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.

Asphalt pavement consists of aggregates resulting in a waste material at end of its life. The aggregates can be reused as basic material for asphalt or cementitious binding agents. In both scenarios, the recycled aggregates should provide a good bond with the binder to achieve strength. This study focuses on reusing recycled asphalt aggregates (RAA) in mortar. The major weakness of RAA is the thin oily film originating from the asphalt residue, weakening the bond with cement. The pyrolysis method is accessed in an attempt to overcome this weakness. Three scenarios were investigated; the use of virgin aggregates (VA), RAA, and pyrolysis recycled asphalt aggregate (PRAA) as constituent in mortar. All variables were set a constant except for the aggregate type, the VA mortar function as controlling element. This research is methodologically based on experimental data conducted in the laboratory, while aggregate samples were taken from the field. To analyze the influence of pyrolysis to the aggregate-to-cement bond behaviour, qualitative and quantitative data were collected. The quantitative data were the mechanical properties, the mortar tensile, and compression strength. The qualitative data were obtained from scanning electron microscope readings to visually observe the aggregate surface roughness and voids, including the aggregates cross-section and pre-existing micro-cracks in the aggregate-to-cement interface. Supporting data were the aggregates‘ abrasion rate and absorption. The RAA resulted in a significant mortar strength decrease. This conclusion was supported by the findings of pre-existing cracks in the interfacial transition zone. The pyrolysis method improved the compression strength but negligibly affected the tensile behavior. The compression and tensile strength increased as a function of time for both RAA and PRAA, and a strength convergence was reached at 28 days. The PRAA is considered an option for reuse in mortar, supporting nature conservation.