Hybrid principal component analysis and artificial neural networks method to improve the seismic response prediction

Seismic vulnerability assessment is crucial for ensuring the structural safety of buildings, particularly in earthquake-prone regions. While Nonlinear Time History Analysis (NLTHA) provides high accuracy, its computational demands make it impractical for rapid assessments. Machine learning (ML) models, especially Artificial Neural Networks (ANN), offer an efficient alternative but often require large datasets for reliable predictions. This study introduces a hybrid Principal Component Analysis-Artificial Neural Network (PCA-ANN) model to enhance seismic response prediction by reducing input dimensionality while preserving critical information. A dataset of over one million seismic responses was generated using NLTHA on three reinforced concrete (RC) frame buildings subjected to various ground motions. Comparative analysis between PCA-ANN and conventional ANN models reveals that PCA-ANN significantly improves both predictive accuracy and computational efficiency. The PCA-ANN model achieved a correlation coefficient (R 2 ) of 99.1% and reduced Mean Squared Error (MSE) by 87% compared to the standalone ANN. Additionally, PCA-ANN maintained robust performance with limited dataset sizes, achieving an R 2 above 75% using only 25% of the dataset, whereas ANN failed under similar conditions. Further validation through Incremental Dynamic Analysis (IDA) and fragility curves shows that PCA-ANN exhibits discrepancies below 2% compared to NLTHA. The model also achieves the lowest Relative Squared Error (RSR) (18%, 21%, and 24% for low-, mid-, and high-rise buildings, respectively) and the lowest Percentage Bias (PBias) (1.7%, 0.5%, and 0.3% for the same building types) when utilizing the full dataset. These results highlight PCA-ANN’s superior reliability across varying structural heights and dataset sizes. This study demonstrates that PCA-ANN is an efficient and accurate tool for seismic risk assessment, reducing computational costs while maintaining predictive reliability.