Existing building anti-seismic performance dynamic evaluation method and system based on digital twinning

Abstract

The invention discloses an existing building anti-seismic performance dynamic evaluation method and system based on digital twinning, and relates to the technical field of building anti-seismic evaluation. Comprising the steps that building material data and existing building data are collected, preprocessing is carried out based on the collected existing building data, the collected building data are adjusted based on a main body part and a secondary part, and a digital model twin with an existing building is constructed based on the building material data and the adjusted building data; and model verification: comparing the constructed existing building model with existing acquisition parameters, simulating an earthquake to test the existing building model, presetting anti-seismic damage data, summarizing anti-seismic data and comparing the summarized anti-seismic data with the anti-seismic damage data. According to the method, the existing building is divided into the main body part and the secondary part, meanwhile, the sampling frequency and precision of the main body part and the secondary part are adjusted through an algorithm, the model complexity is reduced while the model precision is kept, and the requirement for computing resources is lowered.

Description

Technical Field

[0001] The present invention relates to the technical field of building seismic assessment, and specifically to a method and system for dynamically assessing the seismic performance of existing buildings based on digital twins. Background Art

[0002] The existing digital twin dynamic assessment of the seismic performance of existing buildings is achieved by creating an accurate virtual model of the building and combining it with sensor data installed in the actual building to monitor and simulate the impact of earthquakes on the building in real time and evaluate its seismic performance. It can not only predict the performance of the building in an earthquake, but also provide optimization suggestions based on the analysis results, helping to improve the safety and stability of the building and ensure that damage is reduced when a disaster occurs.

[0003] The application publication number CN110472350A is a method for assessing earthquake damage based on virtual simulation. With the help of a virtual simulation system, by collecting building samples with different structural parameters in multiple regions, the building samples are simplified and abstracted into a simple diagram of the building structure, and a damage matrix of the building sample database is established. The relationship between the earthquake intensity and the average damage index is curve-fitted to obtain the relationship between the earthquake intensity and the damage index of building samples with different structural forms. By randomly combining and matching the various structural parameters of the building samples and different earthquake intensities, the virtual simulation system is finally used to simulate the damage of the building samples under different structural parameters and different earthquake intensities. The method for assessing earthquake damage based on virtual simulation of the present invention can virtually simulate the damage of buildings with different structural parameters under different earthquake intensities, so that people can understand the damage process of earthquakes on buildings and the seismic performance of buildings with different structures.

[0004] The existing dynamic assessment of the seismic performance of existing buildings requires the construction of a complex mathematical model that can reflect the interaction between the micro and macro scales in cross-scale modeling, which increases the complexity and construction difficulty of the model. As the model scale expands and the accuracy improves, the demand for computing resources also grows exponentially, resulting in a large demand for computing resources in digital twin technology. In the simulation process, it is necessary to consider the impact of changes in material properties at the micro scale on structural performance at the macro scale, as well as the feedback effect of structural deformation at the macro scale on material properties at the micro scale, further increasing the demand for computing resources. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for dynamically evaluating the seismic performance of existing buildings based on digital twins, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method and system for dynamically evaluating the seismic performance of existing buildings based on digital twins, the method comprising:

[0007] Data collection, collecting building material data and existing building data, verifying and summarizing the building material data and existing building data, and judging the influence on the structural performance of the existing building according to the change of building material properties;

[0008] Part identification, preprocessing based on the collected existing building data, dividing the building into main parts and secondary parts, which can optimize the data processing efficiency and improve the accuracy of building condition assessment;

[0009] Algorithm adjustment, based on the main parts and secondary parts, adjusting the collected building data, efficiently screening out the data of the main parts, reducing the processing requirements for the data of the secondary parts, and effectively reducing the consumption of computing resources;

[0010] Parametric modeling, constructing a digital model twin to the existing building based on the building material data and the adjusted building data;

[0011] Model verification, presetting a difference value, comparing the constructed existing building model with the existing collected parameters, if it exceeds the preset difference value, calibrating the existing building model according to the difference data, enhancing the accuracy of the existing building model;

[0012] Seismic test, presetting seismic parameters, simulating an earthquake to test the existing building model, collecting the damage data of the building model, directly observing and recording the changes of the existing building model under the action of the earthquake, providing a basis for evaluating the seismic performance of the existing building model;

[0013] Evaluation feedback, presetting seismic damage data, based on the damage data of the existing building model, summarizing the seismic data and comparing it with the seismic damage data, if it exceeds the seismic damage data, feedback calibrating the building model according to the seismic damage data, being able to accurately identify the weak structures in the damage data of the existing building model, providing a basis for the maintenance and reinforcement of the existing building, and improving the seismic performance and safety of the existing building.

