Optimization seismic resilience: a machine learning approach for vertical irregular buildings

Abstract

The paper is a landmark in earthquake and structural engineering, with modern machine-learning techniques applied to introduce innovative investigations into forecasting seismic behavior for vertically uneven structures using sophisticated machine-learning methodologies. The research constructs a very accurate model for making predictions using the XGBoost algorithm with the Owl Search algorithm (OSA) for hyperparameter tuning, which explicitly considers complex behavior in the structures under seismic stresses. The variety within the dataset is broad and covers all kinds of irregularities in the structures, such as stiffness and mass irregularities; thus, it has been used to accurately represent the complex characteristics of actual buildings. The results indicate a strong dependence of base shear capacity and seismic performance on the irregularity of stiffness and mass. The test accuracy of the optimized XGBoost model was 98.8%. The result was better than that of conventional models, thus proving the effectiveness of integrating the Owl Search Algorithm in further fine-tuning the parameters. These results give new variables as insight into affecting earthquake resilience and represent practical applications that enhance building design and retrofitting processes. This is further underlined by the proposal of future research directions that would extend the model’s applicability to other structural anomalies and include additional machine-learning methodologies. Through AI-driven approaches, this study captured complicated structural dynamics with the utmost precision, thus opening new insights that could be brought into practice to improve building design and retrofitting strategies in a way that would diminish the impact of seismic events.

Graphical abstract