An earthquake-resistant reinforcing method and reinforcing system suitable for historical buildings

Abstract

The application discloses a kind of seismic reinforcement method and reinforcing system suitable for historical building, it is related to existing building reinforcing technical field.A kind of seismic reinforcement system suitable for historical building, including have: data modeling module, risk assessment module, disturbance analysis module, buffer decision module, reinforcement early warning module, component reinforcement decision module, collaborative reinforcement decision module and scheduling optimization module.The application establishes the structure coupling model of adjacent building and strength correlation analysis method, identifies linkage stress relationship and collaborative response characteristics, provides theoretical basis and determination standard for collaborative reinforcement across building unit;By generating collaborative reinforcement strategy according to linkage demand, clear collaborative mode and deployment position, the overall coordination of colony level reinforcement scheme and the linkage flexibility of structure system are enhanced.

Description

Technical Field

[0001] This invention relates to the field of existing building reinforcement technology, and in particular to a seismic reinforcement method and system applicable to historical buildings. Background Technology

[0002] With the advancement of urban renewal and cultural preservation, a large number of historical buildings face frequent environmental disturbances and construction interventions, making their seismic resistance and structural safety increasingly important. Traditional seismic reinforcement schemes mostly rely on on-site experience, single-point monitoring, and static reinforcement decisions, which are difficult to adapt to the complex factors such as multi-source disturbances, structural coupling, and resource allocation in complex building complexes. Especially in scenarios where the dual factors of earthquakes and construction disturbances overlap, problems such as assessment lag, response imbalance, and resource waste often occur.

[0003] In recent years, with the development of technologies such as digital twins, structural health monitoring, and multi-objective optimization, the protection and reinforcement of historical buildings has gradually evolved towards data-driven and intelligent decision-making. Some technologies have attempted to introduce 3D modeling, sensor monitoring, or structural analysis software for local risk analysis, but there are still three significant shortcomings: First, they fail to achieve fusion modeling of multi-source data (such as earthquake prediction, construction disturbance, and crack evolution) in the temporal and spatial dimensions; second, they lack the ability to dynamically perceive risks at the component and cluster levels; and third, reinforcement schemes are mostly statically set, unable to achieve adaptive scheduling based on disturbance levels, historical defects, and building value, making it difficult to form a global optimization strategy across components and buildings.

[0004] Therefore, the key technical problem that urgently needs to be solved at this stage is how to construct a dynamic seismic strengthening mechanism oriented towards component-level response and strategy coordination in building clusters with multi-source disturbances and significant structural differences, so as to achieve an integrated closed loop of perception, judgment, intervention and scheduling. Summary of the Invention

[0005] This invention proposes a seismic reinforcement method and system suitable for historical buildings. It integrates digital twin modeling, risk thermal analysis, spectrum response determination, strategy cascade reasoning and multi-objective scheduling optimization. Under complex disturbance scenarios, it outputs an intelligent reinforcement scheme with implementation logic and control sequence, thereby improving the overall seismic toughness and protection efficiency of historical building clusters.

[0006] A seismic reinforcement method applicable to historical buildings, comprising:

[0007] Real-time collection of predicted earthquake data, structural morphology information of target historical buildings and adjacent buildings, environmental disturbance information, component crack information and historical damage information of target areas, and synchronous matching to generate corresponding digital twins, constructing a three-dimensional structural model and reinforcement dataset covering the building complex;

[0008] Based on the reinforcement dataset and historical damage information, the seismic hazard of each digital twin is comprehensively analyzed, the corresponding risk level is output, and the risk heat map of the building cluster is updated.

[0009] The environmental disturbance information is spectrally decomposed to calculate the dynamic response index of each component area in each frequency band, and the results are overlaid and analyzed. Based on the results, the response level of the component area and the overall environmental disturbance level are divided, the response level of each component area is marked, and the results are integrated into the risk heat map. The structural load is calculated based on the environmental disturbance level, and the buffering strategy is obtained.

[0010] The predicted earthquake data is used as environmental disturbance information to predict the response level of the component area. Based on the stepped warning threshold, a reinforcement warning is generated. Based on the component crack information, the crack direction and propagation rate are predicted. The component reinforcement strategy is obtained based on the prediction results.

[0011] Strength correlation analysis is performed on the structural coupling relationship between the target historical building and its adjacent buildings, and a collaborative reinforcement strategy is obtained based on the results.

[0012] Based on the reinforcement dataset and risk heat map, a multi-objective optimization algorithm is used to comprehensively calculate the seismic performance index, reinforcement demand index and building importance index. The weights of the three types of indexes are dynamically adjusted according to the level of environmental disturbance. The buffer strategy, component reinforcement strategy and collaborative reinforcement strategy are comprehensively judged to determine whether the three types of strategies should be implemented, the implementation method and the implementation order, and to generate reinforcement scheduling schemes for each target historical building in the building cluster.

[0013] As a preferred technical solution of the present invention, the risk classification corresponding to the output includes: extracting risk characteristic parameters related to seismic response based on the reinforcement dataset and historical damage information, including crack distribution, deformation capacity, stress path integrity and external disturbance influence of the component area, calculating the seismic hazard assessment value characterizing the seismic resistance of the target historical building, comparing the seismic hazard assessment value with the risk level threshold, determining the risk level of the digital twin, and mapping the risk level to the risk heat map of the building cluster and completing the update.

[0014] As a preferred embodiment of the present invention, the division of the response level of the component area and the overall environmental disturbance level includes: performing spectral decomposition on the environmental disturbance information, extracting components of different frequency bands in the disturbance signal, calculating the dynamic response index of the component area under each frequency band, including displacement response, stress amplitude and acceleration change; weighting and superimposing the dynamic response index under different frequency bands according to the component area to obtain the comprehensive response of each component area, and dividing the corresponding response level based on the comprehensive response; and simultaneously performing aggregate analysis on the response levels of each component area according to spatial distribution to determine the environmental disturbance level reflecting the overall disturbance intensity of the building.