[0014] For the data collection, testing the building materials of the existing building to obtain the chemical properties and physical properties of the building materials, collecting the geometric dimensions, material properties, internal layout and performance parameters of the existing building, ensuring the accuracy and reliability of the subsequent data twin modeling.

[0015] For the part identification, presetting the seismic importance score, preprocessing based on the collected building data, and dividing the building into main parts and secondary parts according to the seismic capacity of different parts of the building, and the division formula is as follows:

[0016]

[0017] Among them, S i represents the comprehensive seismic importance score of building part i, w j represents the weight of the j-th seismic evaluation index, A ij represents the original score of building part i on the j-th seismic evaluation index, A jmin and Ajmax respectively represent the minimum score and the maximum score of all building parts on the j-th seismic evaluation index, C ij represents the correction coefficient of building part i on the j-th seismic evaluation index, P i represents the potential seismic risk coefficient of building part i, D i represents the seismic demand coefficient of building part i;

[0018] The seismic importance score of the corresponding part of the building is calculated, and the seismic importance score is closed with the preset seismic importance score. The area lower than the preset seismic importance score is the secondary part, and the area equal to or greater than the preset seismic importance score is the main part.

[0019] Based on the main part and the secondary part, the algorithm adjusts the collected building data. The sampling frequency formula for the main part is as follows:

[0020]

[0021] Among them, F i is the adjusted sampling frequency of main part i, α is the seismic performance importance coefficient of main part i, F b is the high-density sampling frequency of the main part foundation, F r is the frequency adjustment factor calculated from the physical parameters of main part i;

[0022] The sampling frequency formula for the secondary part is as follows:

[0023]

[0024] Among them, F j is the adjusted sampling frequency of secondary part j, β j is the seismic performance importance coefficient of secondary part j, F b is the low-density sampling frequency of the secondary part foundation, F r is the frequency reduction factor calculated from the physical parameters of secondary part j.

[0025] Based on the main part and the secondary part, the algorithm adjusts the collected building data. The sampling accuracy formula for the main part is as follows:

[0026]

[0027] Among them, A j is the adjusted sampling precision of the main body part i, A b is the basic high-precision sampling precision of the main body part, A c is the precision adjustment factor calculated from the physical parameters of the main body part i;

[0028] The sampling frequency formula for the secondary part is as follows:

[0029]

[0030] Among them, Aj is the adjusted sampling precision of the secondary part j, Aa is the basic low-precision sampling precision of the secondary part, and As is the precision reduction factor calculated from the physical parameters of the secondary part j.

[0031] For the model verification, a preset difference value is set. The constructed building model is compared with the existing acquisition parameters. If it exceeds the preset difference value, the building model is calibrated according to the difference data. The calibration formula is as follows:

[0032]

[0033] When D k >T k , the k-th round of model calibration is performed;

[0034] Among them, D k is the comprehensive difference index of the k-th round of verification, w i is the weight of the i-th parameter, M ki is the predicted value of the building model for the i-th parameter in the k-th round of verification, P ki is the value of the i-th parameter obtained through actual acquisition in the k-th round of verification, R ki is the reference range or allowable error of the i-th parameter in the k-th round of verification, T k is the preset difference threshold for the k-th round of verification. When the actual difference D k exceeds this threshold, the k-th round of model calibration process will be triggered.

[0035] For the evaluation feedback, preset seismic damage data. Based on the damage data of the building model, the seismic data is summarized and compared with the seismic damage data. The judgment formula is as follows:

[0036]

[0037] When E>T, it means that the seismic performance of the building model does not meet the preset requirements and further optimization or adjustment is needed;

[0038] Among them, E is the seismic performance evaluation error, m is the number of evaluation indicators, D Mj is the damage data of the building model, DPj For preset seismic damage data, max(D Mj , D Pj ) is to take the larger value of D Mj and D Pj . w j is the weight of the j-th damage index, and 1 / m∑ m j=1 is the mean calculation. T is the preset evaluation error threshold. When the actual evaluation error E exceeds this threshold, it indicates that the seismic performance of the building model does not meet the preset requirements.

