Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region
This study aims to develop different-classification-scheme-based building-seismic-resilience (BSR)-mapping models using random forest (RF) and a support vector machine (SVM). Based on a field survey of earthquake-damaged buildings in Shuanghe Town, the epicenter of the Changning M 5.8 earthquake tha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/9/2226 |
| _version_ | 1797601811851378688 |
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| author | Haijia Wen Xinzhi Zhou Chi Zhang Mingyong Liao Jiafeng Xiao |
| author_facet | Haijia Wen Xinzhi Zhou Chi Zhang Mingyong Liao Jiafeng Xiao |
| author_sort | Haijia Wen |
| collection | DOAJ |
| description | This study aims to develop different-classification-scheme-based building-seismic-resilience (BSR)-mapping models using random forest (RF) and a support vector machine (SVM). Based on a field survey of earthquake-damaged buildings in Shuanghe Town, the epicenter of the Changning M 5.8 earthquake that occurred on 17 June 2019, we selected 19 influencing factors for BSR assessment to establish a database. Based on three classification schemes for the description of BSR, we developed six machine learning assessment models for BSR mapping using RF and an SVM after optimizing the hyper-parameters. The validation indicators of model performance include precision, recall, accuracy, and F1-score as determined from the test sub-dataset. The results indicate that the RF- and SVM-based BSR models achieved prediction accuracies of approximately 0.64–0.94 for different classification schemes applied to the test sub-dataset. Additionally, the precision, recall, and F1-score indicators showed satisfactory values with respect to the BSR levels with relatively large sample sizes. The RF-based models had a lower tendency for overfitting compared to the SVM-based models. The performance of the BSR models was influenced by the quantity of total datasets, the classification schemes, and imbalanced data. Overall, the RF- and SVM-based BSR models can improve the evaluation efficiency of earthquake-damaged buildings in mountainous areas. |
| first_indexed | 2024-03-11T04:09:08Z |
| format | Article |
| id | doaj.art-694dd24b64de44af893c4d2eb223116c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:09:08Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-694dd24b64de44af893c4d2eb223116c2023-11-17T23:37:13ZengMDPI AGRemote Sensing2072-42922023-04-01159222610.3390/rs15092226Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous RegionHaijia Wen0Xinzhi Zhou1Chi Zhang2Mingyong Liao3Jiafeng Xiao4National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education Chongqing, School of Civil Engineering, Chongqing University, Chongqing 400045, ChinaState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100083, ChinaNational Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education Chongqing, School of Civil Engineering, Chongqing University, Chongqing 400045, ChinaNational Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education Chongqing, School of Civil Engineering, Chongqing University, Chongqing 400045, ChinaNational Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education Chongqing, School of Civil Engineering, Chongqing University, Chongqing 400045, ChinaThis study aims to develop different-classification-scheme-based building-seismic-resilience (BSR)-mapping models using random forest (RF) and a support vector machine (SVM). Based on a field survey of earthquake-damaged buildings in Shuanghe Town, the epicenter of the Changning M 5.8 earthquake that occurred on 17 June 2019, we selected 19 influencing factors for BSR assessment to establish a database. Based on three classification schemes for the description of BSR, we developed six machine learning assessment models for BSR mapping using RF and an SVM after optimizing the hyper-parameters. The validation indicators of model performance include precision, recall, accuracy, and F1-score as determined from the test sub-dataset. The results indicate that the RF- and SVM-based BSR models achieved prediction accuracies of approximately 0.64–0.94 for different classification schemes applied to the test sub-dataset. Additionally, the precision, recall, and F1-score indicators showed satisfactory values with respect to the BSR levels with relatively large sample sizes. The RF-based models had a lower tendency for overfitting compared to the SVM-based models. The performance of the BSR models was influenced by the quantity of total datasets, the classification schemes, and imbalanced data. Overall, the RF- and SVM-based BSR models can improve the evaluation efficiency of earthquake-damaged buildings in mountainous areas.https://www.mdpi.com/2072-4292/15/9/2226earthquakebuilding seismic resilience (BSR)machine learning model (MLM)different classification schemes |
| spellingShingle | Haijia Wen Xinzhi Zhou Chi Zhang Mingyong Liao Jiafeng Xiao Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region Remote Sensing earthquake building seismic resilience (BSR) machine learning model (MLM) different classification schemes |
| title | Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region |
| title_full | Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region |
| title_fullStr | Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region |
| title_full_unstemmed | Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region |
| title_short | Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region |
| title_sort | different classification scheme based machine learning model of building seismic resilience assessment in a mountainous region |
| topic | earthquake building seismic resilience (BSR) machine learning model (MLM) different classification schemes |
| url | https://www.mdpi.com/2072-4292/15/9/2226 |
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