Building repair project whole cycle management system based on wood structure stability big data

Abstract

The present application relates to the technical field of ancient building repair and structure health monitoring, and particularly relates to a building repair project whole cycle management system based on wood structure stability big data; comprising: calling historical monitoring data and preparing a repair scheme as input; performing rigidity compatibility and stress migration risk analysis on the scheme to generate multi-factor evaluation indexes; calculating structure topology robustness score and comparing with threshold value to output routine repair or risk early warning signal; accordingly triggering scheme optimization or generating execution instruction and storing log. The present application solves the failure problem caused by traditional repair relying on subjective experience, and realizes whole cycle digital management and control based on rigidity and stress flow dynamic balance.

Description

Technical Field

[0001] This invention relates to the field of ancient building restoration and structural health monitoring technology, specifically to a full-cycle management system for building restoration projects based on big data on the stability of wooden structures. Background Technology

[0002] In the field of conservation and restoration of ancient wooden structures, wooden components often face the problem of mechanical performance degradation due to material rheology and environmental factors, requiring regular component replacement or reinforcement to maintain structural safety. However, existing restoration work generally focuses on appearance restoration or local repair of damaged parts, and restoration decisions mainly rely on qualitative judgments based on human experience. Due to the lack of quantitative assessment of the differences in mechanical properties between new and old materials, and the failure to fully consider the disturbance effect of local intervention on the overall structural stress system, the new replacement materials are often incompatible with the original structure in terms of stiffness, which can easily lead to stress concentration risks, causing overload of adjacent components or dislodging of joints, and then inducing secondary instability of the overall structure due to excessive local reinforcement, making it difficult to achieve true preventive protection.

[0003] Therefore, how to construct a full-cycle quantitative evaluation system that includes both material micro-compatibility and structural macro-stability to avoid the risk of repair damage has become an urgent technical problem to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a full-cycle management system for building repair projects based on big data on timber structure stability. Specifically, the technical solution of this invention includes:

[0005] Repair data management center, stiffness compatibility analysis unit, stress migration prediction unit, topology robustness assessment unit, and repair decision execution unit;

[0006] The repair data management center is used to retrieve historical monitoring data and proposed repair plans for wooden structures. The historical monitoring data and proposed repair plans are used as input data and sent to the stiffness compatibility analysis unit for compatibility risk assessment to obtain the stiffness compatibility index. At the same time, the proposed repair plan is sent to the stress migration prediction unit for stress migration risk analysis to obtain the stress migration coefficient.

[0007] The topology robustness assessment unit is used to conduct a multi-factor comprehensive risk assessment of the stiffness compatibility index and stress migration coefficient to obtain a structural topology robustness score. Based on a preset project risk safety threshold, the score is processed to generate a routine repair signal or risk warning signal for project management.

[0008] When a risk warning signal is generated, the repair decision execution unit is used to trigger a scheme optimization instruction and feed it back to the repair data management center to start the reconfiguration process of the repair scheme.

[0009] When a routine repair signal is generated, the repair decision execution unit generates a repair execution instruction and stores the instruction and related stiffness change data as a project management log.

[0010] Preferably, the compatibility risk assessment process is as follows:

[0011] Obtain new material physical property data of the components to be repaired in the proposed repair plan, as well as deformation data reflecting the current state of the components to be repaired from historical monitoring data;

[0012] Based on the deformation data, the rheological degradation parameter characterizing the degradation of material properties is calculated by data fitting and trend analysis methods.

[0013] Based on the rheological attenuation parameters, the preset initial design stiffness reference value is reduced to obtain the equivalent stiffness value of the old material.

[0014] The stiffness value of the new material in the physical property data of the new material is compared with the equivalent stiffness value of the old material. The absolute value of the deviation between the two is calculated. The value obtained after normalization and threshold truncation of the absolute value of the deviation is output as the stiffness compatibility index for risk assessment.

[0015] Preferably, the stress migration risk analysis process is as follows:

[0016] The repair locations in the proposed repair plan are identified as key risk points;

[0017] Based on the structural topology network data of wooden buildings, the primary associated nodes directly connected to the key risk nodes and the secondary associated nodes indirectly connected to them are identified.

[0018] When the stiffness properties of critical risk nodes undergo planned changes, the internal force redistribution values ​​on the first and second-level associated nodes are calculated based on the mechanical transmission relationship model.

