Machine learning-enhanced metaheuristic optimization of lead rubber bearings for inter-story isolated buildings under seismic load

Abstract

This paper presents a machine learning-enhanced metaheuristic optimization framework to improve the seismic performance of inter-story isolated buildings equipped with lead rubber bearings (LRBs). The study focuses on three key objectives: 1) assessing the influence of isolator placement at different elevations on critical seismic responses; 2) identifying optimum LRB parameters across different isolator placements, through multi-objective optimization using particle swarm optimization (PSO) algorithm; and 3) developing an Artificial Neural Network (ANN) based surrogate model for rapid prediction of optimal LRB parameters. A 10-story shear building with LRBs installed at intermediate stories is modeled and analyzed under 175 historical earthquake records to attain these objectives. The results demonstrate that the seismic performance of inter-story isolated buildings is highly dependent on the elevation of isolator placement, with lower, mid, and upper story level inter-story isolation (ISI) configurations being most effective for displacement reduction, acceleration control, and base shear mitigation, respectively. The multi-objective optimization framework using PSO has successfully identified the story-specific optimal LRB parameters, and comparative analyses confirmed moderate performance gains over non-optimized configurations. The proposed ANN model exhibits strong predictive accuracy and generalizes well, offering a computationally efficient alternative to metaheuristic optimization runs, enabling rapid estimation of optimum LRB parameters.

Graphical abstract

Keywords

ISI / LRB / multi-objective optimization / PSO / ANN

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