Building post-earthquake state simulation method based on digital twinning
By constructing a geometrically-physical coupled digital twin and a multi-source sensor network, combined with a data assimilation algorithm, the post-earthquake status of buildings is updated in real time, solving the problems of hazard, long cycle and low accuracy of traditional assessment methods, and realizing high-fidelity and rapid damage identification and assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SEISMOLOGICAL BUREAU OF GANSU PROVINCE CHINA EARTHQUAKE ADMINISTRATION
- Filing Date
- 2026-01-20
- Publication Date
- 2026-05-12
AI Technical Summary
Existing methods for assessing the post-earthquake condition of buildings suffer from problems such as high detection risk, long cycle, low accuracy, and a disconnect between the model and the actual situation. The application of digital twin technology in this field is insufficient.
A geometrically-physical coupled digital twin is constructed, embedded with a nonlinear finite element structural model, and a multi-source sensor network is deployed to collect data in real time. The structural response data and simulation results are dynamically fused using a data assimilation algorithm, the digital twin is updated in real time, post-earthquake damage indicators are calculated, and the results are displayed through a three-dimensional visualization platform.
It achieves high-fidelity dynamic reconstruction of the post-earthquake state of buildings, improves the accuracy and timeliness of damage identification, and significantly enhances emergency response efficiency.
Smart Images
Figure CN122021276A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of digital twin simulation technology, and in particular to a method for simulating the post-earthquake state of buildings based on digital twins. Background Technology
[0002] Existing methods for assessing the post-earthquake condition of buildings mainly fall into two categories: One is the traditional on-site inspection method, where inspectors, equipped with specialized equipment, enter the building to manually inspect for damage such as cracks, deformation, and material spalling, and then assess the overall condition using structural mechanics calculations. However, this method has significant drawbacks: firstly, the post-earthquake building environment is complex and highly hazardous, making manual inspection prone to secondary disasters and difficult to cover all critical components; secondly, the inspection cycle is long, typically requiring 3-7 days to complete a full assessment, failing to meet the timeliness requirements of emergency response; and thirdly, the assessment results rely heavily on the inspectors' experience, are highly subjective, and lack accuracy. The other category is the traditional numerical simulation method, which establishes a finite element model of the building and inputs seismic motion parameters to simulate the post-earthquake response. However, this method suffers from a disconnect between the model and the physical entity: on the one hand, model parameters are based on design drawings and cannot reflect actual conditions such as construction deviations and material aging, leading to significant errors between the simulation results and the actual post-earthquake condition; on the other hand, the simulation process cannot integrate real-time post-earthquake sensing data, cannot dynamically update the building's damage evolution, and struggles to accurately predict the risk of secondary damage under aftershocks. Currently, digital twin technology has been initially applied in fields such as building lifecycle management and intelligent manufacturing, but its application in the field of post-earthquake building state simulation still has many shortcomings. Therefore, for those skilled in the art, how to conduct high-precision post-earthquake building state simulation based on digital twin technology is an urgent problem to be solved. Summary of the Invention
[0003] The purpose of this invention is to provide a digital twin-based method for simulating the post-earthquake state of buildings to solve the problems mentioned in the background art. It integrates digital twin technology, structural health monitoring data, seismic motion input and finite element simulation model to achieve high-fidelity, real-time simulation and evaluation of the structural state of buildings after earthquake.
[0004] To achieve the above objectives, the present invention provides the following solution: a method for simulating the post-earthquake state of buildings based on digital twins, the specific steps of which include the following: A geometric-physical coupled digital twin of the target building is constructed based on three-dimensional geometric data, and a nonlinear finite element structural model is embedded to obtain the initial digital twin; Based on the basic information of the target building, a multi-source sensor network is deployed to collect structural response data in real time before, during, and after the earthquake. The ground motion time history data is used as an external excitation input to drive the initial digital twin to perform dynamic time history simulation and obtain simulation results; A data assimilation algorithm is used to dynamically fuse the structural response data with the simulation results, and the initial digital twin is updated in real time. The system calculates post-earthquake structural damage indicators based on the updated digital twin, generates an assessment report, and displays the post-earthquake status of buildings through a 3D visualization platform.
