A dynamic evaluation method and system for seismic performance of existing buildings based on digital twins

Abstract

The present invention discloses a method and system for dynamically evaluating the seismic performance of existing buildings based on digital twins, and relates to the technical field of building seismic evaluation. The method includes collecting building material data and existing building data, pre-processing based on the collected existing building data, adjusting the collected building data based on the main part and the secondary part, and constructing a digital model that is a twin of the existing building based on the building material data and the adjusted building data; model verification, comparing the constructed existing building model with the existing collection parameters, testing the existing building model with a simulated earthquake, presetting seismic damage data, and summarizing the seismic data for comparison with the seismic damage data. The present invention divides the existing building area into the main part and the secondary part. At the same time, the sampling frequency and accuracy of the main part and the secondary part are adjusted through an algorithm, thereby reducing the model complexity while maintaining the model accuracy and reducing the demand for computing resources.

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 to help 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. By means of a virtual simulation system, building samples with different structural parameters in multiple regions are collected, the building samples are simplified and abstracted into building structural diagrams, and a damage matrix of the building sample database is established. The relationship between earthquake intensity and average damage index is curve-fitted to obtain the relationship between earthquake intensity and 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 damage of the building samples under different structural parameters and different earthquake intensities is finally simulated in the virtual simulation system. 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, thereby allowing people to understand the destructive 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 complex mathematical models 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 for 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, which further increases the demand for computing resources. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for dynamic evaluation of the seismic performance of existing buildings based on digital twins to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method and system for dynamic evaluation of seismic performance of existing buildings based on digital twins, the method comprising:

[0007] 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 building material properties on the performance of existing building structures;

[0008] Part identification: pre-processing the collected existing building data to divide the building into main parts and secondary parts, which can optimize data processing efficiency and improve the accuracy of building status assessment;

[0009] 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;

[0010] Parametric modeling: building a digital model that is a twin of the existing building based on building material data and adjusted architectural data;

[0011] Model verification: preset difference values ​​are used to compare the constructed existing building model with the existing acquisition parameters. If the difference exceeds the preset value, the existing building model is calibrated based on the difference data, thus enhancing the accuracy of the existing building model.

[0012] Seismic testing: pre-set earthquake parameters, simulate earthquakes to test existing building models, collect damage data on building models, directly observe and record changes in existing building models under earthquake action, and provide a basis for evaluating the seismic performance of existing building models;

[0013] 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.

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

[0015] The part identification presets the seismic importance score and performs pre-processing based on the collected building data. The building is divided into main parts and secondary parts according to the seismic resistance of different parts of the building. The classification formula is as follows:

[0016]

[0017] Among them, S i represents the comprehensive score of seismic importance of building part i, w j A represents the weight of the jth seismic assessment index, 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 building part i on the jth seismic assessment index, i represents the potential seismic risk factor 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 closed with the preset seismic importance score. The area with a 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.

[0019] The algorithm adjustment is based on the main parts and secondary parts to adjust the collected building data. The sampling frequency formula of the main parts is as follows:

[0020]

[0021] 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_main is the basic high-density sampling frequency of the main body parts, F r_main Frequency adjustment factor calculated for the physical parameters of body part i;

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

[0023]

[0024] 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_sub is the basic low-density sampling frequency of the secondary parts, F r_sub Frequency reduction factor calculated for the physical parameters of secondary location j.

[0025] The algorithm adjustment is based on the main parts and secondary parts to adjust the collected building data. The main part sampling accuracy formula is as follows:

[0026]

[0027] Among them, A j is the sampling accuracy after adjustment of the subject part i, A b The basic high-precision sampling accuracy of the main body parts, A c is the accuracy adjustment factor for the calculation of the physical parameters of body part i;

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

[0029]

[0030] 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.

[0031] The model verification is performed by presetting the difference value, comparing the constructed building model with the existing acquisition parameters. If the difference exceeds the preset value, the building model is calibrated according to the difference data. The calibration formula is as follows:

[0032]

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

[0034] Among them, D k is the comprehensive index of difference 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 verification, 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 k-th round of verification. When the actual difference D k When this threshold is exceeded, the k-th round of model calibration process is triggered.