[0019] The rate of change of the redistributed internal force values ​​relative to their original internal force values ​​is calculated separately. The average rate of change at the same level is then taken, and the results are normalized and weighted summed according to the associated levels. The final calculated value is output as the stress migration coefficient for assessing the range of risk propagation.

[0020] Preferably, the multi-factor comprehensive risk assessment process is as follows:

[0021] It has preset stiffness risk weighting factors and migration risk weighting factors for quantifying the importance of different risk factors;

[0022] Multiply the stiffness compatibility index by the stiffness risk weighting factor to obtain the stiffness risk component; multiply the stress migration coefficient by the migration risk weighting factor to obtain the migration risk component.

[0023] The arithmetic square root of the weighted sum of squares of the stiffness risk component and the migration risk component is calculated as the risk vector magnitude. The difference between the unit safety benchmark value and this magnitude is scaled and then used as the comprehensive risk score, i.e., the structural topological robustness score.

[0024] Preferably, the generation logic of the risk warning signal is as follows:

[0025] The calculated structural topology robustness score is compared with the preset project risk safety threshold;

[0026] If the structural topology robustness score is lower than the project risk safety threshold, the current repair plan is deemed to have a high risk, and a risk warning signal is generated.

[0027] If the structural topology robustness score is equal to or higher than the project risk safety threshold, the current repair plan is deemed to be risk-controllable, and a routine repair signal is generated.

[0028] Preferably, the data analysis process for the rheological attenuation parameters includes:

[0029] Extract the deformation increment sequence of the component to be repaired within a preset time period from historical monitoring data;

[0030] From the deformation increment sequence, the elastic deformation component caused by short-term fluctuations of external load is removed by data filtering technology to obtain the trend component reflecting long-term creep.

[0031] A functional relationship model between the creep trend component and time is established, the time power law feature of the model is extracted, and this feature is quantified into a rheological decay parameter.

[0032] Preferably, the construction and simulation process of the mechanical transmission relationship model includes:

[0033] Retrieve historical mechanical performance data of mortise and tenon joints related to key risk nodes stored in the database;

[0034] Based on the proposed repair plan, the key parameters in the historical mechanical performance data are updated to simulate the node state after repair.

[0035] The updated node performance data is substituted into the structural topology network for iterative equilibrium calculation until the forces on all nodes in the network reach a new equilibrium state, and the stress values ​​of each associated node in this state are recorded as internal force redistribution values.

[0036] Preferably, the execution and reconfiguration process of the scheme optimization instructions is as follows:

[0037] In response to risk warning signals, the dominant risk factors that lead to a decrease in the structural topological robustness score are analyzed. The dominant risk factors are stiffness compatibility index or stress migration coefficient.

[0038] If the dominant risk factor is the stiffness compatibility index, then resource allocation optimization data will be generated to adjust the selection or procurement recommendations for repair materials.

[0039] If the dominant risk factor is the stress migration coefficient, then construction management optimization suggestions data involving adjustments to construction procedures and temporary support schemes will be generated.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. This invention changes the traditional repair system's reliance on manual experience to qualitatively judge the appearance or damage status through the collaborative work of the repair data management center, topology robustness assessment unit, and repair decision execution unit. The system combines the objective facts of historical monitoring data with the theoretical parameters of the proposed scheme to establish a full-cycle digital control loop, effectively avoiding repair failures caused by subjective experience oversights, and elevating repair management from simple physical repair to a dynamic balance management level of stiffness and stress flow.

[0042] 2. This invention addresses the performance degradation problem caused by material rheology mentioned in the background art. It utilizes deformation data to invert rheological degradation parameters and calculates the reduction of the initial design stiffness of the old material, thereby obtaining an equivalent stiffness value that reflects the true residual performance of the component. By comparing this equivalent value with the stiffness of the new material, the physical performance gap between the old and new materials can be accurately quantified, ensuring a high degree of mechanical compatibility between the replacement material and the original structure, and fundamentally avoiding the risk of stress concentration caused by excessive stiffness of the new material.

[0043] 3. This invention addresses the problem of existing technologies neglecting the impact of local repairs on the overall stress system. By identifying primary and secondary related nodes, it simulates the redistribution of internal forces after changes in the stiffness of key risk nodes. This mechanism can quantify the butterfly effect caused by local repairs, detect and warn of potential overloads in adjacent components or dislodging of nodes due to sudden increases in local stiffness, and prevent secondary instability of the overall structure caused by excessive local reinforcement.