[0015] As a preferred technical solution of the present invention, the acquisition buffer strategy includes: applying an equivalent transformation of disturbance to the building components of the target historical building based on the environmental disturbance level, calculating the structural load of each component area under different disturbance levels, and performing a matching analysis between the structural load and the disturbance level of the corresponding component area; when the structural load exceeds the preset bearing range, acquiring a buffer strategy, including alternative buffering methods and corresponding deployment locations.

[0016] As a preferred technical solution of the present invention, the generation of reinforcement early warning includes: comparing the response level of the component area with the step early warning threshold, obtaining the early warning status according to its step, and marking the corresponding component area in the thermal risk map.

[0017] As a preferred technical solution of the present invention, the acquisition of component reinforcement strategy includes: extracting the spatial distribution characteristics of cracks based on component crack information, including crack length, width, direction and location, and predicting crack direction and propagation rate by combining historical construction evolution data, and identifying potential crack penetration paths or structural failure trends; and acquiring component reinforcement strategy based on the prediction results, including alternative reinforcement methods and corresponding reinforcement locations.

[0018] As a preferred technical solution of the present invention, the strength correlation analysis includes: extracting the structural morphological features, foundation forms and relative spatial arrangement relationships of the target historical building and its adjacent buildings in a three-dimensional structural model, and constructing a structural coupling model between buildings; evaluating the load transfer path, common load-bearing components and boundary conditions coupling degree between adjacent buildings based on the structural coupling model, and calculating their relative deformation coordination and linkage response intensity under seismic disturbance.

[0019] As a preferred technical solution of the present invention, the acquisition of the collaborative reinforcement strategy includes: determining whether there is a need for joint reinforcement between the target historical building and adjacent buildings based on the results of strength correlation analysis; when the need for joint reinforcement reaches a set condition, acquiring the collaborative reinforcement strategy, including alternative collaborative reinforcement methods and corresponding reinforcement locations, wherein the alternative collaborative reinforcement methods include one or a combination of connection reinforcement, shared damping device, boundary reinforcement or synchronous unloading structure.

[0020] As a preferred technical solution of the present invention, the acquisition of the reinforcement scheduling scheme includes: calculating the seismic performance index, reinforcement demand index, and building importance index of each target historical building, wherein the seismic performance index includes the remaining bearing capacity of the component, the rate of change of frequency, and the dynamic amplification factor; the reinforcement demand index includes the crack propagation rate in the component area, the degree of structural stiffness degradation, and the response level; and the building importance index is determined based on the historical protection level, the population density, and the regional disaster impact coefficient. The weights of the above three types of indicators are dynamically adjusted according to the environmental disturbance level to construct a multi-objective optimization model for reinforcement scheduling. Priority objective functions are established for the buffer strategy, the component reinforcement strategy, and the collaborative reinforcement strategy to calculate the strategy priority score. The strategy priority score is compared with a preset trigger threshold to determine whether the three types of strategies should be implemented, the implementation method, and the implementation order, thereby generating a reinforcement scheduling scheme for each target historical building in the building cluster.

[0021] A seismic reinforcement system suitable for historical buildings, comprising:

[0022] Data modeling module: Collects predicted earthquake data of the target area, structural morphology information of the target historical building and adjacent buildings, environmental disturbance information, component crack information and historical damage information, and generates digital twin model to construct three-dimensional structural model and reinforcement dataset;

[0023] Risk assessment module: Performs comprehensive analysis on digital twin models, outputs risk classification, and updates risk heatmap;

[0024] Disturbance analysis module: classifies the response level of component areas and the overall environmental disturbance level;

[0025] Buffer decision module: Calculates structural loads based on environmental disturbance levels and obtains buffer strategies;

[0026] Reinforcement early warning module: Predicts the response level of the component area and generates reinforcement early warning based on the tiered early warning threshold;

[0027] Component reinforcement decision module: Based on component crack information, predicts crack direction and propagation rate, and obtains component reinforcement strategies;

[0028] Collaborative reinforcement decision-making module: Performs strength correlation analysis and obtains collaborative reinforcement strategies based on the results;

[0029] Scheduling optimization module: It uses a multi-objective optimization algorithm to comprehensively calculate seismic performance indicators, reinforcement demand indicators and building importance indicators, and generates reinforcement scheduling schemes for each target historical building in the building cluster.

[0030] The present invention has the following advantages:

[0031] This invention achieves synchronous modeling of the structural state and disturbance environment of historical buildings and their adjacent buildings by constructing a three-dimensional structural model and digital twin covering the building complex, thereby improving the integrity and real-time performance of the complex-level seismic analysis. By integrating the reinforcement dataset and historical damage information, it extracts multi-dimensional risk parameters related to seismic response and outputs quantifiable risk classification results by combining risk level thresholds, which helps to achieve differentiated reinforcement decisions for individual components and building complexes.

[0032] This invention refines the disturbance sensitivity distribution of each component region by performing spectral decomposition and weighted superposition analysis of environmental disturbance signals and dynamic response indicators, thereby improving the accuracy of structural response identification and the reliability of regional risk identification. By combining component response prediction with a tiered early warning mechanism, it achieves multi-level reinforcement early warning output, intervenes in potential failure areas of components in advance, and improves the initiative and timeliness of earthquake-resistant measures deployment.

[0033] By matching analysis based on disturbance level and structural load, triggering criteria and deployment suggestions for buffering strategies were constructed, enhancing the system's rapid response and local protection capabilities under sudden disturbance conditions. By analyzing crack spatial characteristics and propagation trends, component evolution paths were predicted, and matching component reinforcement strategies were generated, effectively improving the accuracy and efficiency of reinforcement schemes in the early stages of crack evolution.

