Numerical Modeling and Seismic Behavior Analysis of Structures Using Advanced Software and Performance Optimization of Retrofitting with Artificial Intelligence

This paper presents a novel and integrated approach for the numerical modeling and seismic behavior analysis of reinforced concrete structures. The primary objective of this research is to thoroughly investigate the response of structures under seismic loading and to optimize the retrofitting process using advanced computational methods and artificial intelligence (AI) algorithms. The study aims to combine accurate analytical tools with intelligent learning algorithms to take a significant step toward enhancing the seismic performance of structures. To this end, advanced software tools such as ETABS for structural modeling, OpenSees for nonlinear dynamic analyses, and MATLAB for implementing intelligent algorithms have been employed. A detailed three-dimensional model of a reinforced concrete moment-resisting frame has been developed, incorporating accurate material properties, plastic hinge behavior, and actual records of several significant earthquakes. The modeling was carried out to reflect the real behavior characteristics of the structure as accurately as possible. Subsequently, nonlinear time-history analyses were performed on the model to extract key seismic response parameters such as inter-story drift, base shear, structural drift, and the effective lateral stiffness of the entire structure. To evaluate and enhance seismic performance, these response parameters were fed into an optimization process utilizing artificial intelligence algorithms, particularly the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). These algorithms, inspired by natural evolutionary processes and collective social behavior, can intelligently explore the search space and provide optimal solutions for the structural retrofitting problem. The results of the numerical analysis and optimization demonstrate that the simultaneous use of accurate numerical tools and intelligent algorithms can significantly improve the effectiveness of structural retrofitting, reduce the vulnerability of structures to earthquakes, decrease inter-story drift, and increase the overall lateral stiffness of the structure. These findings are particularly valuable for structural engineers and researchers working in seismic design and can greatly contribute to the development of AI-based design methodologies for reinforced concrete structures. Moreover, the outcomes of this study are generalizable to other structural systems as well.

کلیدواژه ها:

Nonlinear analysis ، seismic retrofitting ، artificial intelligence ، reinforced concrete frame ، numerical modeling ، optimization algorithms (GA ، PSO)