Seismic damage prediction and ground motion characterization in RC buildings via unsupervised learning
Computers and Concrete, vol.37, no.4, pp.765-789, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 37 Issue: 4
- Publication Date: 2026
- Doi Number: 10.12989/cac.2026.37.4.765
- Journal Name: Computers and Concrete
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
- Page Numbers: pp.765-789
- Keywords: clustering analysis, machine learning, principal component analysis (PCA), seismic damage prediction, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP)
- Uşak University Affiliated: Yes
Abstract
This study investigates the latent relationship between ground motion parameters and seismic damage in reinforced concrete (RC) buildings using an unsupervised machine learning framework. A dataset comprising 21 seismic parameters from 466 ground motion records and the nonlinear response of 1,056 RC building models was analyzed. Unlike traditional regression-based studies, this research employs a “blind” learning approach to determine if clustering algorithms can autonomously identify physical damage mechanisms. The K-means algorithm, validated by K-fold cross-validation and internal indices, identified three distinct hazard categories. Crucially, the algorithm autonomously isolated a “Pulse-Like/Resonance-Critical” cluster driven by frequency-content parameters (Vmax/Amax and Tm) rather than simple peak intensity. Furthermore, Principal Component Analysis (PCA) revealed that a reduced “Core Parameter Set” of six indices (including EDA, Arias Intensity, and Vmax/Amax explains 89% of the variance, offering a practical subset for ground motion selection. Finally, manifold learning techniques (t-SNE and UMAP) demonstrated that the complex seismic damage landscape can be unfolded into a quasi-linear manifold, validating the stability of the proposed clustering.