[0034] This invention establishes a structural coupling model and strength correlation analysis method for adjacent buildings to identify the linkage force relationship and collaborative response characteristics, providing a theoretical basis and judgment criteria for collaborative reinforcement across building units. By generating collaborative reinforcement strategies based on linkage requirements, clarifying the collaboration mode and deployment location, it enhances the overall coordination of cluster-level reinforcement schemes and the linkage toughness of structural systems.

[0035] This invention constructs a multi-objective optimization model and dynamically adjusts three types of indicators (seismic performance, reinforcement requirements, and building importance) in conjunction with the level of environmental disturbance. It comprehensively judges the execution scheme and priority of the three types of strategies, thereby realizing the optimal allocation of resources and intelligent scheduling of reinforcement tasks in a multi-disturbance environment. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only schematic diagrams of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0037] Figure 1 This is a structural schematic diagram of a seismic reinforcement system for historical buildings used in an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0039] Example 1: A seismic reinforcement method applicable to historical buildings, comprising the following steps:

[0040] Step S1: Real-time collection of predicted earthquake data for the target area, structural morphology information of the target historical building and adjacent buildings, environmental disturbance information, component crack information and historical damage information, and synchronous matching to generate corresponding digital twins, constructing a three-dimensional structural model and reinforcement dataset covering the building complex;

[0041] In the current embodiment, the predicted earthquake data includes earthquake motion prediction information for the target area within a specific future period. This data originates from earthquake site simulation data released by regional earthquake monitoring agencies or synthetic seismic waves generated by a self-built earthquake prediction model. Key fields include acceleration time history (in m / s²). 2 ), response spectrum (discretely sampled in 0.15-second periodic segments), and peak acceleration (PGA).

[0042] Structural morphology information includes the three-dimensional structural morphology parameters of the target historical building and adjacent buildings in their current state. This information is primarily acquired by a cluster-level UAV laser point cloud scanning system and then cleaned and reconstructed using Building Information Modeling (BIM). Specifically, it includes the floor height, number of floors, component geometric dimensions, connection node types, and component material information for each building, constructing a geometric topology with component regions as the smallest unit.

[0043] Environmental disturbance information is acquired in real time through disturbance sensors deployed at and around the construction site in the target area, including ground accelerometers, attached vibrometers, and underground wave monitoring nodes. The sampling frequency is higher than 500Hz, supporting frequency domain analysis. Recorded information includes the acceleration time history of the disturbance signal, disturbance type labels (e.g., piling, excavation, transportation), and spatial coordinates of the measuring points. Each disturbance data point is bound to its source component region ID, supporting subsequent disturbance response mapping.

[0044] Component crack information includes the location, length, width, direction, and distribution area of ​​cracks detected in various component areas of the cluster buildings using laser mapping instruments and image recognition algorithms. For cracks that cannot be detected on the surface, thermal imaging monitoring equipment is used to assist in judging their potential development trend. The data acquisition cycle varies from 5 to 30 minutes, and combined with historical crack evolution curves, a crack information evolution data chain is formed.

[0045] After data collection is complete, the four types of data are synchronized and matched based on a timestamp alignment and component area spatial mapping mechanism. The timestamp synchronization uses a unified coordinating time server, while the spatial matching uses the component area number in the 3D structural model as the matching benchmark to construct a data mapping matrix between the target historical building and its associated information.

[0046] After synchronization and matching are completed, the component-level digital modeling interface is invoked to generate digital twins of the target historical building and its adjacent buildings. These digital twins are dynamic simulation models built based on structural morphology information and monitoring data, possessing real-time response capabilities. Their internal structure includes component mechanical properties, dynamic disturbance channels, historical damage mapping, and sensor data flow entry points, supporting various subsequent response calculations and early warning simulation tasks.

[0047] Based on the generated digital twin, a three-dimensional structural model covering the entire building complex is constructed through spatial aggregation and classification management. This model, along with various collected data, forms a structured reinforcement dataset. This dataset serves as the core input for the entire process, running through subsequent steps such as seismic hazard analysis, disturbance response assessment, and scheduling optimization.

[0048] Step S2: Based on the reinforcement dataset and historical damage information, perform a comprehensive analysis of the seismic hazard of each digital twin, output the corresponding risk level, and update the risk heat map of the building cluster;

[0049] The risk classification corresponding to the output includes: extracting risk characteristic parameters related to seismic response based on the reinforcement dataset and historical damage information, including crack distribution, deformation capacity, stress path integrity and external disturbance impact in the component area, calculating the seismic hazard assessment value characterizing the seismic resistance of the target historical building, comparing the seismic hazard assessment value with the risk level threshold, determining the risk level of the digital twin, and mapping the risk level to the risk heat map of the building cluster and completing the update.

[0050] In this embodiment, the reinforcement dataset provides the core input parameters of the digital twin in this step, including the following four categories generated in step S1: structural morphology parameters (such as floor height, number of floors, component type and material); disturbance monitoring data (such as spectral response, disturbance level distribution); crack history sequence (obtained by periodic sampling from the structural monitoring system); and initial static and dynamic response values ​​output by the digital twin simulation interface.

[0051] The historical damage information comes from the local cultural relic protection archives database and manual inspection reports, covering the structural damage, repair history, component aging degree, and damage distribution in previous earthquake events for each target historical building. Some of the historical damage data has been standardized into a structural scoring scale, forming a comparable indicator system with sensor data.

[0052] During the extraction of risk characteristic parameters:

[0053] Crack distribution ( f crack This is obtained by comparing the number of cracks, the average length, and the total crack area within the component area;

[0054] Deformability ( f deform The remaining deformation capacity is assessed by comparing historical disturbances with current strain sensor data;

[0055] Force path integrity ( f loadpath Based on the component connection relationships in the three-dimensional structural model, the proportion of failure nodes in the continuous load path is analyzed;

[0056] External disturbance impact ( f disturbance By combining the frequency, amplitude and component response data recorded by the disturbance sensor, the disturbance coupling influence coefficient is weighted and scored.

