AI-driven seismic optimization of outrigger systems in high-rise buildings: A machine learning framework for enhanced performance in earthquake-prone regions

2025-12-17

Aqdas Shehzad, Wang Xiu-Xin, Huang Xing-Huai, Karim Ullah, Alsharef Mohammad, Ahmed Althobaiti, Aymen Flah,
AI-driven seismic optimization of outrigger systems in high-rise buildings: A machine learning framework for enhanced performance in earthquake-prone regions,
Journal of Building Engineering,
Volume 112,
2025,
113864,
ISSN 2352-7102,
https://doi.org/10.1016/j.jobe.2025.113864.
(https://www.sciencedirect.com/science/article/pii/S2352710225021011)
Abstract: This study introduces a novel machine learning-based framework to optimize outrigger systems for enhancing the seismic performance of tall buildings. Unlike conventional static or heuristic design methods, the proposed model integrates Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for seismic response prediction, and applies Genetic Algorithms (GA) within a closed-loop optimization cycle. The framework uniquely combines supervised learning, reinforcement-based refinement, and structural simulations (pushover and time-history analysis) to automatically identify the optimal outrigger configuration position, stiffness, damping, and material properties under varying seismic loads. Empirical data and synthetic simulations are used to train the models, resulting in significant reductions in lateral displacement (46.67 %), inter-story drift (55.56 %), and improved energy dissipation (33.33 %). Benchmarking against standalone ANN and SVM models demonstrates superior prediction accuracy (RMSE = 0.089) and design efficiency. The proposed ANN-SVM-GA model is code-adaptable, resource-efficient, and scalable across seismic zones, achieving an 18 % RMSE reduction over ensemble baselines marking a significant advancement in intelligent structural design for earthquake-prone regions.
Keywords: Seismic optimization; Outrigger systems; Artificial Neural Networks (ANN); Support Vector Machines (SVM); Reinforcement learning; Genetic algorithm (GA); Tall buildings; Earthquake resilience; Structural performance prediction; Machine learning in civil engineering