Evaluation of Building Seismic Capacity Based on Improved Naive Bayesian Algorithm
The influencing factors of building seismic capacity are analyzed, the basic cause events of the assessment target based on fault tree analysis (FTA) are determined, the basic cause events in the FTA model are classified and summarized, and a judgment system of building seismic capacity is built. Th...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | International Journal of Geophysics |
| Online Access: | http://dx.doi.org/10.1155/2023/8532542 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546681489457152 |
|---|---|
| author | Yalong Li Wei Wang Bin Tan Hongxia Wang |
| author_facet | Yalong Li Wei Wang Bin Tan Hongxia Wang |
| author_sort | Yalong Li |
| collection | DOAJ |
| description | The influencing factors of building seismic capacity are analyzed, the basic cause events of the assessment target based on fault tree analysis (FTA) are determined, the basic cause events in the FTA model are classified and summarized, and a judgment system of building seismic capacity is built. The weight of each index factor in the Gini index calculation system is used, and the importance of the index is analyzed. On the basis of the Spearman correlation coefficient calculation of the index, the improved naive Bayesian algorithm is combined with the importance of the index to build a judgment model for the seismic capacity of housing buildings. The sample set is constructed based on the judgment system with the basic data of some housing buildings in Huoshan County. In order to improve the generalization ability and avoid overfitting, the K-SMOTE algorithm for mixed sampling was modified to improve sample balance, and random k-fold cross validation method was used for sample division and model optimization, achieving the determination of seismic capacity level of building. The research results indicate the following: (1) the accuracy of model evaluation is 93%, with model accuracy and recall rates of 0.913 and 0.93, respectively, indicating strong generalization ability of the model. (2) Selecting some actual examples of a building, the model judgment results are consistent with the actual results, verifying the correctness of the proposed method for building the model, which can be effectively used for determining the seismic capacity of building structures. (3) Applying the proposed method to the seismic capacity assessment of buildings in the Ta-pieh Mountains of Lu’an, it is concluded that the seismic capacity of urban buildings is common, while that of rural buildings is poor. |
| format | Article |
| id | doaj-art-9877778d016d48c59b05d4a48b37184b |
| institution | Kabale University |
| issn | 1687-8868 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Geophysics |
| spelling | doaj-art-9877778d016d48c59b05d4a48b37184b2025-02-03T06:47:38ZengWileyInternational Journal of Geophysics1687-88682023-01-01202310.1155/2023/8532542Evaluation of Building Seismic Capacity Based on Improved Naive Bayesian AlgorithmYalong Li0Wei Wang1Bin Tan2Hongxia Wang3Anhui Earthquake AgencyAnhui Earthquake AgencyAnhui Earthquake AgencyAnhui Academy of Agricultural SciencesThe influencing factors of building seismic capacity are analyzed, the basic cause events of the assessment target based on fault tree analysis (FTA) are determined, the basic cause events in the FTA model are classified and summarized, and a judgment system of building seismic capacity is built. The weight of each index factor in the Gini index calculation system is used, and the importance of the index is analyzed. On the basis of the Spearman correlation coefficient calculation of the index, the improved naive Bayesian algorithm is combined with the importance of the index to build a judgment model for the seismic capacity of housing buildings. The sample set is constructed based on the judgment system with the basic data of some housing buildings in Huoshan County. In order to improve the generalization ability and avoid overfitting, the K-SMOTE algorithm for mixed sampling was modified to improve sample balance, and random k-fold cross validation method was used for sample division and model optimization, achieving the determination of seismic capacity level of building. The research results indicate the following: (1) the accuracy of model evaluation is 93%, with model accuracy and recall rates of 0.913 and 0.93, respectively, indicating strong generalization ability of the model. (2) Selecting some actual examples of a building, the model judgment results are consistent with the actual results, verifying the correctness of the proposed method for building the model, which can be effectively used for determining the seismic capacity of building structures. (3) Applying the proposed method to the seismic capacity assessment of buildings in the Ta-pieh Mountains of Lu’an, it is concluded that the seismic capacity of urban buildings is common, while that of rural buildings is poor.http://dx.doi.org/10.1155/2023/8532542 |
| spellingShingle | Yalong Li Wei Wang Bin Tan Hongxia Wang Evaluation of Building Seismic Capacity Based on Improved Naive Bayesian Algorithm International Journal of Geophysics |
| title | Evaluation of Building Seismic Capacity Based on Improved Naive Bayesian Algorithm |
| title_full | Evaluation of Building Seismic Capacity Based on Improved Naive Bayesian Algorithm |
| title_fullStr | Evaluation of Building Seismic Capacity Based on Improved Naive Bayesian Algorithm |
| title_full_unstemmed | Evaluation of Building Seismic Capacity Based on Improved Naive Bayesian Algorithm |
| title_short | Evaluation of Building Seismic Capacity Based on Improved Naive Bayesian Algorithm |
| title_sort | evaluation of building seismic capacity based on improved naive bayesian algorithm |
| url | http://dx.doi.org/10.1155/2023/8532542 |
| work_keys_str_mv | AT yalongli evaluationofbuildingseismiccapacitybasedonimprovednaivebayesianalgorithm AT weiwang evaluationofbuildingseismiccapacitybasedonimprovednaivebayesianalgorithm AT bintan evaluationofbuildingseismiccapacitybasedonimprovednaivebayesianalgorithm AT hongxiawang evaluationofbuildingseismiccapacitybasedonimprovednaivebayesianalgorithm |