[0005] Preferably, the three-dimensional geometric data includes building information model data or three-dimensional point cloud data; the geometry-physical coupled digital twin simultaneously characterizes the geometric morphology and structural mechanical properties of the target building.
[0006] Preferably, the process further includes verifying the initial digital twin. The verification criteria are that the geometric error is less than a set first threshold and the mechanical performance error is less than a set second threshold. After the verification is passed, the process proceeds to the next step. The first threshold and the second threshold are set according to industry standards based on the needs of the application scenario.
[0007] Preferably, the basic information of the target building includes a BIM model, design drawings, and material testing reports; based on the basic information, the key vulnerable components of the target building are identified; and a multi-source sensor network, including accelerometers, strain gauges, and displacement gauges, is deployed on the key vulnerable components.
[0008] Preferably, the data assimilation algorithm includes extended Kalman filtering, unscented Kalman filtering, or a deep learning-based surrogate model correction algorithm.
[0009] Preferably, by minimizing the residual between the simulated value and the measured value, the model parameters in the initial digital twin are corrected in real time, including material stiffness degradation parameters, damping ratio, boundary connection state and local damage factor, thereby realizing online updating of the digital twin and obtaining an updated digital twin that tends to be consistent with the actual structural state.
[0010] Preferably, the structural response data is first preprocessed by outlier removal and spatiotemporal matching, and then the preprocessed structural response data is assimilated and fused with the simulation results to ensure data consistency.
[0011] Preferably, the post-earthquake structural damage indicators include inter-story drift angle, degree of plastic hinge development, component damage index, overall structural ductility ratio, location of weak story, and degree of damage concentration.
[0012] Preferably, after the post-earthquake simulation assessment is completed, the entire process data of this earthquake event, model update records, and assessment results are stored in a historical case database to train machine learning models and improve the level of post-earthquake response prediction and digital twin initialization for similar buildings.
[0013] Preferably, the 3D visualization platform is built using WebGL or Unity3D engine, supports integration with GIS system, and realizes the post-earthquake status linkage display of building group-level digital twins.
[0014] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: (1) Achieving high-fidelity dynamic reconstruction of the post-earthquake state of buildings. By constructing a geometrically-physical coupled digital twin, not only is the spatial form of the building accurately represented, but its real mechanical behavior is also embedded. Compared with traditional post-earthquake assessment methods that rely solely on empirical formulas or simplified models, this invention can more realistically simulate the complex response process of structures under strong earthquakes, significantly improving the physical consistency and credibility of simulation results.
[0015] (2) Improve the accuracy and timeliness of post-earthquake damage identification. By deploying a multi-source sensor network to acquire structural response data in real time before, during, and after the earthquake, and then using a data assimilation algorithm to dynamically fuse the measured data with the simulation results, the model parameters are corrected online, so that the digital twin continuously approximates the real structural state. This closed-loop feedback mechanism effectively overcomes the "model mismatch" problem of traditional simulation and greatly improves the accuracy of damage location and quantification. Through the technical closed loop of "real-time sensing + online simulation + rapid assimilation + automatic evaluation + instant push", this invention improves the traditional post-earthquake damage identification from "hours / days after the event" to "minutes from the epicenter to the post-earthquake response level", thereby significantly improving the timeliness of damage identification, urban seismic resilience, and emergency response efficiency. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] The purpose of this invention is to provide a method for simulating the post-earthquake state of buildings based on digital twins, such as... Figure 1 As shown, the specific steps include the following: S1. Construct a geometric-physical coupled digital twin of the target building based on three-dimensional geometric data, and embed a nonlinear finite element structural model to obtain the initial digital twin; S2. Deploy a multi-source sensor network based on the basic information of the target building to collect structural response data in real time before, during and after the earthquake. S3. Use the ground motion time history data as an external excitation input to drive the initial digital twin to perform dynamic time history simulation and obtain the simulation results; S4. Employ a data assimilation algorithm to dynamically fuse structural response data with simulation results and update the initial digital twin in real time; S5. Calculate post-earthquake structural damage indicators based on the updated digital twin, generate an assessment report, and display the post-earthquake status of the building through a 3D visualization platform.