[0035] 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:

[0036]

[0037] 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;

[0038] 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, DPj is the preset seismic 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.

[0039] The evaluation feedback is based on the preset seismic damage data. Based on the building model damage data, the seismic data is summarized and compared 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 expectations and needs further optimization;

[0042] Among them, AE is the seismic assessment error, D Mi is the model seismic data, 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.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

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

[0045] When modeling existing buildings in digital twins, the existing building model is calibrated based on the difference data through model verification, seismic testing and evaluation feedback, thereby enhancing the accuracy of the existing building model. At the same time, the existing building model is calibrated based on feedback from the seismic test data 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the principle structure of the present invention;

[0047] Figure 2 This is a schematic diagram of the principle structure of the part differentiation in the present invention;

[0048] Figure 3 This is a schematic diagram of the sampling adjustment principle structure of the present invention;

[0049] Figure 4 This is a schematic diagram of the model verification structure in the present invention;

[0050] Figure 5 This is a schematic diagram of the seismic test principle structure of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] In this application, for ease of understanding, the method steps used do not need to be executed in the order of the steps in this embodiment during actual operation. In other embodiments, these steps may be performed simultaneously or in a different order.

[0053] like Figure 1-Figure 5 As shown, the present invention provides a technical solution: a method and system for dynamically evaluating the seismic performance of existing buildings based on digital twins, the method comprising:

[0054] 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 building material properties on the performance of existing building structures;

[0055] Part identification: pre-processing the collected existing building data to divide the building into main parts and secondary parts, which can optimize data processing efficiency and improve the accuracy of building status assessment;

[0056] 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;

[0057] Parametric modeling: building a digital model that is a twin of the existing building based on building material data and adjusted architectural data;

[0058] Model verification: preset difference values ​​are used to compare the constructed existing building model with the existing acquisition parameters. If the difference exceeds the preset value, the existing building model is calibrated based on the difference data, thus enhancing the accuracy of the existing building model.

[0059] Seismic testing: pre-set earthquake parameters, simulate earthquakes to test existing building models, collect damage data on building models, directly observe and record changes in existing building models under earthquake action, and provide a basis for evaluating the seismic performance of existing building models;

[0060] 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.

[0061] The data collection involves testing the building materials of existing buildings to obtain the chemical and physical properties of the building materials, and collecting the geometric dimensions, material properties, internal layout, and performance parameters of the existing buildings to ensure the accuracy and reliability of subsequent data twin modeling.

[0062] The part identification presets the seismic importance score and performs pre-processing based on the collected building data. The building is divided into main parts and secondary parts according to the seismic resistance of different parts of the building. The classification formula is as follows:

[0063]

[0064] Among them, S i It represents the comprehensive score of the seismic importance of building part i. The higher the score, the more critical the seismic performance of the part is and the greater the impact on the overall structure. j It represents the weight of the jth seismic assessment 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 assessment. ij A represents the original score of building part i on the jth seismic assessment index. These scores are based on the actual collected building data, such as material strength, structural size, connection method, etc. jminAjmax and Ajmax represent the minimum score and maximum score of all building parts on the jth seismic assessment index, respectively, and are used to standardize the original scores to the range of 0 to 1 for easy comparison and analysis. ij P represents the correction coefficient of building part i on the jth seismic assessment index. This coefficient is used to consider the impact of the specific conditions or characteristics of building part i on the jth index. For example, for parts with irregular structures, the correction coefficient of their structural stability index can be appropriately adjusted. i The potential seismic risk factor of building part i reflects the special risks or weaknesses that may exist in part i, such as aging materials, unreliable connections, etc. i The seismic demand coefficient of building part i reflects the degree of seismic demand of part i in the overall structure. For example, for a part located on the critical path of the structure, its seismic demand coefficient may be higher.

[0065] 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 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.

[0066] Bring in data,

[0067] Weight wj:

[0068] w1=0.4;

[0069] w2=0.3;

[0070] w3=0.3;

[0071] Minimum score A jmin and the 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 default correction coefficient is 1 (i.e., the influence of specific conditions or characteristics is not considered)

[0077] Potential seismic risk factor 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 of each part.