[0044] 4. After generating a risk warning, this invention can further identify whether the dominant factor causing the score reduction is stiffness incompatibility in the material dimension or excessive stress migration in the structural dimension. Based on this, the system generates differentiated optimization strategies: if it is a stiffness problem, adjust the material selection; if it is a migration problem, optimize the construction process or support scheme. This intelligent attribution and classification mechanism realizes the leap from simply discovering problems to automatically generating solutions, which greatly improves the decision-making efficiency and construction safety of repair projects. Attached Figure Description

[0045] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0046] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0048] Example 1:

[0049] Please see Figure 1 The full-cycle management system for building renovation projects based on big data on timber structure stability includes a renovation data management center, a stiffness compatibility analysis unit, a stress migration prediction unit, a topology robustness assessment unit, and a renovation decision execution unit.

[0050] The renovation data management center is used to retrieve historical monitoring data and proposed renovation plans for wooden buildings. The historical monitoring data and proposed renovation plans are used as input data and sent to the stiffness compatibility analysis unit for compatibility risk assessment to obtain the stiffness compatibility index. At the same time, the proposed renovation plan is sent to the stress migration prediction unit for stress migration risk analysis to obtain the stress migration coefficient.

[0051] The topology robustness assessment unit is used to conduct a multi-factor comprehensive risk assessment of the stiffness compatibility index and stress migration coefficient to obtain the structural topology robustness score. Based on the preset project risk safety threshold, the score is processed to generate routine repair signals or risk warning signals for project management.

[0052] When a risk warning signal is generated, the repair decision execution unit is used to trigger the scheme optimization instruction and feed it back to the repair data management center to start the reconfiguration process of the repair scheme.

[0053] When a routine repair signal is generated, the repair decision execution unit generates a repair execution instruction and stores the instruction and related stiffness change data as a project management log.

[0054] This embodiment provides a full-cycle management system for building repair projects based on big data on timber structure stability. The system constructs a closed-loop control system from data acquisition to decision execution; the system mainly consists of five core functional modules:

[0055] Repair Data Management Center: As the central database and scheduling interface of the system, its main function is to achieve the fusion of multi-source heterogeneous data. In this embodiment, the center is equipped with a data interface for real-time retrieval of historical monitoring data of wooden buildings stored in the cloud and proposed repair plans submitted by engineers, including the parameters of the components to be replaced in the BIM model. The center integrates the above two into a standardized input vector and distributes them to the downstream analysis units. If historical monitoring data of a specific building is missing, the repair data management center is equipped with a backup database interface, which allows retrieval and estimation based on the general creep model parameters of typical wooden buildings of the same region, era, and tree species as estimated inputs to ensure the initial operation of the system in the absence of measured historical data.

[0056] Stiffness compatibility analysis unit: This unit focuses on risk calculation at the local material dimension; after receiving input data, it performs compatibility risk assessment based on the rheological model and outputs a quantitative index—stiffness compatibility index, which is used to characterize the degree of matching between new and old materials in terms of mechanical properties.

[0057] Stress migration prediction unit: This unit focuses on risk calculation in the global topology dimension; based on the proposed repair plan, it simulates the redistribution of internal forces after stiffness changes, performs stress migration risk analysis, and outputs stress migration coefficients to characterize the degree of disturbance of the overall structure by local intervention.

[0058] Topological robustness assessment unit: As the core of the decision-making process, this unit performs a multi-factor comprehensive risk assessment; it receives the stiffness compatibility index. and stress migration coefficient The structural topological robustness score is calculated by combining preset weighting factors. This unit has a built-in comparison logic circuit, based on a preset project risk safety threshold. The scores are binarized and analyzed to generate routine repair signals or risk warning signals.

[0059] Repair Decision Execution Unit: As the system's output actuator, when it receives a risk warning signal, it triggers a scheme optimization instruction, forcing the system to roll back to the management center to start the reconfiguration process; when it receives a regular repair signal, it locks the current scheme and generates a repair execution instruction, while simultaneously writing stiffness change data into the tamper-proof project management log.

[0060] This embodiment elevates the restoration management from simple appearance / damage repair to the level of stiffness and stress flow management, effectively solving the problem of secondary instability of the overall structure caused by the mismatch of stiffness between new and old materials or local over-strengthening, and realizing preventive protection of the restoration of cultural relics buildings.