[0020] Furthermore, in S1, the 3D geometric data includes building information model data or 3D point cloud data; importing the 3D geometric data into the digital twin platform to construct a twin mapping model of the target building can be achieved through the following steps: S11. Preprocess the architectural structure information of the three-dimensional geometric data to obtain architectural structure preprocessing information; S12. Upload the pre-processed building structure information to the digital twin platform; S13. Initialize the building information tools for the digital twin platform; S14. Construct a twin mapping model of the target building using preprocessed building structure information and building information tools. The geometric-physical coupled digital twin simultaneously represents the geometric morphology and structural mechanical properties of the target building. The nonlinear finite element structural model includes the plastic damage constitutive relations of concrete and steel, which can accurately represent the plastic deformation process and damage evolution law of components such as beams, columns, and shear walls in the target building.
[0021] After the initial digital twin is constructed, it is validated. The validation criteria are geometric error and mechanical performance error. If the geometric error is less than a set first threshold and the mechanical performance error is less than a set second threshold, the validation proceeds to the next step. The first and second thresholds are set according to industry standards based on the application scenario. In this embodiment, validation is considered successful when the geometric error is <5% and the mechanical performance error is <10%. If validation fails, the 3D geometric data or nonlinear finite element structural model parameters are readjusted until validation is successful. By introducing an initial digital twin validation mechanism, the reliability of the simulation starting point is ensured.
[0022] Furthermore, in S2, the basic information of the target building includes a BIM model, design drawings, and material testing reports. Based on this basic information, key vulnerable components of the target building are identified. A multi-source sensor network, including accelerometers, strain gauges, and displacement gauges, is deployed on these key vulnerable components. The sensor sampling frequency is ≥100Hz, and the sensors possess the capability to withstand seismic impact strength ≥1000g and electromagnetic interference immunity ≥EMI Class B. The sensors are not isolated but rather form a collaborative monitoring system via wired or wireless means (such as LoRa, 5G, NB-IoT), possessing capabilities such as data synchronization, remote transmission, and edge computing.
[0023] Furthermore, in S3, ground motion time history data refers to the record of ground motion changing over time during an earthquake. It originates from actual seismic station records or artificially synthesized seismic waves. Ground motion time history data is used as external excitation input to the initial digital twin constructed above to perform dynamic time history simulation and obtain simulation results. This step realizes the use of real earthquake input to drive a high-fidelity virtual model and then predict structural response, serving as a bridge connecting the physical world and the digital world.
[0024] Furthermore, the data assimilation algorithms in S4 include extended Kalman filtering, unscented Kalman filtering, or deep learning-based surrogate model correction algorithms. The specific steps for dynamically fusing structural response data with simulation results using data assimilation algorithms are as follows: S41, First, the structural response data collected by S2 is preprocessed. The preprocessing includes outlier removal, noise reduction and spatiotemporal matching to obtain standardized structural response data. S42, then input the standardized structural response data and structural dynamic time history simulation results into the selected data assimilation algorithm for assimilation and fusion, and output the fusion result; S43. Verify data consistency based on fusion results. If the consistency meets the preset threshold, proceed to the subsequent correction steps. If it does not meet the threshold, return to step S41 for reprocessing.
[0025] By employing a data assimilation algorithm, the structural response measured by sensors in the physical world is dynamically fused with the simulation results in the virtual world. This continuously corrects and optimizes the digital twin model, making it closer to the state of the real structure. By minimizing the residual between the simulated and measured values, the model parameters in the initial digital twin are corrected in real time, including material stiffness degradation parameters, damping ratio, boundary connection state, and local damage factor. This enables online updates of the digital twin, resulting in an updated digital twin that closely matches the actual structural state.
[0026] Furthermore, in S5, post-earthquake structural damage indicators are calculated based on the updated digital twin, including inter-story drift angle, plastic hinge development, component damage index, overall structural ductility ratio, weak story location, and damage concentration. Based on the updated high-fidelity digital twin, multi-dimensional structural damage indicators are calculated to generate a structural assessment report. The assessment results not only reflect overall safety but also accurately identify areas requiring emergency reinforcement or areas prohibited from use, providing data-driven scientific basis for emergency decisions (such as personnel evacuation and repair priority) and avoiding "overly conservative" or "misjudgment of risk."
