Safety detection method for existing building curtain wall
By constructing digital twin models and health assessment models for stone curtain walls, the problem of high costs in safety testing of stone curtain walls has been solved, achieving low-cost and high-efficiency testing and risk assessment, and providing detailed testing reports and maintenance recommendations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA CONSTR EIGHT ENG DIV CORP LTD
- Filing Date
- 2026-02-25
- Publication Date
- 2026-06-02
Abstract
Description
Technical Field
[0001] This invention relates to the field of building structure safety testing technology, specifically to a method for testing the safety of existing building curtain walls. Background Technology
[0002] With the development of building technology and people's pursuit of architectural aesthetics, stone curtain walls are increasingly widely used in the construction field, becoming an important means to enhance the quality and value of buildings. As a modern building exterior wall system, stone curtain walls mainly consist of stone unit panels, supporting structures, and sealing materials. Considering the heavy weight of stone, in addition to strict quality control during the early design and construction stages, safety testing during the later use stages is also indispensable; otherwise, it will bring great safety hazards. Currently, the main testing methods used domestically and internationally include: observation, pull-out, and impact testing. These methods have different underlying principles and judgment standards for the testing structures, and require commissioning professional third-party testing units. Considering the building's life cycle, commissioned testing is costly. For the complex decorative structures of stone curtain walls in large public buildings, how to conduct low-cost, efficient, and comprehensive safety testing of existing stone curtain wall components to minimize the impact on normal operation is an urgent problem to be solved. Summary of the Invention
[0003] To overcome the shortcomings of existing technologies, a method for testing the safety of existing building curtain walls is provided to address the high cost of outsourcing safety testing of existing stone curtain wall structures to third parties.
[0004] To achieve the above objectives, a method for safety testing of existing building curtain walls is provided, comprising the following steps: The initial state data of the geometric shape, material properties, connection status and environmental load of the stone curtain wall of the existing building are collected. The initial data includes three-dimensional point cloud data, visible light image data, infrared thermal imaging data, acoustic emission signal data of key structural nodes in a specific time period, structural strain data and ambient temperature data. A digital twin model of the stone curtain wall is constructed using the initial data, and its parameters are calibrated. Using the digital twin model, the structural response data of the stone curtain wall under various typical working conditions were simulated; Based on the initial data and the structural response data, a health assessment model is constructed to calculate the comprehensive health index of the stone curtain wall; The safety level of the stone curtain wall is determined based on the comprehensive health index and the preset safety level.
[0005] Furthermore, the method also includes inputting multiple historical comprehensive health indices of the stone curtain wall as input data sequences into a time-series-based performance degradation model to obtain the evolution curve of the comprehensive health index of the stone curtain wall, and estimating the remaining lifespan of the stone curtain wall based on the evolution curve and a preset performance failure threshold.
[0006] Furthermore, the performance degradation model is an autoregressive moving average model, a Kalman filter model, or a deep learning model based on a long short-term memory network.
[0007] Furthermore, the step of constructing a digital twin model of the stone curtain wall using the initial data includes: Based on the three-dimensional point cloud data, a three-dimensional geometric model of the stone curtain wall is established; Based on the visible light image data and the infrared thermal imaging spectrum data, the defects of the curtain wall panels, the aging cracks of the structural adhesive and the rust areas of the metal components of the stone curtain wall are identified and marked, and mapped in the three-dimensional geometric model to form a visual defect model. Based on the original design data and on-site survey results of the stone curtain wall, initial material constitutive models and physical property parameters are assigned to each component in the three-dimensional geometric model to form an initial finite element analysis model.
[0008] Furthermore, the parameter calibration includes: Using the acoustic emission signal data, the structural strain data, and the ambient temperature data as inputs, the same temperature boundary conditions are applied to the initial finite element analysis model. The material elastic modulus, thermal expansion coefficient, and node connection stiffness parameters in the initial finite element analysis model are adjusted through iterative calculations until the error between the strain calculation result of the initial finite element analysis model under the same temperature boundary conditions and the structural strain data is less than a preset error threshold, thereby obtaining a calibrated digital twin model.
