Comparison of Modeling and Analysis Results of Seismic Retrofitting Using Artificial Intelligence and Evaluation of Their Advantages and Disadvantages at Industrial Scale

Given the increasing risks and destructive impacts of earthquakes on civil structures, there is a growing need to use novel and efficient methods for seismic retrofitting and enhancing structural performance. Although traditional retrofitting methods have been effective in many cases, they face limitations in accuracy, cost, implementation time, and precise predictability of structural behavior. In this context, modern technologies—especially Artificial Intelligence (AI)—have attracted researchers and engineers as powerful and precise tools for advanced analysis, performance evaluation, and optimization of retrofitting processes. This study focuses on comparing the performance of AI-based modeling and analysis methods for seismic retrofitting at an industrial scale. Algorithms and techniques such as Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Machine Learning (ML) are evaluated as tools for analysis and optimization. The results indicate that these techniques can accurately predict structural behavior under seismic loads and provide optimal retrofitting solutions. Key benefits of AI include reduced analysis time, increased prediction accuracy, ability to process large datasets, and simultaneous optimization of multiple variables. However, challenges such as the need for precise training data, complexity in interpreting model outputs, and initial implementation costs at industrial scale also exist. This article discusses the pros and cons of AI models in retrofitting and offers practical recommendations for industrial application, aiming to improve the safety and performance of structures.

کلیدواژه ها:

Artificial Intelligence ، Seismic Retrofitting ، Artificial Neural Networks ، Genetic Algorithms ، Machine Learning ، Structural Analysis