[0061] Example 2:

[0062] The compatibility risk assessment process is as follows:

[0063] Obtain new material physical property data of the components to be repaired in the proposed repair plan, as well as deformation data reflecting the current state of the components to be repaired from historical monitoring data;

[0064] Based on deformation data, the rheological degradation parameters characterizing the degradation of material properties are calculated through data fitting and trend analysis methods.

[0065] Based on the rheological attenuation parameters, the preset initial design stiffness reference value is reduced to obtain the equivalent stiffness value of the old material.

[0066] The stiffness values ​​of the new material in the physical property data are compared with the equivalent stiffness values ​​of the old material. The absolute value of the deviation between the two is calculated. The value obtained after normalization and threshold truncation of the absolute value of the deviation is output as the stiffness compatibility index for risk assessment.

[0067] This embodiment details the specific calculation logic for compatibility risk assessment, aiming to quantify the physical performance gap between new and old materials; the assessment process includes the following steps:

[0068] Data acquisition steps: The system extracts the physical property data of new materials from the proposed repair plan, denoted as... Specifically, this includes the initial value of the elastic modulus of new wood. Simultaneously, deformation data of the current state of the components to be repaired is extracted from historical monitoring data and recorded as follows: This data contains components from the past. Deflection and axial compression within each cycle;

[0069] Steps for calculating rheological parameters; using The system uses the least squares method for nonlinear regression to calculate the rheological attenuation parameters. This parameter is defined as the material properties over time. The rate constant, which decays exponentially, characterizes the creep properties of ancient building timber under long-term loads.

[0070] Equivalent stiffness reduction steps; the system retrieves the initial design stiffness reference value of the component to be repaired during its initial construction. By combining the rheological attenuation parameters for reduction calculation, the equivalent stiffness value of the old material is obtained. The reduction model used in this embodiment is: ,in The building's age;

[0071] Index generation steps; the system is based on the elastic modulus of new wood. Calculate the stiffness value of the new material Assuming the geometric dimensions of the components remain unchanged before and after replacement, according to Hooke's Law, stiffness is proportional to the modulus of elasticity, and the calculation formula is as follows:

[0072]

[0073] Among them, in order to ensure The numerical values ​​have clear physical evaluation significance, and the normalization function specifically adopts the threshold ratio method; the system has a preset maximum allowable stiffness deviation threshold. For example, the value is the initial stiffness. of Based on the allowable error settings in the "Technical Specification for Maintenance and Reinforcement of Ancient Wooden Structures"; The calculation formula is expanded as follows:

[0074]

[0075] This formula ensures that when the stiffness deviation between the new and old materials exceeds the engineering-allowed limit threshold... At that time, the risk index Saturation , indicating complete incompatibility; when the deviation is within the allowable range, the exponent is at Linear distribution within the interval;

[0076] This embodiment introduces a time-based rheological attenuation parameter, which makes the stiffness comparison between new and old materials no longer based on theoretical design values, but on the actual residual mechanical properties of the components, thereby accurately identifying the stress concentration risk caused by the material gap.

[0077] Example 3:

[0078] The stress migration risk analysis process is as follows:

[0079] The repair locations in the proposed repair plan are identified as key risk points;

[0080] Based on the structural topology network data of wooden buildings, primary associated nodes directly connected to key risk nodes and secondary associated nodes indirectly connected to them were identified.

[0081] When the stiffness properties of critical risk nodes undergo planned changes, the internal force redistribution values ​​on the first and second-level associated nodes are calculated based on the mechanical transmission relationship model.

[0082] The rate of change of the redistributed internal force values ​​relative to their original internal force values ​​is calculated separately. The average rate of change at the same level is then taken, and the results are normalized and weighted summed according to the associated levels. The final calculated value is output as the stress migration coefficient for assessing the range of risk propagation.