[0009] Furthermore, the step of constructing a health assessment model to calculate the comprehensive health index of the stone curtain wall includes: A primary evaluation index is established for the stone curtain wall, which includes material performance degradation, structural connection reliability, structural load-bearing capacity, and overall deformation control. Secondary evaluation indicators are established under each primary evaluation indicator. The secondary evaluation indicators under the primary evaluation indicator of material performance degradation include a sealing performance sub-indicator based on infrared thermal imaging data and a component appearance defect sub-indicator based on high-definition visible light image data. The secondary evaluation indicators under the primary evaluation indicator of structural connection reliability include a microcrack activity sub-indicator based on acoustic emission signal data and a connection node stress level sub-indicator based on simulated stress distribution cloud map. The secondary evaluation indicators under the primary evaluation indicator of structural bearing capacity include a component strength reserve sub-indicator based on simulated stress-strain time history curve. The secondary evaluation indicators under the primary evaluation indicator of overall deformation control include a panel deflection sub-indicator and an inter-layer displacement angle sub-indicator based on simulated displacement deformation cloud map. Determine the weighting coefficients between each secondary assessment indicator and the primary assessment indicator, as well as between each primary assessment indicator and the comprehensive health index; The comprehensive health index is calculated based on the weighting coefficients using a weighted summation algorithm.
[0010] Furthermore, acoustic emission sensors are deployed at key structural nodes of the stone curtain wall to collect acoustic emission signal data of the key structural nodes within a specific time period.
[0011] The beneficial effects of this invention lie in the fact that the existing building curtain wall safety inspection method of this invention addresses the unique characteristics of different stone curtain wall projects in terms of geographical location, usage environment, construction technology, curtain wall design, and materials. Regular inspection of the stone curtain wall allows for effective risk assessment of changing usage conditions. Therefore, this invention proposes a method for safety assessment of existing building stone curtain walls, achieving low-cost, high-efficiency, and comprehensive safety inspection of existing stone curtain wall components and materials, minimizing the impact on normal operation. Detailed Implementation
[0012] The present application will now be described in further detail with reference to the embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit the invention.
[0013] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present application will now be described in detail with reference to the embodiments.
[0014] This invention provides a method for safety testing of existing building curtain walls, comprising the following steps:
[0015] S1. Collect initial state data on the geometric shape, material properties, connection status, and environmental load of the stone curtain wall of existing buildings.
[0016] The initial data includes 3D point cloud data, visible light image data, infrared thermal imaging data, acoustic emission signal data of key structural nodes within a specific time period, structural strain data, and ambient temperature data.
[0017] Specifically, the three-dimensional point cloud data of the entire curtain wall system and its constituent components are obtained by using drones equipped with three-dimensional laser scanners or ground-based fixed three-dimensional laser scanning equipment.
[0018] When using a drone equipped with a 3D laser scanner for data collection, the 3D flight path of the drone is planned in advance. The 3D flight path ensures that the scanning beam of the 3D laser scanner on the drone can cover all exposed surfaces of the curtain wall to be inspected during the flight, and keeps the distance between the scanner and the curtain wall surface within the preset optimal scanning distance range. At the same time, the flight speed is controlled to ensure that the density of the point cloud data reaches the preset standard of points per square meter.
[0019] High-resolution visible light images and infrared thermal imaging data of curtain wall panels, sealant joints, and operable sashes were acquired using drones equipped with high-definition visible light cameras and infrared thermal imagers.
[0020] At key structural nodes of the curtain wall system, including panel-beam connection nodes, beam-column connection nodes, and column-to-main structure embedded parts connection nodes, acoustic emission sensors, strain sensors, and temperature sensors are pre-deployed or installed on-site to obtain acoustic emission signal data, structural strain data, and ambient temperature data of the key structural nodes within a specific time period.
[0021] S2. Construct a digital twin model of the stone curtain wall using the initial data and perform parameter calibration.
[0022] Based on the initial state data, a multi-dimensional digital twin model that precisely corresponds to the physical entity of the existing building curtain wall is constructed in a computer-aided design and engineering analysis platform, and the parameters of the digital twin model are calibrated.
[0023] In step S2, the steps of constructing a digital twin model of the stone curtain wall using the initial data include: S21. Based on the three-dimensional point cloud data, establish a three-dimensional geometric model of the stone curtain wall.
[0024] The three-dimensional geometric model accurately reproduces the actual spatial position, size, and geometric deviation of the curtain wall panels, columns, beams, connectors, and fasteners.
[0025] S22. Based on visible light image data and infrared thermal imaging data, identify and label the defects of the curtain wall panels, the aging cracks of the structural adhesive, and the rust areas of the metal components of the stone curtain wall, and map them in the three-dimensional geometric model to form a visual defect model.
[0026] The acquired infrared thermal imaging data is used to identify thermal bridges or thermal defects caused by aging or damage of the sealing strip or failure of the air gap seal in the glass panel, and the coordinate information of the identified defect areas is integrated into the visual defect model.