[0039] For the above-mentioned evaluation feedback, preset seismic damage data, based on the damage data of the building model, summarize the seismic data and compare it with the seismic damage data. The feedback calibration formula is as follows:

[0040]

[0041] When AE > T, it indicates that the seismic performance of the building model does not meet the expectation and needs to be further optimized;

[0042] Among them, AE is the seismic evaluation error, D Mi is the seismic data of the model, D Pi is the preset seismic damage data, Ri is the range of the evaluation index, ((D Mi −D Pi ) / R i ) 2 is the squared difference term, (1−min(D Mi , D Pi ) / R i ) is the adjustment factor, w i is the weight of the evaluation index, and ∑ n i=1 is the weighted average calculation to ensure that the evaluation error takes into account the weights of each evaluation index. T is the evaluation error threshold.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] This dynamic evaluation method and system for the seismic performance of existing buildings based on digital twins divides the existing buildings into main parts and secondary parts through part identification and algorithm adjustment. At the same time, by adjusting the sampling frequency and accuracy of the main parts and secondary parts through algorithms, on the basis of ensuring the evaluation accuracy of the main parts, the secondary parts of the digital twin model of the existing building are simplified, reducing the model complexity while maintaining the model accuracy, thereby reducing the demand for computing resources, optimizing the data processing efficiency, and taking into account the accuracy of the existing building state evaluation.

[0045] When building a model of an existing building based on digital twins, through model verification, seismic testing, and evaluation feedback, the existing building model is calibrated according to the differential data, enhancing the accuracy of the existing building model. At the same time, based on the seismic test data, the existing building model is feedback-calibrated to identify the weak structures of the existing building model, providing a basis for the maintenance and reinforcement of the existing building, and improving the seismic performance and safety of the existing building. Description of the Drawings

[0046] Figure 1 Schematic diagram of the principle structure of the present invention;

[0047] Figure 2 Schematic diagram of the principle structure of the part differentiation in the present invention;

[0048] Figure 3 Schematic diagram of the principle structure of the sampling adjustment in the present invention;

[0049] Figure 4 Schematic diagram of the model verification structure of the present invention;

[0050] Figure 5 Schematic diagram of the principle structure of the seismic test of the present invention. Detailed Implementation Modes

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] In this application, for the convenience of understanding, the method steps used do not necessarily need to be executed in the order of the steps in this embodiment during actual operation. In some other embodiments, these steps can be carried out simultaneously or in a changed order.

[0053] As Figures 1 - 5 shown, the present invention provides a technical solution: a method and system for dynamically evaluating the seismic performance of an existing building based on digital twins, and the method includes:

[0054] Data collection: Collect building material data and existing building data, verify and summarize the building material data and existing building data, and judge the impact on the structural performance of the existing building according to the changes in the properties of the building materials;

[0055] Part identification: Preprocess the collected existing building data, divide the building into main parts and secondary parts, which can optimize the data processing efficiency and improve the accuracy of the building state evaluation;

[0056] Algorithm adjustment: Based on the main parts and secondary parts, adjust the collected building data, efficiently screen out the data of the main parts, reduce the processing requirements for the data of the secondary parts, and effectively reduce the consumption of computing resources;

[0057] Parametric modeling: Based on the building material data and the adjusted building data, construct a digital model that is twin to the existing building;

[0058] Model verification: Preset a difference value, compare the constructed existing building model with the existing collected parameters. If it exceeds the preset difference value, calibrate the existing building model according to the difference data, enhancing the accuracy of the existing building model;

[0059] Seismic test: Preset seismic parameters, simulate an earthquake to test the existing building model, collect the damage data of the building model, directly observe and record the changes of the existing building model under the action of the earthquake, providing a basis for evaluating the seismic performance of the existing building model;

[0060] Evaluation and feedback: Preset seismic damage data, based on the damage data of the existing building model, summarize the seismic data and compare it with the seismic damage data. If it exceeds the seismic damage data, calibrate the building model according to the seismic damage data, accurately identifying the weak structures in the damage data of the existing building model, providing a basis for the maintenance and reinforcement of the existing building, and improving the seismic performance and safety of the existing building.

[0061] The data collection: Test the building materials of the existing building to obtain the chemical and physical properties of the building materials, collect the geometric dimensions, material properties, internal layout and performance parameters of the existing building, ensuring the accuracy and reliability of the subsequent data twin modeling.