[0057] After normalization, the above indicators are input into the seismic hazard analysis model for calculation. The "seismic hazard assessment value" is obtained by the following calculation formula: H=w 1 ⋅f crack +w 2 ⋅f deform +w 3 ⋅f loadpath +w 4 ⋅f disturbance ;in H This indicates the hazard assessment value of the target building unit; f *These represent the characteristic values ​​of the four types of risk parameters mentioned above; w1 to w4 are multi-factor weighting coefficients, set based on expert experience or historical statistical data, and dynamically adjusted according to the building type.

[0058] The calculated seismic hazard assessment value, R, is compared with a preset risk level threshold set (e.g., low risk 0.0–0.3, medium risk 0.3–0.6, high risk 0.6–0.8, and extremely high risk >0.8) to determine the risk level label for each digital twin. The risk level results are then linked to component areas in the 3D structural model using a spatial mapping algorithm, and a risk heat map of the building cluster is generated or updated simultaneously. This risk heat map is a two-dimensional visualization layer used to express the risk levels of different building units and their component areas within the cluster space. Its color gradient represents the risk level, it is periodically refreshed automatically, and serves as an important reference for subsequent response analysis, buffer assessment, and reinforcement scheduling.

[0059] Step S3: Perform spectral decomposition on the environmental disturbance information, calculate the dynamic response index of each component area in each frequency band, and perform superimposed analysis. Based on the results, classify the response level of the component area and the overall environmental disturbance level, mark the response level of each component area, and integrate it into the risk heat map.

[0060] The process of classifying the response levels of component areas and the overall environmental disturbance level includes: performing spectral decomposition on the environmental disturbance information, extracting components of different frequency bands from the disturbance signal, and calculating the dynamic response indices of the component areas at each frequency band, including displacement response, stress amplitude, and acceleration change; weighting and superimposing the dynamic response indices at different frequency bands according to the component areas to obtain the comprehensive response of each component area, and classifying the corresponding response level based on the comprehensive response; and simultaneously performing aggregate analysis on the response levels of each component area according to their spatial distribution to determine the environmental disturbance level reflecting the overall disturbance intensity of the building.

[0061] In this embodiment, the acquired disturbance signal is preprocessed using Fast Fourier Transform (FFT) to extract the dominant frequencies and energy components of multiple frequency bands in the disturbance signal. The signal is then divided into three dominant structural response frequency bands: low frequency (<2Hz), mid frequency (2–10Hz), and high frequency (>10Hz) to accommodate the frequency characteristics differences of walls, beams, columns, and decorative components in historical buildings. For each component area, its dynamic response indices are calculated in each frequency band, including but not limited to the following three categories: displacement response, the maximum displacement of the component's center of mass under disturbance, reflecting the overall deformation capacity; stress amplitude, the difference in peak stress values ​​per unit time, reflecting the stress concentration within the structure; and acceleration rate of change, the slope of acceleration change within a continuous time slice, reflecting the severity of vibration.

[0062] The three response parameters mentioned above are time-aligned and mapped to the structure, and then bound to the corresponding component regions. Different weighting coefficients are set according to the importance of different frequency bands in contributing to the structural response, and the weighted sums are then used to form the comprehensive response value of the component region.

[0063] Based on the comprehensive response value, and combined with the structural experience standards and measured data of historical building complexes, multi-level response level thresholds (such as level 5) are set, and the component areas are divided into "normal response", "low disturbance response", "medium disturbance response", "high disturbance response" and "extremely high disturbance response" levels. The response level of each component area, as a visual expression of structural sensitivity, will be embedded in the risk heat map of the building complex in the form of color markings.

[0064] After obtaining the regional response levels of all components, an aggregate analysis is performed based on the spatial distribution density of components and the degree of clustering of disturbance intensity to comprehensively calculate the community-level environmental disturbance level. This level reflects the scope and intensity of the impact of external disturbances on the building community.

[0065] Step S4: Calculate the structural load based on the environmental disturbance level and obtain the buffering strategy;

[0066] The buffer acquisition strategy includes: applying an equivalent transformation of disturbance to the building components of the target historical building based on the environmental disturbance level, calculating the structural load of each component area under different disturbance levels, and performing a matching analysis between the structural load and the disturbance level of the corresponding component area; when the structural load exceeds the preset bearing range, acquiring a buffer strategy, including alternative buffer methods and corresponding deployment locations.

[0067] In this embodiment, the structural load mentioned in this step refers to the equivalent force borne by the building component area under a specific disturbance level. This load combines the disturbance spectrum, amplitude characteristics and component material properties, and is automatically generated by the disturbance-response conversion model.

[0068] To achieve the equivalent conversion of disturbances into loads, the corresponding calculation model parameters are first selected based on the response level and disturbance frequency band weights identified in step S3 for each component region. The main components of the disturbance signal (such as mid-frequency vibrations) are assigned higher dynamic amplification factors to enhance the realism of the structural response simulation. The geometric parameters of the components (such as cross-sectional dimensions and component length) and material properties (such as Young's modulus and damping ratio) are directly extracted from the digital twin to ensure that the load calculations are structurally specific.

[0069] An environmental disturbance level is classified using a disturbance intensity mapping table, with each level corresponding to a set of disturbance input parameter ranges. The disturbance of each level is input into the virtual model of the component through disturbance loading, generating structural load data for the component region under these conditions. This structural load includes the following typical fields: maximum axial force of the component (unit: kN); maximum bending moment of the component (unit: kN·m); relative displacement between nodes (unit: mm); and bearing capacity ratio of the critical stress region.

[0070] The obtained structural load values ​​are compared with the ultimate bearing capacity of the component itself. The ultimate bearing capacity is obtained from structural design drawings and standards, or through calibration by a structural health monitoring system. If the comparison result exceeds the threshold, the component area is determined to be in an overload risk state.