[0083] This embodiment describes in detail the process of stress migration risk analysis, aiming to predict the butterfly effect caused by local repairs;

[0084] The analysis process includes the following steps:

[0085] The first step is node identification; the system parses the BIM model and marks the locations of stiffness changes involved in the proposed repair plan, such as major dismantling repairs and pier joint repairs, as key risk nodes. ;

[0086] The second step is topological association identification; based on the structural topological network data of the wooden building, it is represented as an adjacency matrix in graph theory. The system traverses the matrix to identify associated nodes:

[0087] First-level related nodes :and There are component joints with direct mortise and tenon connections, such as column heads and directly connected lintels;

[0088] Secondary related nodes :and Connected but not directly connected Extension component nodes;

[0089] The third step is the simulation of internal force redistribution; the system is based on a mechanical transmission relationship model, specifically a finite element model including semi-rigid nodal features, to simulate when... The stiffness is determined by Mutation At that time, the force flow changes within the network; the system calculates the redistribution values ​​of internal forces on the first- and second-level associated nodes at this time. ,in The system represents the node index; the fourth step is coefficient calculation; the system calculates the rate of change of internal forces at each node. To avoid calculation failure due to the original internal force being zero, a modified relative change rate formula is used:

[0090]

[0091] in, For example, a preset non-zero small extreme value. To prevent the denominator from being zero (abnormal); Original internal forces; stress migration coefficient The influence of differences in the number of nodes is eliminated by calculating a normalized weighted average. The specific calculation formula is as follows:

[0092]

[0093] In order to balance the impact of the difference in the number of nodes at different levels on the overall risk assessment, the above formula adopts the hierarchical average weighting method.

[0094] in, and These represent the total number of first-level and second-level associated nodes, respectively. and These are the distance decay weights for first-level and second-level related nodes, respectively. The specific weight values ​​are based on topological distance. The inverse square law is determined; the topological distance of first-order associated nodes is defined. Topological distance of second-level related nodes The formula for calculating the weight is:

[0095]

[0096] in, These are normalization coefficients used to ensure... ;In the formula, This represents the average rate of change of internal forces at the first-level associated nodes; and To determine the weight of the influence of different levels, take... and This is to quantify the physical fact that the effects of stress migration decay rapidly with increasing distance;

[0097] Using the inverse square law The physical basis is that the mortise and tenon joints of wooden buildings are semi-rigid connections, and there is a significant dissipation effect when energy is transferred through the joints, as well as friction and compression deformation. Experiments show that the attenuation rate of stress waves in discrete mortise and tenon topology networks is faster than the linear attenuation in continuous media. Therefore, using the inverse square relationship can more accurately simulate the spatial convergence characteristics of stress concentration risk at the topological level and avoid overestimating the response of far-end nodes.

[0098] This embodiment quantifies the range and intensity of the disturbance to the surrounding structure caused by the repair behavior through hierarchical weighted calculation, and effectively warns of the risk of overload of adjacent components or dislodging of joints caused by a sudden increase in local stiffness.

[0099] Example 4:

[0100] The multi-factor comprehensive risk assessment process is as follows:

[0101] It has preset stiffness risk weighting factors and migration risk weighting factors for quantifying the importance of different risk factors;

[0102] Multiply the stiffness compatibility index by the stiffness risk weighting factor to obtain the stiffness risk component; multiply the stress migration coefficient by the migration risk weighting factor to obtain the migration risk component.

[0103] The arithmetic square root of the weighted sum of squares of the stiffness risk component and the migration risk component is calculated as the risk vector magnitude. The difference between the unit safety benchmark value and this magnitude is scaled and then used as the comprehensive risk score, i.e., the structural topological robustness score.

[0104] To eliminate dimensional differences and establish a unified risk calculation space, the system first calculates the stiffness compatibility index obtained in the aforementioned steps. and stress migration coefficient Mapped to dimensionless normalized risk components; define stiffness risk components. Define migration risk components:

[0105]

[0106] in, For example, the preset stress migration tolerance upper limit Structural topological robustness score The calculation formula is revised as follows:

[0107]

[0108] in, It represents the ratio of the current state vector magnitude to the maximum risk magnitude in the multidimensional risk space. and These are normalized weight coefficients, and satisfy... Under this formula, This represents the system's safety margin. The lower the score, the higher the rigidity risk or migration risk, thus avoiding logical vulnerabilities where high risks cancel each other out.

[0109] This embodiment provides a multi-dimensional quantitative indicator that mathematically integrates micro-risks at the material level and macro-risks at the structural level, providing objective data support for repair decisions.

[0110] Example 5:

[0111] The logic for generating risk warning signals is as follows:

[0112] The calculated structural topology robustness score is compared with the preset project risk safety threshold;

[0113] If the structural topology robustness score is lower than the project risk safety threshold, the current repair plan is deemed to have a high risk, and a risk warning signal is generated.