[0062] The part identification: Preset the seismic importance score, preprocess the collected building data, and divide the building into main parts and secondary parts according to the seismic capacity of different parts of the building. The division formula is as follows:

[0063]

[0064] where S i represents the comprehensive seismic importance score of building part i. The higher this score, the more critical the seismic performance of this part, and the greater the impact on the overall structure. w j represents the weight of the j-th seismic evaluation index. The weight value is between 0 and 1, and the sum of all weights should be 1. The size of the weight reflects the relative importance of each index in the overall seismic evaluation. A ij represents the original score of building part i on the j-th seismic evaluation index. These scores are based on the actual collected building data, such as material strength, structural dimensions, connection methods, etc. A jminAjmin and Ajmax represent the minimum score and the maximum score of all building parts on the j-th seismic evaluation index, respectively, which are used to standardize the original score to the range of 0 to 1 for easy comparison and analysis, C ij represents the correction coefficient of building part i on the j-th seismic evaluation index, which is used to consider the influence of the specific conditions or characteristics of building part i on the j-th index. For example, for parts with irregular structures, the correction coefficient of their structural stability index can be adjusted appropriately, P i represents the potential seismic risk coefficient of building part i, which reflects the possible special risks or weaknesses of part i, such as material aging, unreliable connections, etc., D i represents the seismic demand coefficient of building part i, which reflects the degree of seismic demand of part i in the overall structure. For example, for parts located on the critical path of the structure, their seismic demand coefficients may be higher;

[0065] Calculate the seismic importance scores of the corresponding parts of the building, and close the seismic importance scores with the preset seismic importance scores. The areas with scores lower than the preset seismic importance scores are secondary parts, and the areas equal to or higher than the preset seismic importance scores are main parts.

[0066] Input the data,

[0067] Weight wj:

[0068] w1 = 0.4;

[0069] w2 = 0.3;

[0070] w3 = 0.3;

[0071] Minimum score A jmin and maximum score A jmax :

[0072] A1min = 60, A1max = 90;

[0073] A2min = 70, A2max = 80;

[0074] A3min = 70, A3max = 90;

[0075] Correction coefficient C ij :

[0076] The preset correction coefficients are all 1 (i.e., the influence of specific conditions or characteristics is not considered)

[0077] Potential seismic risk coefficient Pi:

[0078] P1 = 0.2;

[0079] P2 = 0.1;

[0080] Seismic demand coefficient Di:

[0081] D1 = 1.2;

[0082] D2 = 0.8;

[0083] Calculate the comprehensive seismic importance score Si for each part.

[0084] Comprehensive seismic importance score S1 for part 1:

[0085] For j = 1:

[0086] (A1max - A1min) / (A11 - A1min) = (90 - 60) / (80 - 60) = 1.5;

[0087] w1×(1.5)×C11 - P1×D1 = 0.4×1.5×1 - 0.2×1.2 = 0.6 - 0.24 = 0.36;

[0088] For j = 2:

[0089] (A2max - A2min) / (A12 - A2min) = 70 - 7080 - 70 = undefined (but here we assume the denominator is not zero, and this situation needs to be handled in practice);

[0090] Preset A12 to deviate slightly from A2min, for example, A12 = 71, then:

[0091] (A2max - A2min) / (A12 - A2min) = (80 - 70) / (71 - 70) = 10;

[0092] w2×(10)×C12 - P1×D1 = 0.3×10×1 - 0.2×1.2 = 3 - 0.24 = 2.76;

[0093] For j = 3:

[0094] (A3max - A3min) / (A13 - A3min) = (90 - 70) / (90 - 70) = 1;

[0095] w3×(1)×C13 - P1×D1 = 0.3×1×1 - 0.2×1.2 = 0.3 - 0.24 = 0.06;

[0096] Therefore, the total score for part 1:

[0097] S1 = 0.36 + 2.76 + 0.06 = 3.18;

[0098] Comprehensive seismic importance score S2 for part 2 (calculated similarly):

[0099] For j = 1:

[0100] (A1max - A1min) / (A21 - A1min) = (90 - 60) / (60 - 60) = undefined (handle this case);

[0101] Preset A21 to deviate slightly from A1min, for example, A21 = 61, then:

[0102] (A1max - A1min) / (A21 - A1min) = (90 - 60) / (61 - 60) = 30;

[0103] w1×(30)×C21 - P2×D2 = 0.4×30×1 - 0.1×0.8 = 12 - 0.08 = 11.92;

[0104] For j = 2:

[0105] (A2max - A2min) / (A22 - A2min) = (80 - 70) / (80 - 70) = 1;

[0106] w2×(1)×C22 - P2×D2 = 0.3×1×1 - 0.1×0.8 = 0.3 - 0.08 = 0.22;

[0107] For j = 3:

[0108] (A3max - A3min) / (A23 - A3min) = (90 - 70) / (70 - 70) = undefined (handle this case);

[0109] Preset A23 to deviate slightly from A3min, for example, A23 = 71, then:

[0110] (A3max - A3min) / (A23 - A3min) = (90 - 70) / (71 - 70) = 20;

[0111] w3×(20)×C23 - P2×D2 = 0.3×20×1 - 0.1×0.8 = 6 - 0.08 = 5.92;

[0112] Therefore, the total score of part 2:

[0113] S2 = 11.92 + 0.22 + 5.92 = 18.06;

[0114] Conclusion: According to the calculation, the comprehensive seismic importance score of part 2 (S2 = 18.06) is significantly higher than that of part 1 (S1 = 3.18). Therefore, part 2 is regarded as the main part, while part 1 is regarded as the secondary part.