[0071] When an overload risk is detected, the buffering strategy acquisition phase should be initiated immediately. The buffering strategy refers to a proactive intervention method that reduces the impact of risks by pre-deploying buffering devices or local vibration reduction measures before disturbances (earthquakes and high-frequency vibrations caused by construction) trigger actual structural damage.

[0072] Based on the spatial location of the overloaded component, the direction of the disturbance source, and the response category, a set of candidate solutions are retrieved from the buffer strategy library, including but not limited to: vibration isolation pad installation, laying rubber / elastic materials at the bottom of the component or at connection nodes; rapid deployment of dampers, installing temporary energy-dissipating dampers, such as friction type or viscous type; temporary support reinforcement, adding scaffold-type external force support structures in the risk area; construction phase intervention, if construction disturbance is the main source, it is recommended to adjust the work sequence or reduce the work intensity.

[0073] Each candidate strategy is associated with its applicable component area, installation requirements, and impact coefficient. Structural simulation is used to rapidly simulate the response changes caused by different strategy combinations after intervention, and an intervention effectiveness score is output. Finally, the buffer strategy with the highest score is selected, and its corresponding deployment location coordinates are generated for subsequent reinforcement scheduling.

[0074] Step S5: Use the estimated earthquake data as environmental disturbance information to predict the response level of the component area, and generate a reinforcement warning based on the stepped warning threshold;

[0075] The generation of reinforcement early warning includes: comparing the response level of the component area with the tiered early warning threshold, obtaining the early warning status according to its tier, and marking the corresponding component area in the thermal risk map.

[0076] In this embodiment, the "estimated earthquake data" is provided by a third-party earthquake early warning platform and is updated regularly, including: earthquake early warning wave velocity and amplitude; predicted epicenter location and depth; estimated intensity distribution; arrival time and duration.

[0077] This type of data is uniformly converted into disturbance input conditions for structural response prediction. Since these disturbances are future disturbances, the system employs a digital twin-based predictive simulation algorithm to construct a dynamic extrapolation model from disturbance input to response output. This model maps seismic disturbance parameters to the predicted response level for each structural component region, consistent with the response level calculation logic used in step S3, ensuring the consistency of subsequent early warning judgments. The predicted response level is divided into five levels: "Slight Response," "Moderate Response," "Severe Response," "Critical Response," and "Supercritical Response."

[0078] Based on the response level of the component, a comparison is made with a preset tiered warning threshold table. This table categorizes warning levels according to the response level, with common settings as follows: Level I (green): Structural response is below the warning threshold, no intervention required; Level II (yellow): Moderate response, pre-deployment of buffer devices recommended; Level III (orange): Significant response, key attention recommended and local reinforcement preparation initiated; Level IV (red): Predicted overload, immediate warning activation and locking of reinforcement scheduling channels. The generated warning status is not only retained in the response record as a level, but also integrated with the building cluster risk heat map: the warning level information of each component area is automatically overlaid with color labels (e.g., yellow indicates Tier II), allowing regulators or construction decision-makers to quickly identify high-risk areas in the two-dimensional heat map, facilitating subsequent reinforcement deployment or buffer control. Furthermore, the warning status will be used as input parameters in the subsequent S6 step for crack propagation prediction and component reinforcement strategy acquisition, to assist in determining whether cracks are propagating in risk areas and whether the scheduling plan needs to be updated synchronously.

[0079] Step S6: Predict the crack direction and propagation rate based on the component crack information, and obtain the component reinforcement strategy based on the prediction results;

[0080] The acquisition of component reinforcement strategies includes: extracting the spatial distribution characteristics of cracks based on component crack information, including crack length, width, direction and location, and predicting crack direction and propagation rate by combining historical construction evolution data, identifying potential crack penetration paths or structural failure trends; and acquiring component reinforcement strategies based on the prediction results, including alternative reinforcement methods and corresponding reinforcement locations.

[0081] In this embodiment, the component crack information mentioned in this step is collected in real time by a crack monitoring sensor array deployed on the surface of the component. Combined with image recognition and structural scanning data, a complete spatial crack map is formed, which mainly includes the following fields: crack length (mm), width (μm) and depth estimation; crack centerline direction (marked in angular coordinate system); crack start / end coordinates (three-dimensional positioning within the component area); crack activity (defined by the rate of change of time series); historical repair records and recurrence frequency.

[0082] Before predicting the direction and propagation rate of cracks, historical component evolution data (such as previous reinforcement records, disturbance intervention time points, environmental humidity effects, etc.) are fused and modeled to establish a "crack evolution trend model".

[0083] This model employs a graph-based prediction mechanism, treating cracks as path units and dividing the component area into node domains to predict the potential propagation path map of cracks under different disturbance levels and warning states. The simulation system models the possible paths for cracks to propagate from their original point to connecting component boundaries or critical load-bearing nodes after a short period of disturbance response, and calculates the evolution risk score for each path. Simultaneously, the crack propagation rate is quantitatively analyzed. The propagation rate depends not only on the crack's own eigenvalues ​​but also on the current disturbance level, structural fatigue level, and the coupling results of the temperature and humidity fields. Based on these factors, a crack growth regression model is established to calculate the rate of change of crack length and depth per unit time, used to determine whether a warning state has been entered.

[0084] When the prediction results show that the crack has any of the following trends: potentially penetrating key components; advancing towards the structural boundary; or reaching a threshold growth rate, it enters the component reinforcement strategy generation stage.