[0114] If the structural topology robustness score is equal to or higher than the project risk safety threshold, the current repair plan is deemed to be risk-controllable, and a routine repair signal is generated.

[0115] This embodiment describes in detail the triggering logic of the risk warning signal; the system has a configurable project risk safety threshold. Project risk safety threshold It is not arbitrarily set, but rather based on big data statistical calibration of a historical restoration case database; the system selects past... For example, selecting over 1000 successful cases where secondary instability did not occur after repair, and calculating their... Score distribution, and Set it to the 15th percentile value of this distribution, for example, 85 points; if sufficient historical case data is lacking, the system will default to... The initial value is set to a preset empirical value, such as 85 points. This means that only when the robustness score of the current solution is within the normal distribution range of historical successful cases, i.e., higher than the bottom 15% of samples, will it be judged as a routine repair. The comparator executes the following logic:

[0116] Comparison logic: Calculate the... and Real-time comparison; early warning generation: if The system determines that the current repair plan will lead to a decrease in structural stability. Based on the difference logic, it may mean that a certain risk equilibrium has been broken or that a specific risk is too high. The system immediately generates a risk warning signal Warning_Flag=1.

[0117] like If the plan is deemed to be within a controllable range, a regular repair signal Warning_Flag=0 is generated.

[0118] This embodiment automates and standardizes the review of repair plans, avoiding the subjectivity and oversight of human experience-based judgment.

[0119] Example 6:

[0120] The data analysis process for rheological attenuation parameters includes:

[0121] Extract the deformation increment sequence of the component to be repaired within a preset time period from historical monitoring data;

[0122] From the deformation increment sequence, the elastic deformation component caused by short-term fluctuations of external load is removed by data filtering technology to obtain the trend component reflecting long-term creep.

[0123] A functional relationship model between the creep trend component and time is established, the time power law feature of the model is extracted, and this feature is quantified into a rheological decay parameter.

[0124] This embodiment is a further refinement of the process for obtaining rheological attenuation parameters in Embodiment 2;

[0125] The specific quantization conversion process is as follows: The system first bases the conversion on the creep trend component. The power function model was obtained by fitting: ,in, The creep amplitude coefficient, This is the creep time exponent, where time is... The unit needs to cooperate with The time dimension remains consistent, typically measured in years; both are fitting constants obtained by the system through nonlinear regression calculation of historical monitoring data using the least squares method; in order to map this power-law characteristic to the exponential decay model of Example 2. In this process, the system needs to perform equivalent parameter conversion;

[0126] The system calculates the current building age. The equivalent instantaneous creep rate at the location; considering the complexity of the creep mechanism of ancient building timber, this embodiment adopts the instantaneous equivalent method for engineering approximation: that is, assuming that at the current building age... At any given moment, the instantaneous relative rate of change of the power-law model is equal to that of the exponential model, thus mapping the measured nonlinear creep characteristics into a single attenuation coefficient that facilitates stiffness reduction calculations; based on the current building age... Based on the assumption that the relative rate of change in the power-law creep model is equal to that in the exponential decay model, the formula for calculating the rheological decay parameter is derived as follows:

[0127]

[0128] The system will calculate Substituting this into the stiffness reduction formula of Example 2, the physical mapping from deformation monitoring data to stiffness parameters is completed;

[0129] The data analysis process includes:

[0130] Incremental Extraction: The system extracts the deformation increment sequence of the components to be repaired from the historical monitoring database on a weekly basis. ;

[0131] Filtering and Separation: Applying a low-pass filter to process sequences The system identifies and removes high-frequency fluctuating elastic deformation components caused by short-term factors such as wind load and crowd load, while retaining low-frequency trend components. This component reflects the long-term creep of wood;

[0132] Model quantization: Establishing functional relationship models The system obtains the slope by differentiating the model. The characteristics of this slope change are quantified into material rheological attenuation parameters. ;

[0133] This embodiment effectively eliminates environmental noise interference through signal processing technology, accurately extracts rheological characteristics that reflect the degradation of the material's essential properties, and improves the accuracy of stiffness assessment.

[0134] Example 7:

[0135] The process of constructing and simulating the mechanical transmission relationship model includes:

[0136] Retrieve historical mechanical performance data of mortise and tenon joints related to key risk nodes stored in the database;

[0137] Based on the proposed repair plan, key parameters in the historical mechanical performance data will be updated to simulate the state of the nodes after repair.