[0084] Comprehensive seismic importance score S1 of location 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 − 70 80 − 70 = undefined (but here we assume the denominator is not 0, which needs to be handled in practice);

[0090] Assume that A12 deviates 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 Section 1 is:

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

[0098] The comprehensive seismic importance score S2 of location 2 (calculated similarly):

[0099] For j=1:

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

[0101] Assume that A21 deviates 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] Assume that A23 deviates 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 for Section 2 is:

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

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

[0115] 4. The algorithm adjustment is based on the main parts and secondary parts to adjust the collected building data. The sampling frequency formula of the main parts is as follows:

[0116]

[0117] 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_main is the basic high-density sampling frequency of the main body parts, F r_main Frequency adjustment factor calculated for the physical parameters of body part i;

[0118] The formula for the sampling frequency of secondary parts 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 factor of the secondary part j, F b_sub is the basic low-density sampling frequency of the secondary parts, F r_sub Frequency reduction factor calculated for the physical parameters of secondary location j.

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

[0122]

[0123] Among them, A j is the sampling accuracy after adjustment of the subject part i, A b is the basic high-precision sampling accuracy of the main body, that is, the sampling accuracy before adjustment, A c is the accuracy adjustment factor for the calculation of the physical parameters of body part i;

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

[0125]

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

[0127] The model verification is performed by presetting the difference value, comparing the constructed building model with the existing acquisition parameters. If the difference exceeds the preset value, the building model is calibrated according to the difference data. The calibration formula is as follows:

[0128]

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

[0130] Among them, D k is the comprehensive difference index of the k-th round of verification, which indicates the comprehensive difference 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 the parameter in the overall difference assessment, M ki is the predicted value of the building model on the i-th parameter in the k-th round of verification, 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 validation, which is used to standardize the difference value to make it comparable between different parameters. k is the preset difference threshold for the k-th round of verification. When the actual difference D k When this threshold is exceeded, the k-th round of model calibration process is triggered.

[0131] Consider a building model with three parameters (n=3) to be verified: 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] The model predicted value of parameter A is M1A=80, the actual collected value is P1A=78, and the reference range or allowable error is R1A=5;

[0133] The model predicted value of parameter B is M1B=60, the actual collected value is P1B=65, and the reference range or allowable error is R1B=10;

[0134] The model predicted value of parameter C is M1C=90, the actual collected value is P1C=88, and the reference range or allowable error is R1C=5;

[0135] The preset difference threshold T1=1;

[0136] Difference calculation:

[0137] The difference in parameter A is: (M1A − P1A) / R1A = (80 − 78) / 5 = 0.4;

[0138] Difference in parameter B: (M1B − P1B) / R1B = (60 − 65) / 10 = −0.5;

[0139] The difference in parameter C is: (M1C − P1C) / R1C = (90 − 88) / 5 = 0.4;

[0140] Comprehensive difference index D1:

[0141]

[0142] ≈0.432;

[0143] Difference judgment:

[0144] Comparing D1 and T1, that is, 0.432<1, the correct judgment should be D1≤T1, indicating that the difference of the model in the first round of validation is within the acceptable range and no calibration is required.

[0145] 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:

[0146]

[0147] 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;

[0148] Where E is the seismic performance evaluation error, which represents the comprehensive difference between the damage data of the building model under simulated or actual earthquake action and the preset seismic damage data; m is the number of evaluation indicators, which represents the total number of damage indicators involved in the seismic performance evaluation; D Mj is the damage data of the building model, which is the damage value of the building model under simulated or actual earthquake action, D Pj is the preset seismic damage data, which means the damage value preset according to seismic design specifications or experience at the jth damage index, max(D Mj , D Pj ) is to take D Mj and D Pj The larger value in is used to standardize the difference value to ensure that the difference calculation is not exaggerated due to the denominator being too small. j is the weight of the jth damage index, reflecting the importance of this index in the overall seismic performance evaluation, 1 / m∑ m j=1 It is the mean calculation, which 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 means that the seismic performance of the building model does not meet the preset requirements.