[0085] The component reinforcement strategy includes the following core components: reinforcement method recommendations, such as external steel reinforcement, carbon fiber cloth reinforcement, component section thickening, rebar repair, and adhesive sealing, which are matched from the strategy library based on the stress type and geometric conditions of the component where the crack is located; reinforcement location determination, which identifies high-risk sections that should be prioritized by combining crack spatial distribution maps and propagation path simulations; reinforcement level assessment, which determines whether to use lightweight rapid sealing measures or deploy high-strength structural reinforcement processes based on crack severity and location sensitivity; and strategy suitability scoring, which filters out unsuitable solutions based on historical building material compatibility, construction feasibility, and time window requirements.

[0086] The output component reinforcement strategy will enter the subsequent scheduling optimization stage, where it will be evaluated together with other strategies to determine whether to implement it and its priority.

[0087] Step S7: Perform strength correlation analysis on the structural coupling relationship between the target historical building and its adjacent buildings, and obtain a collaborative reinforcement strategy based on the results;

[0088] The intensity correlation analysis includes: extracting the structural morphological features, foundation forms and relative spatial arrangement relationships of the target historical building and its adjacent buildings from the three-dimensional structural model, and constructing a structural coupling model between the buildings; evaluating the load transfer path, common load-bearing components and boundary conditions coupling degree between adjacent buildings based on the structural coupling model, and calculating their relative deformation coordination and linkage response intensity under seismic disturbance.

[0089] The acquisition of collaborative reinforcement strategies includes: determining whether there is a need for joint reinforcement between the target historical building and adjacent buildings based on the results of strength correlation analysis; when the need for joint reinforcement reaches the set conditions, acquiring collaborative reinforcement strategies, including alternative collaborative reinforcement methods and corresponding reinforcement locations, wherein the alternative collaborative reinforcement methods include one or a combination of connection reinforcement, shared damping devices, boundary reinforcement, or synchronous unloading structures.

[0090] In this embodiment, in this step, in order to identify the needs and strategies for collaborative reinforcement, a structural coupling modeling and strength correlation analysis mechanism is introduced, which is specifically designed for the complex mutual influence relationships in the cluster-style historical building environment.

[0091] The following data were extracted from the 3D structural model: structural morphological features, including building height, volume, bay layout, and component arrangement; foundation data, including pile foundation arrangement, foundation type (e.g., raft foundation, strip foundation), and burial depth; and spatial arrangement relationships, including horizontal distances between buildings, vertical staggered floors, and connecting structures (e.g., connecting corridors, attached walls). This data was used to construct a structural coupling model, which is expressed using a graph data structure. Individual buildings serve as nodes, common components and coupling boundaries as edges, and attribute weights represent the strength of their physical connections.

[0092] Based on the coupled model, multidimensional structural behavior analysis is performed: load transfer path identification, analyzing possible load transfer chains based on the direction of seismic action and foundation type (e.g., through shared underground walls, aerial connecting beams); detection of shared load-bearing components, identifying shared floor slabs, supporting columns, and foundation blocks among multiple buildings; and determination of the degree of coupling of boundary conditions, judging whether there are physical boundary relationships between buildings that support, restrict, or couple deformations, such as soft connections and rigid connections.

[0093] A uniform disturbance signal is introduced into the simulation platform to calculate the relative deformation compatibility index and the linkage response intensity index between the target historical building and its adjacent buildings. Deformation compatibility reflects whether the two buildings deform synchronously under the same disturbance; linkage response intensity characterizes whether there is a significant coupling trend in the stress response. When the above indices exceed the set coupling threshold, it is determined that there is a need for linkage reinforcement.

[0094] Based on the coupling results and response analysis, one or more of the following collaborative reinforcement methods are selected (which must simultaneously meet the requirements of construction feasibility and structural effectiveness): Connection reinforcement, using flexible connecting components between buildings to control differential displacement; Shared damping devices, installing energy dissipation components (such as hydraulic dampers) between two buildings; Boundary reinforcement, enhancing the strength of coupling areas such as wall junctions and connecting corridor nodes; Synchronous unloading structures, using buffer structures to transfer peak stress on one side of the building to the more stable side. For each collaborative strategy, considering factors such as building spacing, disturbance direction, and deformation mode, the specific deployment location is output (e.g., northwest corner connecting corridor node, middle section of shared basement support wall, etc.). This strategy will serve as an important input parameter for multi-objective optimization scheduling in subsequent steps, participating in the overall arrangement of construction sequence and resource allocation.

[0095] Step S8: Based on the reinforcement dataset and risk heat map, use a multi-objective optimization algorithm to comprehensively calculate the seismic performance index, reinforcement demand index and building importance index, and dynamically adjust the weights of the three types of indexes according to the environmental disturbance level. Make a comprehensive judgment on the buffer strategy, component reinforcement strategy and collaborative reinforcement strategy, determine whether the three types of strategies should be implemented, the implementation method and the implementation order, and generate reinforcement scheduling schemes for each target historical building in the building cluster.

[0096] The acquisition of the reinforcement scheduling scheme includes: calculating the seismic performance index, reinforcement demand index, and building importance index for each target historical building. The seismic performance index includes the remaining bearing capacity of the components, the rate of change of frequency, and the dynamic amplification factor. The reinforcement demand index includes the crack propagation rate in the component area, the degree of structural stiffness degradation, and the response level. The building importance index is determined based on the historical protection level, population density, and regional disaster impact coefficient. The weights of the above three types of indicators are dynamically adjusted according to the environmental disturbance level. A multi-objective optimization model for reinforcement scheduling is constructed, and priority objective functions are established for buffer strategy, component reinforcement strategy, and collaborative reinforcement strategy to calculate the strategy priority score. The strategy priority score is compared with a preset trigger threshold to determine whether the three types of strategies should be implemented, the implementation method, and the implementation order, thereby generating a reinforcement scheduling scheme for each target historical building in the building cluster.