[0115] The algorithm adjustment is based on the main part and the secondary part to adjust the collected building data. The sampling frequency formula for the main part is as follows:

[0116]

[0117] Among them, F i is the adjusted sampling frequency of the main part i, α is the seismic performance importance coefficient of the main part i, which is used to adjust its weight in the overall evaluation, F b is the basic high-density sampling frequency of the main part, that is, the sampling frequency before adjustment, F r is the frequency adjustment factor calculated from the physical parameters of the main part i;

[0118] The sampling frequency formula for the secondary part is as follows:

[0119]

[0120] Among them, F j is the adjusted sampling frequency of the secondary part j, β j is the seismic performance importance coefficient of the secondary part j, which is used to adjust its weight in the overall evaluation, usually less than the coefficient of the main part, F b is the basic low-density sampling frequency of the secondary part, that is, the sampling frequency before adjustment, F r is the frequency reduction factor calculated from the physical parameters of the secondary part j.

[0121] The algorithm adjustment is based on the main part and the secondary part to adjust the collected building data. The sampling accuracy formula for the main part is as follows:

[0122]

[0123] Among them, A j is the adjusted sampling accuracy of the main part i, A b is the basic high-precision sampling accuracy of the main part, that is, the sampling accuracy before adjustment, A c is the accuracy adjustment factor calculated from the physical parameters of the main part i;

[0124] The sampling frequency formula for the secondary part is as follows:

[0125] ;

[0126] Among them, Aj is the secondary part j The adjusted sampling accuracy, Aa is the basic low-precision sampling accuracy of the secondary part, that is, the sampling accuracy before adjustment, As is the secondary partj Precision reduction factor for physical parameter calculation.

[0127] For the model verification, a preset difference value is set. The constructed building model is compared with the existing collected parameters. If the difference exceeds the preset difference value, the building model is calibrated according to the difference data. The calibration formula is as follows:

[0128] ;

[0129] When D k > T k , the k th round of model calibration is performed;

[0130] Wherein, D k is the comprehensive difference index of the k th round of verification, indicating the comprehensive difference degree between the constructed building model and the existing collected parameters in the k th round of verification, w i is the weight of the i th parameter, reflecting the importance of this parameter in the overall difference evaluation, M ki is the predicted value of the building model on the k th parameter in the i th round of verification, P ki is the value of the k th parameter obtained through actual collection in the i th round of verification, R ki is the reference range or allowable error of the k th parameter in the i th round of verification, used to standardize the difference value to make it comparable between different parameters, T k is the preset difference threshold for the k th round of verification. When the actual difference D k exceeds this threshold, the k th round of model calibration process will be triggered.

[0131] A building model is preset, and there are 3 parameters to be verified ( n = 3), namely parameter A , parameter B and parameter C . The weights of these parameters are 0.5, 0.3, and 0.2 respectively (satisfying that the sum of all weights is 1). In the first round of verification, we obtained the following data:

[0132] Parameter A Model predicted value of M 1 A = 80, actual collected value P 1 A = 78, reference range or allowable error R 1 A = 5;

[0133] Parameter B Model predicted value of M 1 B = 60, actual collected value P 1 B = 65, reference range or allowable error R 1 B = 10;

[0134] Parameter C Model predicted value of M 1 C = 90, actual collected value P 1 C = 88, reference range or allowable error R 1 C = 5;

[0135] Preset difference threshold T 1 = 1;

[0136] Difference calculation:

[0137] Parameter A Difference of: ([[]] M 1 A − P 1 A ) / R 1 A = (80 - 78) / 5 = 0.4;

[0138] Parameter B Difference of: ([[]] M 1 B − P 1 B ) / R 1 B = (60 - 65) / 10 = -0.5;

[0139] Parameter C Difference of: ([[]] M 1 C − P 1 C ) / R 1 C = (90 - 88) / 5 = 0.4;

[0140] Comprehensive difference indexD 1:

[0141] ;

[0142] ≈0.432;

[0143] Difference judgment:

[0144] Compare D 1 and T 1, that is, 0.432 < 1, the correct judgment should be D 1 ≤ T 1, indicating that the difference in the first-round verification of the model is within the acceptable range and no calibration is required.

