Résumé:
Seismic vulnerability remains a critical challenge for reinforced concrete (RC) structures, particularly in seismically active regions such as Algeria. The devastating impacts of past earthquakes, including the El Asnam (1980) and Boumerdès (2003) earthquakes, have exposed significant deficiencies in the design, assessment, and rehabilitation of existing RC buildings. While the Algerian Seismic Code (RPA 99/2003 and RPA 2024) provides seismic design guidelines, it lacks a robust framework for post-rehabilitation evaluation, leading to uncertainties in structural safety and performance after retrofitting.This research develops a comprehensive Algeria-specific seismic assessment framework, integrating performance-based seismic design (PBSD), advanced numerical modeling, and modern rehabilitation techniques to enhance the seismic resilience of existing RC structures. Utilizing ETABS-based numerical simulations, the study evaluates the efficiency of steel bracing systems, fiber-reinforced polymers (FRPs), shape-memory alloys (SMAs), and energy dissipation devices in strengthening RC buildings against seismic forces. The proposed framework is validated through real-world case studies, including healthcare and residential infrastructure in Algeria’s high-risk seismic zones. A comparative analysis with international seismic codes (Eurocode 8, ASCE/SEI 41, ACI 318) highlights critical gaps in Algeria’s current seismic regulations, advocating for the adoption of advanced assessment techniques, nonlinear dynamic analysis, and post-rehabilitation evaluation criteria. Furthermore, this research examines the implications of RPA 2024, offering insights into how Algeria’s seismic regulations can evolve to incorporate modern structural engineering innovations.
The findings contribute significantly to the scientific and engineering community, bridging the gap between global advancements and Algeria’s specific seismic challenges. This study provides engineers and policymakers with a scientifically rigorous yet practically feasible tool for seismic assessment and sets a foundation for future research on experimental shake table testing and AI-driven seismic risk assessment.
