Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region

Abstract

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.

Keywords

• earthquake
• building seismic resilience (BSR)
• machine learning model (MLM)
• different classification schemes