[0097] In this embodiment, to achieve coordinated reinforcement scheduling decisions for multiple types of historical buildings in a building cluster, a three-index multi-objective optimization scheduling model for complex interference scenarios is constructed. The core process is as follows:

[0098] A1. Quantification and normalization of the three types of indicator systems;

[0099] 1. Seismic performance indicators: used to measure the remaining capacity of a structure under disturbance, including: residual bearing capacity of components (estimated based on structural response and material fatigue models); rate of change of component frequency (obtained by comparison with the initial design frequency); dynamic amplification factor (DIF).

[0100] 2. Strengthening requirement indicators: These reflect the urgency and necessity of strengthening, including: crack propagation rate; degree of stiffness degradation; and component response level (derived from the analysis results of S3 and S5).

[0101] 3. Building Importance Indicators: These reflect the cultural, social, and disaster control significance of a building, including: historical preservation level (e.g., national key, local protection); regional population density; and regional disaster impact coefficient (derived from urban planning or seismic hazard analysis).

[0102] The three types of indicators are standardized and uniformly converted into evaluation values ​​between 0 and 1, forming an indicator matrix for use as input to the scheduling model.

[0103] A2. Dynamic weight allocation driven by disturbance level;

[0104] Based on the real-time disturbance levels input during the construction phase (obtained through real-time monitoring by construction sensors), a pre-defined disturbance-weight mapping function is invoked to assign different priority weights to the three types of indicators. The weight adjustment method is a non-linear mapping to ensure that the scheduling model has sensitive responsiveness in actual construction environments.

[0105] For example: in high-disturbance scenarios (such as foundation excavation): seismic performance is given primary weight, ensuring structural safety is the top priority;

[0106] In scenarios with moderate disturbance levels (such as decorative layer construction): the weight of reinforcement demand indicators increases, focusing on the treatment of defects;

[0107] In low-disturbance scenarios (such as remote construction in the surrounding area): the importance weight of buildings is increased to protect key cultural relics in advance.

[0108] A3. Strategy Priority Determination and Hardening Scheduling Scheme Generation

[0109] For each target historical building, three types of strategies (buffering, component reinforcement, and collaborative reinforcement) are processed simultaneously, and their respective priority objective functions are established, taking into account: strategy execution cost (materials / labor / construction period); strategy response effect (ability to suppress disturbances or improve structural stability); strategy feasibility (limited by construction site, protection regulations, etc.); and a priority score list is formed for each type of strategy in different time windows (divided by hours).

[0110] The strategy priority score is compared with its corresponding trigger threshold item by item: if the score is greater than or equal to the threshold, it is marked as to be implemented immediately; if the score is slightly lower than the threshold but the trend is increasing, it is marked as to be implemented later; if the score is lower than the warning line, it is not included in the scheduling of this cycle.

[0111] The three types of strategies are integrated and prioritized based on "whether to implement, implementation method, and implementation order". Combined with the spatial layout of buildings and construction intervention capabilities, a cluster-level reinforcement scheduling list is generated. This list will be used to: issue reinforcement task instructions to the construction platform in different time periods; coordinate material preparation and personnel scheduling; and provide emergency response basis for urban management.

[0112] Example 2: A seismic reinforcement system suitable for historical buildings, see [link to example]. Figure 1 As shown, it includes the following modules:

[0113] Data modeling module: Collects predicted earthquake data of the target area, structural morphology information of the target historical building and adjacent buildings, environmental disturbance information, component crack information and historical damage information, and generates digital twin model to construct three-dimensional structural model and reinforcement dataset;

[0114] Risk assessment module: Performs comprehensive analysis on digital twin models, outputs risk classification, and updates risk heatmap;

[0115] Disturbance analysis module: classifies the response level of component areas and the overall environmental disturbance level;

[0116] Buffer decision module: Calculates structural loads based on environmental disturbance levels and obtains buffer strategies;

[0117] Reinforcement early warning module: Predicts the response level of the component area and generates reinforcement early warning based on the tiered early warning threshold;

[0118] Component reinforcement decision module: Based on component crack information, predicts crack direction and propagation rate, and obtains component reinforcement strategies;

[0119] Collaborative reinforcement decision-making module: Performs strength correlation analysis and obtains collaborative reinforcement strategies based on the results;

[0120] Scheduling optimization module: It uses a multi-objective optimization algorithm to comprehensively calculate seismic performance indicators, reinforcement demand indicators and building importance indicators, and generates reinforcement scheduling schemes for each target historical building in the building cluster.

[0121] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A seismic reinforcement method applicable to historical buildings, characterized in that, include: Real-time collection of predicted earthquake data for the target area, structural morphology information of the target historical buildings and adjacent buildings, environmental disturbance information, component crack information and historical damage information, and synchronous matching to generate corresponding digital twins, constructing a three-dimensional structural model and reinforcement dataset covering the building complex; Based on the reinforcement dataset and historical damage information, the seismic hazard of each digital twin is comprehensively analyzed, the corresponding risk level is output, and the risk heat map of the building cluster is updated. The environmental disturbance information is spectrally decomposed to calculate the dynamic response index of each component area in each frequency band, and the results are overlaid and analyzed. Based on the results, the response level of the component area and the overall environmental disturbance level are divided, the response level of each component area is marked, and the results are integrated into the risk heat map. Calculate structural loads based on environmental disturbance levels and obtain buffering strategies; The predicted earthquake data is used as environmental disturbance information to predict the response level of the component area. Based on the stepped warning threshold, a reinforcement warning is generated. Based on the component crack information, the crack direction and propagation rate are predicted. The component reinforcement strategy is obtained based on the prediction results. Strength correlation analysis is performed on the structural coupling relationship between the target historical building and its adjacent buildings, and a collaborative reinforcement strategy is obtained based on the results. Based on the reinforcement dataset and risk heat map, a multi-objective optimization algorithm is used to comprehensively calculate the seismic performance index, reinforcement demand index and building importance index. The weights of the three types of indexes are dynamically adjusted according to the level of environmental disturbance. The buffer strategy, component reinforcement strategy and collaborative reinforcement strategy are comprehensively judged to determine whether the three types of strategies should be implemented, the implementation method and the implementation order, and to generate reinforcement scheduling schemes for each target historical building in the building cluster.