[0027] S23. Based on the original design data and on-site survey results of the stone curtain wall, assign initial material constitutive models and physical property parameters to each component in the three-dimensional geometric model to form an initial finite element analysis model.
[0028] In step S2, the parameter calibration step includes: Acoustic emission signal data, structural strain data, and ambient temperature data acquired within a specific time period are used as inputs. The same temperature boundary conditions are applied to the initial finite element analysis model. The material elastic modulus, thermal expansion coefficient, and node connection stiffness parameters in the initial finite element analysis model are adjusted through iterative calculations until the error between the strain calculation results of the initial finite element analysis model under the same temperature boundary conditions and the structural strain data is less than a preset error threshold, thereby obtaining a calibrated digital twin model.
[0029] During the parameter calibration process of the digital twin model, the location of microcrack initiation points detected by acoustic emission sensors is determined, and the location results are compared and verified with the high-stress region in the initial finite element analysis model. Utilizing the time difference between the reception of the same event signal by multiple acoustic emission sensors, the three-dimensional spatial coordinates of the acoustic emission event source are calculated using a pre-defined wave velocity model and triangulation algorithm. If these coordinates spatially match the predicted high-stress concentration region in the finite element model, the accuracy and reliability of the calibrated digital twin model are further verified, and this degree of agreement is used as an additional criterion for determining the convergence of the calibration process.
[0030] The initial material constitutive models include not only linear elastic models but also nonlinear constitutive models that describe the viscoelastic or hyperelastic mechanical behavior of polymeric materials such as structural sealants and silicone weather-resistant adhesives. The nonlinear constitutive models are either the Mooney-Rivlin model or the Ogden model. Initial values for physical property parameters are preferentially obtained from as-built drawings, material certificates of conformity, and relevant material performance databases. When data is lacking, these values are determined through material testing of non-critical components sampled on-site.
[0031] S3. Using a digital twin model, simulate the structural response data of the stone curtain wall under various typical working conditions.
[0032] Specifically, step S3 includes: Based on historical meteorological data of the area where the existing buildings are located and current building structural load codes, wind load cases, temperature stress cases and seismic action cases are defined. Wind load, temperature stress, and seismic action are applied separately or in combination to the calibrated digital twin model, and nonlinear static and dynamic time history analyses are performed to obtain stress distribution cloud maps, displacement deformation cloud maps, inter-story drift angles, and stress-strain time history curves of key nodes in the curtain wall system under various working conditions.
[0033] The wind load case is defined as a simulation using computational fluid dynamics (CFD) to create a detailed model of the existing building and its surrounding environment. This model simulates the wind pressure distribution on the building surface under different wind directions and speeds, and applies this detailed wind pressure distribution as boundary conditions to the calibrated digital twin model surface. This replaces the simplified shape coefficient method in load specifications, thus obtaining wind-induced response results that more closely approximate reality. The seismic waves input for the dynamic time history analysis are selected from a standard seismic wave library or artificially synthesized based on the site category and design earthquake grouping, ensuring that their spectral characteristics match the target response spectrum.
[0034] S4. Based on the initial data and structural response data, construct a health assessment model to calculate the comprehensive health index of the stone curtain wall.
[0035] Specifically, the steps for constructing a health assessment model to calculate the comprehensive health index of stone curtain walls include: Establish primary evaluation indicators for stone curtain walls, including material performance degradation, structural connection reliability, structural load-bearing capacity, and overall deformation control. Secondary evaluation indicators are established under each primary evaluation indicator. The secondary evaluation indicators under the primary evaluation indicator of material performance degradation include the sealing performance sub-indicator based on infrared thermal imaging data and the component appearance defect sub-indicator based on high-definition visible light image data. The secondary evaluation indicators under the primary evaluation indicator of structural connection reliability include the microcrack activity sub-indicator based on acoustic emission signal data and the connection node stress level sub-indicator based on simulated stress distribution cloud map. The secondary evaluation indicators under the primary evaluation indicator of structural bearing capacity include the component strength reserve sub-indicator based on simulated stress-strain time history curve. The secondary evaluation indicators under the primary evaluation indicator of overall deformation control include the panel deflection sub-indicator and the inter-layer displacement angle sub-indicator based on simulated displacement deformation cloud map. The weighting coefficients between each secondary evaluation indicator and the primary evaluation indicator, as well as between each primary evaluation indicator and the comprehensive health index, are determined based on expert scoring or the analytic hierarchy process. The comprehensive health index is calculated based on the weighting coefficients using a weighted summation algorithm.