[0145] For the above evaluation feedback, preset seismic damage data. Based on the damage data of the building model, summarize the seismic data and compare it with the seismic damage data. The judgment formula is as follows:

[0146] ;

[0147] When E > T it indicates that the seismic performance of the building model does not meet the preset requirements and further optimization or adjustment is required;

[0148] Among them, E is the seismic performance evaluation error, indicating the comprehensive difference degree between the damage data of the building model under simulated or actual earthquake actions and the preset seismic damage data, m is the number of evaluation indicators, indicating the total number of damage indicators participating in the seismic performance evaluation, D Mj is the damage data of the building model, the damage value of the building model under simulated or actual earthquake actions, D Pj is the preset seismic damage data, indicating the preset damage value according to the seismic design code or experience on the j th damage indicator, max ( D Mj ，D Pj ) is to take the D Mj and D Pj the larger value of, for standardizing the difference value to ensure that the difference calculation will not be exaggerated due to too small a denominator, w j is the weight of the j th damage indicator, reflecting the importance of this indicator in the overall seismic performance evaluation, 1 / m ∑ m j=1For mean calculation, it is used to calculate the weighted average of the difference values of all damage indicators. T Is the preset evaluation error threshold. When the actual evaluation error E Exceeds this threshold, it indicates that the seismic performance of the building model does not meet the preset requirements.

[0149] Input the data.

[0150] Number of evaluation indicators ( m ): 3;

[0151] Damage indicator weights ( wj ):

[0152] w 1 = 0.4 (structural deformation);

[0153] w 2 = 0.3 (non-structural component damage);

[0154] w 3 = 0.3 (personnel safety);

[0155] Preset seismic damage data ( DPj ):

[0156] DP 1 = 0.15 (preset damage value of structural deformation);

[0157] DP 2 = 0.10 (preset damage value of non-structural component damage);

[0158] DP 3 = 0.05 (preset damage value of personnel safety, assumed to be a small value of a certain safety index);

[0159] Building model damage data ( DMj )(obtained through simulated earthquake action):

[0160] DM 1 = 0.20 (actual damage value of structural deformation);

[0161] DM 2 = 0.08 (actual damage value of non-structural component damage);

[0162] DM 3 = 0.06 (actual damage value of personnel safety);

[0163] Preset evaluation error threshold ( T ): 0.10;

[0164] ,

[0165] E = 0.4×(|0.20 - 0.15| / 0.20) + 0.3×(|0.08 - 0.10| / 0.10) + 0.3×(|0.06 - 0.05| / 0.06);

[0166] E = 0.4×0.25 + 0.3×0.20 + 0.3×0.17;

[0167] ≈0.187;

[0168] Since E = 0.187 > T = 0.10, according to the evaluation criteria, the seismic performance of the building model does not meet the preset requirements. Therefore, further optimization or adjustment is needed.

[0169] For the above evaluation feedback, the preset seismic damage data, based on the damage data of the building model, summarizes the seismic data and compares it with the seismic damage data. The feedback calibration formula is as follows:

[0170]

[0171] When AE > T, it indicates that the seismic performance of the building model does not meet the expectation and further optimization is required;

[0172] Among them, AE is the seismic evaluation error, comprehensively measuring the difference between the seismic data of the building model and the preset seismic damage data. D Mi is the seismic data of the model, representing the seismic performance value of the building model on the i-th evaluation index. D Pi is the preset seismic damage data, which is the damage threshold of the i-th evaluation index preset according to the seismic design code or experience. Ri is the range of the evaluation index, representing the acceptable variation range of the i-th evaluation index for standardizing the difference calculation. ((D Mi - D Pi ) / R i ) 2 is the squared difference term, reflecting the relative difference size between the seismic data of the model and the preset damage data. (1 - min(D Mi , D Pi ) / R i ) is the adjustment factor. w i is the weight of the evaluation index, reflecting the importance of the i-th evaluation index in the overall seismic evaluation. ∑ n i=1 is the weighted average calculation to ensure that the evaluation error takes into account the weights of each evaluation index. T is the evaluation error threshold, representing the maximum allowable seismic evaluation error.

