Evaluating the Performance of Python-Based Machine Learning in Earthquake-Resistant Building Design: - Fuqaha, Sameh; Nugroho , Guntur (Rekayasa Sipil, 2025)
This study investigates the feasibility of applying artificial intelligence (AI)-based machine learning techniques, specifically a Multiple Linear Regression (MLR) model implemented in Python, for earthquake-resistant building design. The AI-based predictions are compared against conventional SAP2000 structural analysis. As one of the most seismically active regions globally, Indonesia urgently requires efficient and accurate seismic design methodologies. Traditional approaches, while reliable, are often time-consuming and labor-intensive, whereas AI offers rapid data processing and automation. This research predicted key structural parameters—including mass participation ratio, base shear force, inter-story drift, and structural period—using the MLR model and benchmarked against SAP2000 simulations. The AI-based predictions exhibited excellent alignment, with an average deviation of only 0.016%. Statistical validation showed an R² score of 0.999 and a p-value of 0.738, confirming no significant difference between the two methods. Moreover, the AI model significantly reduced computational time, completing analyses within seconds compared to the extended duration required by SAP2000. Despite these advantages, the current AI framework lacks a 3D modeling interface, limiting its applicability for detailed structural design. Future research should enhance AI capabilities by integrating parametric modeling tools and Building Information Modeling (BIM) platforms to support broader implementation in earthquake-resistant structural engineering.