[0036] Acoustic emission sensors deployed at key structural nodes are used to monitor in real time the elastic wave signals generated by the initiation and propagation of internal microcracks in curtain wall components under environmental loads. The acoustic emission signal data is filtered, amplified, and subjected to feature parameter extraction by a signal processor. The extracted feature parameters include ring count, energy, amplitude, and duration. These parameters are used in step S4 to evaluate the microcrack activity sub-indicator under the primary assessment index of structural connection reliability.
[0037] In step S4, based on the initial state data obtained from the multi-source heterogeneous data acquisition step and the structural response data obtained from the virtual loading simulation and performance analysis step, a multi-level, weighted fusion comprehensive health assessment model for the curtain wall system is constructed, and a comprehensive health index characterizing the current safety status is calculated.
[0038] The quantitative calculation method for the sealing performance sub-index under the primary evaluation index of material performance degradation is as follows: First, the infrared thermal imaging data is segmented to identify temperature anomaly areas. Then, the area of each anomaly area and its average temperature difference with the background area are calculated. The product of the area and temperature difference of all anomaly areas is accumulated and divided by the total area of the curtain wall to obtain a standardized thermal defect value. This value is normalized to become the sealing performance sub-index.
[0039] The quantitative calculation method for the stress level sub-index of connection nodes under the primary evaluation index of structural connection reliability is as follows: The maximum stress values of each key connection node obtained from virtual loading simulation under all working conditions are extracted. The maximum stress value is compared with the allowable stress value of the material of the connection node, and the stress ratio is calculated. The maximum value or weighted average value of the stress ratio of all key nodes is transformed by a preset nonlinear mapping function to obtain the stress level sub-index of the connection node. The design of the mapping function ensures that when the stress ratio is close to 1, the sub-index value drops sharply to reflect the reduction of the safety margin.
[0040] S5. Determine the safety level of the stone curtain wall based on the comprehensive health index and the preset safety level.
[0041] Specifically, based on the numerical range of the comprehensive health index, the safety status of existing building curtain walls is divided into multiple preset safety levels, and an inspection report containing a three-dimensional visualized risk heat map and specific maintenance recommendations is generated.
[0042] Safety levels are divided into at least four levels: "Healthy", "Attention", "Warning", and "Danger". Each level corresponds to a specific range of comprehensive health index.
[0043] Risk heatmaps use different colors to identify areas and components with different safety levels on a three-dimensional geometric model.
[0044] Maintenance recommendations automatically generate corresponding handling measures based on the safety level of the component, including "routine inspection", "enhanced monitoring", "partial repair" or "emergency replacement".
[0045] The generated inspection report is an interactive 3D digital report. Users can freely rotate, scale, and section the 3D geometric model of the curtain wall in the report. Clicking on any component or area on the model will instantly bring up detailed information about that component or area, including its safety level, the scores of each sub-indicator, the relevant original image data, the simulation result cloud map, and specific maintenance recommendations. The maintenance recommendations are interfaced with the Building Information Modeling (BIM) or Facility Management (FM) system, which can automatically push the generated repair or replacement task orders to the corresponding management platform to achieve closed-loop management of inspection, evaluation, decision-making, and execution.
[0046] S6. Input multiple historical comprehensive health indices of the stone curtain wall as input data sequences into the time series-based performance degradation model to obtain the evolution curve of the comprehensive health index of the stone curtain wall, and estimate the remaining life of the stone curtain wall based on the evolution curve and the preset performance failure threshold.
[0047] The performance degradation model can be an autoregressive moving average model, a Kalman filter model, or a deep learning model based on a long short-term memory network.
[0048] Specifically, step S6 includes: Establish a time-series-based performance degradation model, using the comprehensive health index obtained from multiple executions of steps S1 to S5 as the input data sequence; The performance degradation model fits the time series of the comprehensive health index and extrapolates it to the future to predict the evolution curve of the comprehensive health index of the curtain wall system over a period of time. Based on the preset performance failure threshold, i.e., if the comprehensive health index is lower than this value, the curtain wall system is considered to have reached the end of its service life. The time point when the index reaches this threshold is determined from the evolution curve, thereby estimating the remaining service life of the existing building curtain wall.