[0027] Based on the updated digital twins, post-earthquake structural damage indicators are calculated, an assessment report is generated, and the post-earthquake status of buildings is displayed through a 3D visualization platform. The 3D visualization platform is built using WebGL or Unity3D engines, supports integration with GIS systems, and enables synchronized display of post-earthquake status across building clusters of digital twins. Through the 3D visualization platform, abstract damage indicators are transformed into intuitive visual elements such as heat maps, deformation cloud maps, and crack markers, and supports multi-angle browsing, time-lapse playback, and focusing on key areas. Simultaneously, the platform can be integrated with GIS systems, extending to the city-wide building cluster level, to achieve synchronized display of regional earthquake damage, greatly improving the efficiency of emergency command and public communication.
[0028] Furthermore, after the post-earthquake simulation assessment is completed, the entire process data of this earthquake event, model update records, and assessment results are stored in a historical case database. This data is used to train machine learning models, improving the prediction of post-earthquake responses of similar buildings and the initialization level of digital twins. As cases accumulate, the system can achieve intelligent prediction of post-earthquake responses of similar buildings and rapid initialization of digital twins, reducing modeling costs and improving the ability to rapidly assess large-scale urban building complexes after earthquakes.
[0029] This embodiment uses a 10-story reinforced concrete frame structure building as the target building. It employs the post-earthquake state simulation method based on digital twins described in this invention to conduct post-earthquake state simulation and assessment. The specific implementation environment is as follows: Intel Core i9-12900K processor, 64GB memory, Windows 10 Professional operating system, ANSYS Mechanical APDL simulation software, data assimilation algorithm implemented in Python, and 3D visualization platform developed based on Unity 3D. The specific implementation steps are as follows: Step 1: Initial Digital Twin Construction Obtain BIM data of the target building (including component dimensions, material information, node connection methods, etc.), import the BIM data into the digital twin modeling platform, and construct the geometric model of the target building. Based on the building structural design code, determine the concrete strength grade as C30 and the steel as HRB400, and establish a nonlinear finite element structural model including the plastic damage constitutive models of concrete and steel. Embed this nonlinear finite element structural model into the geometric model to form a geometric-physical coupled digital twin. Verify the initial digital twin using static loading test data. By comparing the displacement calculated by the model with the experimentally measured displacement, the geometric error is found to be 3.2%, and the mechanical performance error is 7.8%, both meeting the preset thresholds. The verification is successful, and the initial digital twin is obtained.
[0030] Step 2: Deployment and Data Acquisition of Multi-Source Sensor Network Based on the stress characteristics of the target building's frame structure, the sensor deployment scheme was determined as follows: accelerometers were deployed at the beam-column joints of each floor from the 1st to the 10th floor; strain gauges were deployed at the bottom columns, top beams, and shear wall ends; and displacement gauges were deployed at the four corners of the roof, for a total of 40 accelerometers, 24 strain gauges, and 4 displacement gauges. The sensor sampling frequency was set to 200Hz. The data acquisition instrument collected structural response data such as acceleration, strain, and displacement in real time before, during, and after the earthquake. Data transmission adopted a 5G industrial IoT module to ensure the real-time performance and stability of data transmission.
[0031] Step 3: Dynamic Time History Simulation Seismic time history data of the area where the target building is located is collected, and the seismic time history data is imported into simulation software and applied as an external excitation to the initial digital twin. The simulation time step is set to 0.01s, and structural dynamic time history simulation is carried out to obtain simulation results such as displacement, stress, and acceleration of each component.
[0032] Step 4: Digital Twin Update Extended Kalman filtering was used as the preset data assimilation algorithm. The structural response data collected by the sensors was preprocessed: outliers were removed and noise was reduced by wavelet transform. Spatiotemporal matching of the data was achieved based on timestamps to obtain standardized structural response data. The standardized structural response data was dynamically fused with the simulation results to output the fusion result. Based on the result, the parameters of the initial digital twin, such as the elastic modulus of concrete and the damping ratio of steel, were corrected to obtain the updated digital twin. The mechanical response of the updated digital twin was found to have an error of 5.6% compared with the measured response.
