Probabilistic Performance-Based Optimum Seismic Design of RC Structures Considering Soil–Structure Interaction Effects

Abstract

In spite of the advances in the development of efficient computational methods, reliability-based design optimization (RBDO) of large-scale structures subjected to seismic loading still requires prohibitive computational effort. Additional difficulties arise for the optimum design of reinforced concrete (RC) structures because they typically feature a wide variety of sections and shapes because of architectural requirements and versatility of construction. In this paper, a modified discrete gravitational search algorithm (MDGSA) is proposed and described, which is paired with a metamodel to minimize the total cost of structures (i.e., the sum of construction and repair costs) under deterministic and probabilistic constraints. MDGSA exploits the crossover and mutation operators used in the differential evolution optimization algorithm to improve the exploitation and exploration of the original gravitational search algorithm (GSA). The competency of the proposed method against the original GSA is demonstrated through performance-based optimum seismic design of an example nine-story RC building subject to both deterministic and probabilistic constraints. Nonlinear soil-structure interaction effects are taken into account in the dynamic finite element analysis of the soil-structure system. The metamodel, which is a weighted least squares support vector machine with the Morlet wavelet kernel function, is used to predict the seismic response of the structure. Annual probabilities of nonperformance are considered as the probabilistic constraints, and the Monte Carlo simulation technique is used to evaluate the seismic reliability of the structure. Results indicate that the proposed MDGSA in conjunction with the metamodel can provide an efficient and effective tool for the RBDO of large-scale structures.