AI-POWERED EARTHQUAKE RESILIENCE PREDICTIVE MODELING AND DESIGN OPTIMIZATION FOR SEISMIC-RESISTANT STRUCTURES

Abstract

Earthquakes pose significant threats to infrastructure, necessitating advanced resilience strategies. This research explores AI-powered predictive modeling and design optimization for seismic-resistant structures. By integrating deep learning, finite element analysis, and real-time sensor data, the study enhances structural performance assessment and failure prediction. AI-driven simulations optimize material selection, reinforcement patterns, and damping systems to mitigate seismic impact. The proposed framework aims to revolutionize earthquake engineering by enabling proactive decision-making and cost- effective resilient designs. Methods This study employs AI-driven predictive modeling and design optimization techniques to enhance earthquake resilience in structures. A hybrid approach Abhijit S. Kolhe, V.R. Rathi https://iaeme.com/Home/journal/IJCIET 2 editor@iaeme.com integrating deep learning, finite element analysis (FEA), and real-time sensor data is used to assess structural performance. Machine learning models trained on historical seismic data predict potential failure points, while optimization algorithms refine material selection, reinforcement layouts, and damping mechanisms. AI-enhanced simulations validate the effectiveness of various seismic-resistant designs, ensuring practical applicability in real-world construction. Analysis The proposed framework is evaluated through extensive simulations and case studies on different structural configurations. Performance metrics such as displacement, stress distribution, and energy dissipation are analyzed to determine the efficiency of AI-optimized designs. Comparative studies between conventional and AI- assisted seismic-resistant structures reveal improvements in structural integrity, response time, and cost-effectiveness. The integration of real-time sensor data enhances predictive accuracy, enabling proactive reinforcement strategies to mitigate seismic damage. Conclusion This research demonstrates the potential of AI-powered predictive modeling in enhancing earthquake resilience. The proposed system effectively identifies structural weaknesses, optimizes seismic-resistant designs, and improves overall safety. AI-driven analysis outperforms traditional methods in accuracy, adaptability, and cost efficiency, making it a transformative approach for seismic engineering. Future work includes real-world implementation and integration with smart infrastructure systems to further enhance disaster preparedness and resilience.