[0033] Step 5: Post-earthquake condition assessment and visualization Based on the updated digital twin, component-level and structural-level damage indices are calculated to generate a post-earthquake condition assessment report including damaged locations, damage levels, and repair recommendations. The damage data is imported into a 3D visualization platform developed using Unity 3D, with different colors used to label damage levels (green: no damage, yellow: minor damage, orange: moderate damage, red: severe damage) to achieve real-time display of building geometry, damage distribution, and stress cloud maps. Users can click on damaged locations to view detailed damage information and export the assessment report online. By comparing the post-earthquake damage assessment results obtained by this method with actual post-earthquake on-site detection results, it was found that the damage locations matched 92% and the damage level determination was consistent 88%, verifying the accuracy of the method.
[0034] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for simulating the post-earthquake state of buildings based on digital twins, characterized in that, The specific steps include the following: A geometric-physical coupled digital twin of the target building is constructed based on three-dimensional geometric data, and a nonlinear finite element structural model is embedded to obtain the initial digital twin; Based on the basic information of the target building, a multi-source sensor network is deployed to collect structural response data in real time before, during, and after the earthquake. The ground motion time history data is used as an external excitation input to drive the initial digital twin to perform dynamic time history simulation and obtain simulation results; A data assimilation algorithm is used to dynamically fuse the structural response data with the simulation results, and the initial digital twin is updated in real time. The system calculates post-earthquake structural damage indicators based on the updated digital twin, generates an assessment report, and displays the post-earthquake status of buildings through a 3D visualization platform.
2. The method for simulating the post-earthquake state of buildings based on digital twins according to claim 1, characterized in that, The three-dimensional geometric data includes building information model data or three-dimensional point cloud data; the geometry-physical coupled digital twin simultaneously represents the geometric morphological features and structural mechanical properties of the target building.
3. The method for simulating the post-earthquake state of buildings based on digital twins according to claim 1, characterized in that, The initial digital twin is verified, with the verification indicators being that the geometric error is less than a set first threshold and the mechanical performance error is less than a set second threshold. After the verification is passed, the subsequent steps are carried out. The first threshold and the second threshold are set according to industry standards based on the needs of the application scenario.
4. The method for simulating the post-earthquake state of buildings based on digital twins according to claim 1, characterized in that, The basic information of the target building includes BIM model, design drawings, and material testing reports; based on the basic information, the key vulnerable components of the target building are identified; a multi-source sensor network is deployed on the key vulnerable components, and the multi-source sensor network includes accelerometers, strain gauges, and displacement gauges.
5. The method for simulating the post-earthquake state of a building based on digital twins according to claim 1, characterized in that, The data assimilation algorithm includes extended Kalman filtering, unscented Kalman filtering, or a deep learning-based surrogate model correction algorithm.
6. The method for simulating the post-earthquake state of a building based on digital twins according to claim 1, characterized in that, By minimizing the residual between the simulated and measured values, the model parameters in the initial digital twin are corrected in real time, including material stiffness degradation parameters, damping ratio, boundary connection state, and local damage factor, thereby realizing online updating of the digital twin and obtaining an updated digital twin that tends to be consistent with the actual structural state.
7. The method for simulating the post-earthquake state of buildings based on digital twins according to claim 1, characterized in that, First, outlier removal and spatiotemporal matching preprocessing are performed on the structural response data. Then, the preprocessed structural response data is assimilated and fused with the simulation results to ensure data consistency.
8. The method for simulating the post-earthquake state of buildings based on digital twins according to claim 1, characterized in that, The post-earthquake structural damage indicators include inter-story drift angle, degree of plastic hinge development, component damage index, overall structural ductility ratio, location of weak story, and degree of damage concentration.
9. A method for simulating the post-earthquake state of a building based on digital twins according to claim 1, characterized in that, This also includes storing the entire process data of this earthquake event, model update records, and evaluation results into a historical case database after the post-earthquake simulation assessment is completed. This data will be used to train machine learning models and improve the prediction of post-earthquake response and the initialization level of digital twins for similar buildings.
10. A method for simulating the post-earthquake state of a building based on digital twins according to claim 1, characterized in that, The 3D visualization platform is built using WebGL or Unity3D engines and supports integration with GIS systems to achieve synchronized display of the post-earthquake status of building complex-level digital twins.