[0138] The updated node performance data is substituted into the structural topology network for iterative equilibrium calculation until the forces on all nodes in the network reach a new equilibrium state, and the stress values ​​of each associated node in this state are recorded as internal force redistribution values.

[0139] This embodiment is a further refinement of the mechanical transmission relationship model constructed in Embodiment 3;

[0140] The simulation process includes:

[0141] Data retrieval: The system accesses a dedicated database to retrieve historical mechanical performance data of mortise and tenon joints that match the type of key risk nodes, including M-θ hysteresis curve data;

[0142] Parameter update: Based on the repair plan, the rotten tenons were replaced with new wood, and the initial stiffness in the M-θ curve was corrected. and yield moment Based on key parameters, construct the repaired node unit model;

[0143] Iterative balancing: embedding the updated nodal elements into the overall structural stiffness matrix. The system applies a standard load vector. Solve the equation Due to the nonlinearity of the timber joints, the system uses the Newton-Raphson method for iterative equilibrium calculations until the residual force vector norm is less than the convergence tolerance; finally, the stress values ​​of each associated element in the convergence state are recorded as the internal force redistribution values.

[0144] The stress mechanism of semi-rigid mortise and tenon joints in ancient buildings was realistically reproduced using a nonlinear iterative algorithm, so that the predicted stress migration results are consistent with the actual physical characteristics of the wooden structure.

[0145] Example 8:

[0146] The execution and reconfiguration process for the solution optimization command is as follows:

[0147] In response to risk warning signals, the dominant risk factors that lead to a decrease in the structural topological robustness score are analyzed. The dominant risk factors are the stiffness compatibility index or the stress migration coefficient.

[0148] If the dominant risk factor is the stiffness compatibility index, then resource allocation optimization data will be generated to adjust the selection or procurement recommendations for repair materials.

[0149] If the dominant risk factor is the stress migration coefficient, then construction management optimization suggestions data involving adjustments to construction procedures and temporary support schemes will be generated.

[0150] This embodiment describes in detail the system's execution logic for optimizing the solution after generating a risk warning;

[0151] When a risk warning signal is received, the system initiates attribution analysis:

[0152] Factor analysis: Comparing stiffness compatibility indices and stress migration coefficient The normalized values ​​were used to identify the dominant risk factors that led to the abnormal scores.

[0153] Stiffness-driven optimization: If The dominant factor indicates that the new material is too hard or too soft, and the system generates resource allocation optimization data; specifically, it recommends replacing the material with one whose elastic modulus is closer to the desired value. For certain tree species, it is recommended to use pretreatment processes such as artificial aging or physical softening;

[0154] Migration-driven optimization: If As the dominant factor, it means that local changes cause large-scale stress changes, and the system generates construction management optimization suggestions data; specifically, it includes: adjusting the construction sequence, such as staged unloading, or adding temporary support schemes, such as adding jack supports at secondary related nodes to share the migration stress.

[0155] It can not only identify problems, but also automatically generate targeted solutions based on the causes of the problems, achieving a leap from passive monitoring to proactive closed-loop management.

