Implementation of clustering algorithms for damage prediction through seismic local-site parameters: 2023 kahramanmaraş earthquake sequence

Abstract

The latest earthquakes (Morrocco, Nepal, Sichuan - China, etc.) have highlighted the critical importance of local-site parameters on the vulnerability of existing building stock. The paper performs the clustering method based on the sub-surface parameters for structural damage prediction. The data set includes the damage status for 44 locations after the 2023 Kahramanmara & scedil; earthquake sequence and local site parameters: Vs30, predominant frequency (f0), horizontal to vertical spectral ratio value (A0), and engineering bedrock depth (VsD760). The Fuzzy C-Means (FCM) and Spectral Clustering (SC) algorithms are carried out on the pre-processed data set, including the sub-surface parameters for each location and the data set clustered into two-clusters within each method. Then, the estimated clusters are compared with the post-earthquake two clusters representing the cluster of damage and no-damage state for considered locations that composed through official damage assessment reports The FCM algorithm yielded a 90% accuracy compared to actual clusters, while the results of the SC algorithm indicated an 86% accuracy. Among the parameters, the VsD760 and f0 demonstrate the ability to establish a discernible demarcation by manifesting distinguishable clustering patterns. Notably, the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) value is calculated at 97% and 85% for FCM and SC algorithms, respectively. The outcomes of this study offer the potential to predict the structural damage status of a location under a crucial seismic hazard in the pre-earthquake condition. This enables the development earthquake-resistant cities prior to earthquakes or implement necessary precautions to mitigate seismic risk in the afterward.