[0049] The safety inspection method for existing building curtain walls of this invention addresses the unique characteristics of different stone curtain wall projects in terms of geographical location, usage environment, construction technology, curtain wall design, and materials. Regular inspections of stone curtain walls allow for effective risk assessment of changing usage conditions. Therefore, this invention proposes a method for safety assessment of existing stone curtain walls, achieving low-cost, high-efficiency, and comprehensive safety testing of existing stone curtain wall components, minimizing impact on normal operation.
[0050] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.
Claims
1. A method for safety testing of existing building curtain walls, characterized in that, Includes the following steps: The initial state data of the geometric shape, material properties, connection status and environmental load of the stone curtain wall of the existing building are collected. The initial data includes three-dimensional point cloud data, visible light image data, infrared thermal imaging data, acoustic emission signal data of key structural nodes in a specific time period, structural strain data and ambient temperature data. A digital twin model of the stone curtain wall is constructed using the initial data, and its parameters are calibrated. Using the digital twin model, the structural response data of the stone curtain wall under various typical working conditions were simulated; Based on the initial data and the structural response data, a health assessment model is constructed to calculate the comprehensive health index of the stone curtain wall; The safety level of the stone curtain wall is determined based on the comprehensive health index and the preset safety level.
2. The method for safety testing of existing building curtain walls according to claim 1, characterized in that, It also includes inputting multiple historical comprehensive health indices of the stone curtain wall as input data sequences into a time-series-based performance degradation model to obtain the evolution curve of the comprehensive health index of the stone curtain wall, and estimating the remaining lifespan of the stone curtain wall based on the evolution curve and a preset performance failure threshold.
3. The method for safety testing of existing building curtain walls according to claim 2, characterized in that, The performance degradation model is an autoregressive moving average model, a Kalman filter model, or a deep learning model based on a long short-term memory network.
4. The method for safety testing of existing building curtain walls according to claim 1, characterized in that, The steps for constructing a digital twin model of the stone curtain wall using the initial data include: Based on the three-dimensional point cloud data, a three-dimensional geometric model of the stone curtain wall is established; Based on the visible light image data and the infrared thermal imaging spectrum data, the defects of the curtain wall panels, the aging cracks of the structural adhesive and the rust areas of the metal components of the stone curtain wall are identified and marked, and mapped in the three-dimensional geometric model to form a visual defect model. Based on the original design data and on-site survey results of the stone curtain wall, initial material constitutive models and physical property parameters are assigned to each component in the three-dimensional geometric model to form an initial finite element analysis model.
5. The method for safety testing of existing building curtain walls according to claim 4, characterized in that, The parameter calibration includes: Using the acoustic emission signal data, the structural strain data, and the ambient temperature data as inputs, the same temperature boundary conditions are applied to the initial finite element analysis model. The material elastic modulus, thermal expansion coefficient, and node connection stiffness parameters in the initial finite element analysis model are adjusted through iterative calculations until the error between the strain calculation result of the initial finite element analysis model under the same temperature boundary conditions and the structural strain data is less than a preset error threshold, thereby obtaining a calibrated digital twin model.
6. The method for safety testing of existing building curtain walls according to claim 1, characterized in that, The steps of constructing a health assessment model to calculate the comprehensive health index of the stone curtain wall include: A primary evaluation index is established for the stone curtain wall, which includes material performance degradation, structural connection reliability, structural load-bearing capacity, and overall deformation control. Secondary evaluation indicators are established under each primary evaluation indicator. The secondary evaluation indicators under the primary evaluation indicator of material performance degradation include a sealing performance sub-indicator based on infrared thermal imaging data and a component appearance defect sub-indicator based on high-definition visible light image data. The secondary evaluation indicators under the primary evaluation indicator of structural connection reliability include a microcrack activity sub-indicator based on acoustic emission signal data and a connection node stress level sub-indicator based on simulated stress distribution cloud map. The secondary evaluation indicators under the primary evaluation indicator of structural bearing capacity include a component strength reserve sub-indicator based on simulated stress-strain time history curve. The secondary evaluation indicators under the primary evaluation indicator of overall deformation control include a panel deflection sub-indicator and an inter-layer displacement angle sub-indicator based on simulated displacement deformation cloud map. Determine the weighting coefficients between each secondary assessment indicator and the primary assessment indicator, as well as between each primary assessment indicator and the comprehensive health index; The comprehensive health index is calculated based on the weighting coefficients using a weighted summation algorithm.
7. The method for safety testing of existing building curtain walls according to claim 1, characterized in that, Acoustic emission sensors are deployed at key structural nodes of the stone curtain wall to collect acoustic emission signal data of the key structural nodes within a specific time period.