In seismic structural engineering, different methods are used to evaluate performance. Simplified approaches provide conservative estimates, while advanced analyses achieve higher precision at the cost of significant computational effort. Nonlinear Time History Analysis (NTHA) remains the most reliable method, but its high computational demand has led many researchers to propose simplified models, often resulting in conservative outcomes. This study proposes an Artificial Intelligence (AI)-based method to approximate NTHA. An Autoregressive Neural Network (ARNN) is developed to generate complete time-history responses of structures with minimal error relative to NTHA. Using ground motion data and the first three fundamental periods as inputs, the ARNN replicates NTHA responses with high accuracy. Unlike conventional surrogate models that predict only peak responses, the ARNN produces the entire response history. The ARNN is further integrated with a moment-based reliability framework employing the four-moment Pearson distribution (4M-Pearson), enabling efficient and accurate seismic reliability assessment. A three-story base-isolated steel structure is analyzed as a case study. Results demonstrate that the proposed ARNN achieves high precision in predicting both structural time-history responses and seismic reliability.

Sensitivity analysis is a decisive step in experimental and numerical structural mechanics. The analysis of structural model quantifies the importance of each input parameter, potential interaction and effects on structural response. Therefore, this study aimed to help reduce the uncertainty surrounding major variables, providing valuable guidance for conducting future experiments. During the investigation, numerically deterministic sensitivity analysis based on multicriteria model evaluations of load-displacement curves representing actual behavior of the member correctly, were reviewed. Multicriteria model combined the evaluation of peak load, energy dissipation before ultimate loading, and toughness of load-displacement response. The methodology led to a strong sensitivity analysis method, generating an agreement between numerical and experimental responses. Moreover, an investigation of the method was presented for a geopolymer haunch, the numerical model was based on rigid body spring model (RBSM), which enabled precise behavior simulation of reinforced concrete structures. RBSM was refined, enabling in-depth evaluation of stress-strain contours, plasticity index, initial crack formation and crack propagation, as well as RBSM-spring failure modes. The proposed multicriteria sensitivity analysis can be implemented with other simulation methods, such as finite element analysis (FEA) and structural simulation software. The recommended method is applicable to any structural member, where laboratory-tested full-scale specimens were functioning as validation tools. Following the proposed multicriteria sensitivity analysis, experimental load-displacement curves of this study supported the results of numerical RBSM in an acceptable range of error predictions.