the strain-related data monitored by SHM sensors. At the same time, the locations of all stiffness reduction defects on each bridge and the technical conditions of different defects are obtained, and then the distribution data of the stiffness reduction coefficient of the defect is obtained; The above data is used to form a reinforcement learning training database B; a reinforcement learning network is constructed; Set the reinforcement learning action rule, i.e., β n,j The value selection rule of , where j represents the technical condition level of the disease, taking an integer between 1 and 5, and n takes an integer between 1 and N, where N is the number of all stiffness reduction diseases; β n,j The value range is 0.25~1, set the interval step △, the initial state β n,j The value is 0.25, and each action changes β n,j =β n,j +△; Each action causes the global stiffness matrix S to change, and the strain matrix d of the bridge under the corresponding load condition is obtained according to P = S·d, which is the calculation result of the structural response under the influence of the disease; Set the reinforcement learning reward, and calculate the reward by the absolute value of the difference between the calculated result d of the structural response under the influence of the disease and the strain-related data d0 monitored by the SHM sensor, which includes two parts: 1) Process reward: Under the current action, Than the previous action Small, reward +1; 2) Instant reward: Under the current action, When the error is less than 1%, the reward is +10; a t represents the action performed at time t, that is, at time t {β n,j }List value; is the absolute value of the difference corresponding to the action performed at time t; Finally, traverse N,j to obtain the optimal {β n,j } combination, so that {β n,j } is combined as the stiffness reduction coefficient in the modified model of stiffness reduction type damage in step 3, and then the strain matrix d of the bridge is calculated.

5. The method according to claim 1, characterized in that The types of stiffness reduction defects include at least one of structural cracks, steel bar corrosion, interlayer delamination, and deflection.

6. The method according to claim 1, characterized in that The calculation formula of the global stiffness matrix is: Where S is the global stiffness matrix, T i is the transformation matrix from structural analysis unit to global coordinates, k i is the unit stiffness matrix of structural analysis unit i; T i T Indicates T i The transposed matrix of ; L is the number of structural analysis units.

7. The quantitative analysis method according to claim 1, characterized in that: The bridge information is presented in the form of a BIM model; the node matrix size is 1*NDOF, where NDOF is the degree of freedom of the entire bridge after dividing the structural analysis unit.

8. The quantitative analysis method according to claim 1, characterized in that: The bridge structure analysis model constructed based on Matrix-based methods includes the following steps: 1) Extract and analyze the global coordinates and relative positioning of bridge components in the bridge information, decompose the components according to the preset unit size, generate a number of structural analysis units, complete the coordinate conversion, divide adjacent structural analysis units by nodes, generate a node matrix with the coordinates of all nodes, and all structural analysis unit numbers and corresponding nodes constitute a unit matrix; 2) Extract component geometry information and obtain the key geometric characteristics of each structural analysis unit, including cross-sectional area, moment of inertia, section distance, length, and geometric center position. The component is composed of several structural analysis units, and the mapping between the key geometric characteristics of the structural analysis unit and the unit matrix is ​​established; 3) Extract the material information of the component and obtain the material elastic modulus E of each structural analysis unit eff Mapping relationship with the unit matrix; 4) Extract support information, determine the node position coordinates corresponding to the support and the constraint type of the support (one-way or two-way), and establish the mapping relationship between the support and the node matrix; 5) Combined with the mapping relationship between the support and the node matrix, the constraint type of the structural analysis unit is determined, and then the matrix-based methods are used to establish the unit stiffness matrix using the elastic modulus and geometric information of the material information. One structural analysis unit has one unit stiffness matrix.

9. A quantitative analysis system for the impact of bridge defects on structural performance, characterized in that: The system executes the steps of the analysis method according to any one of claims 1 to 8.

10. The analysis system according to claim 9, characterized in that The analysis system includes a disease influence coefficient optimization analysis database A, a reinforcement learning training database B, a bridge structure analysis model building block, a stiffness reduction coefficient optimization module, and a strain matrix calculation module. The disease impact coefficient optimization analysis database A is connected with the BIM model, the SHM sensor data module, and the scheduled inspection report data module; The bridge structure analysis model building block divides the structure analysis units based on the disease influence coefficient optimization analysis database A to establish the bridge structure analysis model; The reinforcement learning training database B is used to store the strain data in the SHM sensor data module, the load data in the regular inspection report data module, and the disease stiffness reduction coefficient distribution data obtained in the bridge structure analysis model building block; The stiffness reduction coefficient optimization module uses the reinforcement learning training database B to obtain the optimal stiffness reduction coefficient combination based on reinforcement learning supervised training; The strain matrix calculation module uses the optimal stiffness reduction coefficient combination output by the stiffness reduction coefficient optimization module to correct the unit stiffness matrix in the structural analysis unit of the bridge structure analysis model, and further calculates the strain matrix d of the bridge.

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