[0173] Substitute the data,

[0174] Number of evaluation indicators (n): 3;

[0175] Evaluation index weight (wi): 0.4 (structural deformation), 0.3 (non-structural component damage), 0.3 (personnel safety);

[0176] Preset seismic damage data (DPi): 0.15 (structural deformation), 0.10 (non-structural component damage), 0.05 (personnel safety);

[0177] Model seismic data (DMi): 0.20 (structural deformation), 0.08 (non-structural component damage), 0.06 (personnel safety);

[0178] Evaluation index range (Ri): 0.20 (structural deformation), 0.15 (non-structural component damage), 0.10 (personnel safety);

[0179] Adjustment coefficient (λ): 0.5 (assumed value, used in adjustment factor);

[0180] Evaluation error threshold (T): 0.15;

[0181] Now, we calculate according to the assumed data:

[0182] AE = 0.4×(|0.20 - 0.15| / 0.20)×(1 - min(0.20, 0.15) / 0.20×0.5)+…+0.3×(|0.08 - 0.10| / 0.15)×(1 - min(0.08, 0.10) / 0.15×0.5)+0.3×(|0.06 - 0.05| / 0.10)×(1 - min(0.06, 0.05) / 0.10×0.5);

[0183] Calculate each item:

[0184] Structural deformation:

[0185] (|0.20 - 0.15| / 0.20)×(1 - 0.15 / 0.2×0.5);

[0186] = 0.25×0.75;

[0187] = 0.1875;

[0188] Non-structural component damage:

[0189] (|0.08 - 0.10| / 0.15)×(1 - 0.08 / 0.15×0.5);

[0190] ≈ 0.1333×0.6;

[0191] = 0.08;

[0192] Personnel safety:

[0193] (|0.06 - 0.05| / 0.10) × (1 - 0.05 / 0.10 × 0.5);

[0194] = 0.1 × 0.75;

[0195] = 0.075;

[0196] Weighted summation:

[0197] AE = 0.4 × 0.1875 + 0.3 × 0.08 + 0.3 × 0.075;

[0198] = 0.075 + 0.024 + 0.0225;

[0199] = 0.1215;

[0200] Since AE = 0.1215 < T = 0.15, according to the adjusted seismic evaluation error formula, the seismic performance of the building model meets the preset requirements.

[0201] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made therein without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic evaluation method for seismic performance of existing buildings based on digital twins, characterized by: The method comprises: Data collection: collect building material data and existing building data, verify and summarize the building material data and existing building data, and determine the impact of changes in the properties of building materials on the performance of existing building structures; Part identification, based on the pre-processing of the collected existing building data, divides the building into main parts and secondary parts, which can optimize data processing efficiency and improve the accuracy of building status assessment; Algorithm adjustment: Based on the main parts and secondary parts, the collected building data is adjusted to efficiently filter out the main part data, reduce the processing requirements for the secondary part data, and effectively reduce the consumption of computing resources; Parametric modeling, building a digital model that is a twin of the existing building based on building material data and adjusted building data; Model verification, preset difference value, compare the constructed existing building model with the existing acquisition parameters, and calibrate the existing building model according to the difference data if it exceeds the preset difference value, thus enhancing the accuracy of the existing building model; Seismic testing: preset earthquake parameters, simulate earthquakes to test existing building models, collect damage data of building models, directly observe and record the changes of existing building models under earthquakes, and provide a basis for evaluating the seismic performance of existing building models; Evaluation feedback, preset seismic damage data, based on the damage data of existing building models, summarize the seismic data and compare it with the seismic damage data, exceed the seismic damage data, and calibrate the building model based on the seismic damage data. It can accurately identify the weak structures in the damage data of existing building models, provide a basis for the maintenance and reinforcement of existing buildings, and improve the seismic performance and safety of existing buildings.

2. The method for dynamic assessment of seismic performance of existing buildings based on digital twins according to claim 1 is characterized in that: The data collection tests the building materials of existing buildings to obtain the chemical and physical properties of the building materials, and collects the geometric dimensions, material properties, internal layout and performance parameters of existing buildings to ensure the accuracy and reliability of subsequent data twin modeling.

3. The method for dynamic evaluation of seismic performance of existing buildings based on digital twins according to claim 1 is characterized in that: The part identification presets the seismic importance score, performs preprocessing based on the collected building data, and divides the building into main parts and secondary parts according to the seismic resistance of different parts of the building. The classification formula is as follows: ； Among them, S i represents the comprehensive score of seismic importance of building part i, w j represents the weight of the jth seismic assessment index, A ij A represents the original score of building part i on the jth seismic assessment index, jmin and Ajmax represent the minimum and maximum scores of all building parts on the jth seismic assessment index, respectively. ij P represents the correction coefficient of the jth seismic assessment index of the building part i, i represents the potential seismic risk factor of building part i, D i represents the seismic demand coefficient of building part i; The seismic importance score of the corresponding part of the building is calculated and closed with the preset seismic importance score. The area with a seismic importance score lower than the preset seismic importance score is the secondary part, and the area with a score equal to or greater than the preset seismic importance score is the main part.