2. The seismic reinforcement method for historical buildings according to claim 1, characterized in that, The risk classification corresponding to the output includes: extracting risk characteristic parameters related to seismic response based on the reinforcement dataset and historical damage information, including crack distribution, deformation capacity, stress path integrity and external disturbance impact in the component area, calculating the seismic hazard assessment value characterizing the seismic resistance of the target historical building, comparing the seismic hazard assessment value with the risk level threshold, determining the risk level of the digital twin, and mapping the risk level to the risk heat map of the building cluster and completing the update.

3. The seismic reinforcement method for historical buildings according to claim 1, characterized in that, The process of classifying the response levels of component areas and the overall environmental disturbance level includes: performing spectral decomposition on the environmental disturbance information, extracting components of different frequency bands from the disturbance signal, and calculating the dynamic response indices of the component areas at each frequency band, including displacement response, stress amplitude, and acceleration change; weighting and superimposing the dynamic response indices at different frequency bands according to the component areas to obtain the comprehensive response of each component area, and classifying the corresponding response level based on the comprehensive response; and simultaneously performing aggregate analysis on the response levels of each component area according to their spatial distribution to determine the environmental disturbance level reflecting the overall disturbance intensity of the building.

4. The seismic reinforcement method for historical buildings according to claim 1, characterized in that, The buffer acquisition strategy includes: applying an equivalent transformation of disturbance to the building components of the target historical building based on the environmental disturbance level, calculating the structural load of each component area under different disturbance levels, and performing a matching analysis between the structural load and the disturbance level of the corresponding component area; when the structural load exceeds the preset bearing range, acquiring a buffer strategy, including alternative buffer methods and corresponding deployment locations.

5. The seismic reinforcement method for historical buildings according to claim 1, characterized in that, The generation of reinforcement early warning includes: comparing the response level of the component area with the tiered early warning threshold, obtaining the early warning status according to its tier, and marking the corresponding component area in the risk heat map.

6. The seismic reinforcement method for historical buildings according to claim 1, characterized in that, The acquisition of component reinforcement strategies includes: extracting the spatial distribution characteristics of cracks based on component crack information, including crack length, width, direction and location, and predicting crack direction and propagation rate by combining historical construction evolution data, identifying potential crack penetration paths or structural failure trends; and acquiring component reinforcement strategies based on the prediction results, including alternative reinforcement methods and corresponding reinforcement locations.

7. The seismic reinforcement method for historical buildings according to claim 1, characterized in that, The intensity correlation analysis includes: extracting the structural morphological features, foundation forms and relative spatial arrangement relationships of the target historical building and its adjacent buildings from the three-dimensional structural model, and constructing a structural coupling model between the buildings; evaluating the load transfer path, common load-bearing components and boundary conditions coupling degree between adjacent buildings based on the structural coupling model, and calculating their relative deformation coordination and linkage response intensity under seismic disturbance.

8. A seismic reinforcement method for historical buildings according to claim 1, characterized in that, The acquisition of collaborative reinforcement strategies includes: determining whether there is a need for joint reinforcement between the target historical building and adjacent buildings based on the results of strength correlation analysis; when the need for joint reinforcement reaches the set conditions, acquiring collaborative reinforcement strategies, including alternative collaborative reinforcement methods and corresponding reinforcement locations, wherein the alternative collaborative reinforcement methods include one or a combination of connection reinforcement, shared damping devices, boundary reinforcement, or synchronous unloading structures.

9. A seismic reinforcement method for historical buildings according to claim 1, characterized in that, The acquisition of the reinforcement scheduling scheme includes: calculating the seismic performance index, reinforcement demand index, and building importance index for each target historical building. The seismic performance index includes the remaining bearing capacity of the components, the rate of change of frequency, and the dynamic amplification factor. The reinforcement demand index includes the crack propagation rate in the component area, the degree of structural stiffness degradation, and the response level. The building importance index is determined based on the historical protection level, population density, and regional disaster impact coefficient. The weights of the above three types of indicators are dynamically adjusted according to the environmental disturbance level. A multi-objective optimization model for reinforcement scheduling is constructed, and priority objective functions are established for buffer strategy, component reinforcement strategy, and collaborative reinforcement strategy to calculate the strategy priority score. The strategy priority score is compared with a preset trigger threshold to determine whether the three types of strategies should be implemented, the implementation method, and the implementation order, thereby generating a reinforcement scheduling scheme for each target historical building in the building cluster.

10. A seismic reinforcement system suitable for historical buildings, characterized in that, The system employs a seismic reinforcement method for historical buildings as described in any one of claims 1 to 9, comprising: Data modeling module: Collects predicted earthquake data of the target area, structural morphology information of the target historical building and adjacent buildings, environmental disturbance information, component crack information and historical damage information, and generates a digital twin model to construct a three-dimensional structural model and reinforcement dataset; Risk assessment module: Performs comprehensive analysis on digital twin models, outputs risk classification, and updates risk heatmap; Disturbance analysis module: classifies the response level of component areas and the overall environmental disturbance level; Buffer decision module: Calculates structural loads based on environmental disturbance levels and obtains buffer strategies; Reinforcement early warning module: Predicts the response level of the component area and generates reinforcement early warning based on the tiered early warning threshold; Component reinforcement decision module: Predicts crack orientation and propagation rate based on component crack information, and obtains component reinforcement strategies; Collaborative reinforcement decision-making module: Performs strength correlation analysis and obtains collaborative reinforcement strategies based on the results; Scheduling optimization module: It uses a multi-objective optimization algorithm to comprehensively calculate seismic performance indicators, reinforcement demand indicators and building importance indicators, and generates reinforcement scheduling schemes for each target historical building in the building cluster.

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