[0149] Bring in data,

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

[0151] Damage index weight (wj):

[0152] w1=0.4 (structural deformation);

[0153] w2=0.3 (damage to non-structural components);

[0154] w3=0.3 (personnel safety);

[0155] Preset seismic damage data (DPj):

[0156] DP1=0.15 (preset damage value of structural deformation);

[0157] DP2=0.10 (preset damage value for non-structural components);

[0158] DP3=0.05 (preset damage value for personnel safety, assumed to be the minimum value of a certain safety indicator);

[0159] Building model damage data (DMj) (obtained by simulating earthquake action):

[0160] DM1=0.20 (actual damage value of structural deformation);

[0161] DM2=0.08 (actual damage value of non-structural components);

[0162] DM3=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, so further optimization or adjustment is needed.

[0169] The evaluation feedback is based on the preset seismic damage data. Based on the building model damage data, the seismic data is summarized and compared 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 expectations and needs further optimization;

[0172] Among them, AE is the seismic assessment error, which comprehensively measures the difference between the seismic data of the building model and the preset seismic damage data, D Mi is the model seismic data, which represents the seismic performance value of the building model on the i-th evaluation index, D Pi is the preset seismic damage data, the damage threshold of the ith evaluation index preset according to the seismic design code or experience, Ri is the range of the evaluation index, which represents the acceptable variation range of the ith evaluation index and is used for the calculation of the standardized difference, ((D Mi −D Pi ) / R i ) 2 is the squared difference term, which reflects the relative difference between the model seismic data 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 assessment, ∑ n i=1 It is a weighted average calculation to ensure that the evaluation error takes into account the weight of each evaluation indicator. T is the evaluation error threshold, which represents the maximum allowable seismic evaluation error.

[0173] Bring in data,

[0174] Number of evaluation metrics (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 index (DPi): 0.15 (structural deformation), 0.10 (damage to non-structural components), 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 (damage to non-structural components), 0.10 (personnel safety);

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

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

[0181] Now, we calculate based on 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 term:

[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;<000第508页>

[0192] Personnel safety:

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

[0194] = 0.1×0.75; 第515页

[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 embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is limited by the accompanying embodiments and their equivalents.

Claims

1. A dynamic assessment 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 building material properties on the performance of existing building structures; Part identification, based on pre-processing of 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 architectural data; Model verification: preset difference values ​​are used to compare the constructed existing building model with the existing acquisition parameters. If the difference exceeds the preset value, the existing building model is calibrated based on the difference data, thus enhancing the accuracy of the existing building model. Seismic testing: pre-set earthquake parameters, simulate earthquakes to test existing building models, collect damage data on building models, directly observe and record changes in existing building models under earthquake action, 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 by: The data collection involves testing the building materials of existing buildings to obtain the chemical and physical properties of the building materials, and collecting the geometric dimensions, material properties, internal layout, and performance parameters of the existing buildings to ensure the accuracy and reliability of subsequent data twin modeling.

3. The method for dynamic assessment of seismic performance of existing buildings based on digital twins according to claim 1 is characterized by: The algorithm adjustment is based on the main parts and secondary parts to adjust the collected building data. The sampling frequency formula of the main parts 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_main is the basic high-density sampling frequency of the main body parts, F r_main Frequency adjustment factor calculated for the physical parameters of body part i; The formula for the sampling frequency of 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_sub is the basic low-density sampling frequency of the secondary parts, F r_sub Frequency reduction factor calculated for the physical parameters of secondary location j.

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

5. The method for dynamic assessment of seismic performance of existing buildings based on digital twins according to claim 1 is characterized by: The model verification is performed by presetting the difference value, comparing the constructed building model with the existing acquisition parameters. If the difference exceeds the preset value, the building model is calibrated 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 difference 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 verification, 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 k-th round of verification. When the actual difference D k When this threshold is exceeded, the k-th round of model calibration process is triggered.

6. The method for dynamic assessment of seismic performance of existing buildings based on digital twins according to claim 1 is characterized by: 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 seismic 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.

7. The method for dynamic assessment of seismic performance of existing buildings based on digital twins according to claim 1 is characterized by: The evaluation feedback is based on the preset seismic damage data. Based on the building model damage data, the seismic data is summarized and compared 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 model seismic data, 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.

8. A dynamic assessment system for seismic performance of existing buildings based on digital twins, characterized by: A dynamic assessment method for seismic performance of existing buildings based on digital twins as described in any one of claims 1 to 7 is used.

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