4. The method for dynamic assessment of seismic performance of existing buildings based on digital twins according to claim 1 is characterized in that: The algorithm adjustment is based on the main part and the secondary part to adjust the collected building data. The formula for the sampling frequency of the main part is as follows: ； Among them, F i is the adjusted sampling frequency of the main part i, α is the importance coefficient of the seismic performance of the main part i, F b is the basic high-density sampling frequency of the main body, F r A frequency adjustment factor calculated for the physical parameters of body part i; The formula for the sampling frequency of the secondary parts is as follows: ； Among them, F j is the adjusted sampling frequency of the secondary part j, β j is the seismic performance importance factor of the secondary part j, F b is the basic low-density sampling frequency of the secondary parts, F r Frequency reduction factor calculated for the physical parameters of secondary site j.

5. The method for dynamic assessment of seismic performance of existing buildings based on digital twins according to claim 1 is characterized in that: The algorithm adjustment is based on the main part and the secondary part to adjust the collected building data. The sampling accuracy formula of the main part is as follows: ； Among them, A j is the adjusted sampling accuracy of body part i, A b A is the basic high-precision sampling accuracy of the main body. c is the precision adjustment factor for the calculation of the physical parameters of body part i; The formula for the sampling frequency of the secondary parts is as follows: ； Among them, Aj is the adjusted sampling accuracy of the secondary part j, Aa is the basic low-precision sampling accuracy of the secondary part, and As is the accuracy reduction factor of the physical parameter calculation of the secondary part j.

6. The method for dynamic assessment of seismic performance of existing buildings based on digital twins according to claim 1 is characterized in that: The model verification presets a difference value, compares the constructed building model with the existing acquisition parameters, and if the difference exceeds the preset difference value, calibrates the building model according to the difference data. The calibration formula is as follows: ； When D k >T k When , the kth round of model calibration is performed; Among them, D k is the comprehensive index of differences in the k-th round of verification, w i is the weight of the i-th parameter, M ki is the predicted value of the building model on the i-th parameter in the k-th round of validation, P ki is the value of the i-th parameter obtained through actual collection in the k-th round of verification, R ki is the reference range or allowable error of the i-th parameter in the k-th round of verification, T k is the preset difference threshold for the kth round of verification. When the actual difference D k When this threshold is exceeded, the k-th round of model calibration process will be triggered.

7. The method for dynamic assessment of seismic performance of existing buildings based on digital twins according to claim 1 is characterized in that: The evaluation feedback is based on the preset earthquake damage data. Based on the building model damage data, the earthquake resistance data is summarized and compared with the earthquake damage data. The judgment formula is as follows: ； When E>T, it means that the seismic performance of the building model does not meet the preset requirements and needs further optimization or adjustment; Among them, E is the seismic performance evaluation error, m is the number of evaluation indicators, and D Mj is the damage data of the building model, D Pj is the preset earthquake damage data, max(D Mj , D Pj ) is to take D Mj and D Pj The larger value of w j is the weight of the jth damage index, 1 / m∑ m j=1 is the mean calculation, T is the preset evaluation error threshold, when the actual evaluation error E exceeds this threshold, it means that the seismic performance of the building model does not meet the preset requirements.

8. The method for dynamic assessment of seismic performance of existing buildings based on digital twins according to claim 1 is characterized in that: The evaluation feedback presets seismic damage data, based on the building model damage data, summarizes the seismic data and compares it with the seismic damage data. The feedback calibration formula is as follows: ； When AE>T, it indicates that the seismic performance of the building model does not meet expectations and needs further optimization; Among them, AE is the seismic assessment error, D Mi is the seismic data of the model, D Pi is the preset earthquake damage data, Ri is the range of the evaluation index, ((D Mi −D Pi ) / R i ) 2 is the squared difference term, (1−min(D Mi , D Pi ) / R i ) is the adjustment factor, w i is the weight of the evaluation index, ∑ n i=1 It is a weighted average calculation to ensure that the evaluation error takes into account the weight of each evaluation indicator, and T is the evaluation error threshold.

9. The digital twin-based dynamic assessment system for seismic performance of existing buildings according to claim 1 is characterized by: The method for dynamically evaluating the seismic performance of existing buildings based on digital twins is used as described in any one of claims 1 to 8.

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