[0156] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A full-cycle management system for building repair projects based on big data on timber structure stability, characterized in that: It includes a data management center for repair, a stiffness compatibility analysis unit, a stress migration prediction unit, a topology robustness assessment unit, and a repair decision execution unit. The repair data management center is used to retrieve historical monitoring data and proposed repair plans for wooden structures. The historical monitoring data and proposed repair plans are used as input data and sent to the stiffness compatibility analysis unit for compatibility risk assessment to obtain the stiffness compatibility index. At the same time, the proposed repair plan is sent to the stress migration prediction unit for stress migration risk analysis to obtain the stress migration coefficient. The topology robustness assessment unit is used to conduct a multi-factor comprehensive risk assessment of the stiffness compatibility index and stress migration coefficient to obtain a structural topology robustness score. Based on a preset project risk safety threshold, the score is processed to generate a routine repair signal or risk warning signal for project management. When a risk warning signal is generated, the repair decision execution unit is used to trigger a scheme optimization instruction and feed it back to the repair data management center to start the reconfiguration process of the repair scheme. When a routine repair signal is generated, the repair decision execution unit generates a repair execution instruction and stores the instruction and related stiffness change data as a project management log. The compatibility risk assessment process is as follows: Obtain new material physical property data of the components to be repaired in the proposed repair plan, as well as deformation data reflecting the current state of the components to be repaired from historical monitoring data; Based on the deformation data, the rheological degradation parameter characterizing the degradation of material properties is calculated by data fitting and trend analysis methods. Based on the rheological attenuation parameters, the preset initial design stiffness reference value is reduced to obtain the equivalent stiffness value of the old material. The stiffness value of the new material in the physical property data of the new material is compared with the equivalent stiffness value of the old material. The absolute value of the deviation between the two is calculated. The value obtained after normalization and threshold truncation of the absolute value of the deviation is output as the stiffness compatibility index for risk assessment. The stress migration risk analysis process is as follows: The repair locations in the proposed repair plan are identified as key risk points; Based on the structural topology network data of wooden buildings, the primary associated nodes directly connected to the key risk nodes and the secondary associated nodes indirectly connected to them are identified. When the stiffness properties of critical risk nodes undergo planned changes, the internal force redistribution values ​​on the first and second-level associated nodes are calculated based on the mechanical transmission relationship model. The rate of change of the redistributed internal force values ​​relative to their original internal force values ​​is calculated separately. The average rate of change at the same level is then taken, and the results are normalized and weighted summed according to the associated levels. The final calculated value is output as the stress migration coefficient for assessing the range of risk propagation.

2. The full-cycle management system for building repair projects based on big data on timber structure stability as described in claim 1, characterized in that, The multi-factor comprehensive risk assessment process is as follows: It has preset stiffness risk weighting factors and migration risk weighting factors for quantifying the importance of different risk factors; Multiply the stiffness compatibility index by the stiffness risk weighting factor to obtain the stiffness risk component; Multiplying the stress migration coefficient by the migration risk weighting factor yields the migration risk component; The arithmetic square root of the weighted sum of squares of the stiffness risk component and the migration risk component is calculated as the risk vector magnitude. The difference between the unit safety benchmark value and this magnitude is scaled and then used as the comprehensive risk score, i.e., the structural topological robustness score.

3. The full-cycle management system for building repair projects based on big data on timber structure stability as described in claim 1, characterized in that, The logic for generating the risk warning signal is as follows: The calculated structural topology robustness score is compared with the preset project risk safety threshold; If the structural topology robustness score is lower than the project risk safety threshold, the current repair plan is deemed to have a high risk, and a risk warning signal is generated. If the structural topology robustness score is equal to or higher than the project risk safety threshold, the current repair plan is deemed to be risk-controllable, and a routine repair signal is generated.

4. The full-cycle management system for building repair projects based on big data on timber structure stability as described in claim 1, characterized in that, The data analysis process for the rheological attenuation parameters includes: Extract the deformation increment sequence of the component to be repaired within a preset time period from historical monitoring data; From the deformation increment sequence, the elastic deformation component caused by short-term fluctuations of external load is removed by data filtering technology to obtain the trend component reflecting long-term creep. A functional relationship model between the creep trend component and time is established, the time power law feature of the model is extracted, and this feature is quantified into a rheological decay parameter.

5. The full-cycle management system for building repair projects based on big data on timber structure stability as described in claim 1, characterized in that, The construction and simulation process of the mechanical transmission relationship model includes: Retrieve historical mechanical performance data of mortise and tenon joints related to key risk nodes stored in the database; Based on the proposed repair plan, the key parameters in the historical mechanical performance data are updated to simulate the node state after repair. The updated node performance data is substituted into the structural topology network for iterative equilibrium calculation until the forces on all nodes in the network reach a new equilibrium state, and the stress values ​​of each associated node in this state are recorded as internal force redistribution values.

6. The full-cycle management system for building repair projects based on big data on timber structure stability as described in claim 3, is characterized in that, The execution and reconfiguration process of the scheme optimization instructions is as follows: In response to risk warning signals, the dominant risk factors that lead to a decrease in the structural topological robustness score are analyzed. The dominant risk factors are the stiffness compatibility index or the stress migration coefficient. If the dominant risk factor is the stiffness compatibility index, then resource allocation optimization data will be generated to adjust the selection or procurement recommendations for repair materials. If the dominant risk factor is the stress migration coefficient, then construction management optimization suggestions data involving adjustments to construction procedures and temporary support schemes will be generated.

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