Advances in Fractal, Fractional and AI-Driven Methods for Earthquake Engineering

This Research Topic highlights recent advances in fractal geometry, fractional calculus, and artificial intelligence (AI) for addressing complex problems in earthquake engineering. Traditional integer-order models and homogeneous assumptions often struggle to characterize the multi-scale, non-stationary, heterogeneous, and strongly nonlinear behaviors inherent in seismic ground motions, fault structures, geotechnical media, and structural systems under earthquake excitations. Fractal approaches effectively quantify scale-invariant features of earthquake sequences, fault roughness, site heterogeneity, and damage patterns. Fractional calculus provides a rigorous framework to model frequency-dependent damping, long-memory effects, rate-dependent hysteresis, and energy dissipation in geomaterials and structures with improved accuracy. AI-driven methods, including machine learning, deep learning, and data-driven modeling, offer powerful tools for big seismic data processing, ground motion prediction, structural response forecasting, damage detection, risk assessment, and rapid post-earthquake evaluation.This topic welcomes high-quality original research articles, comprehensive reviews, and technical notes covering theoretical developments, numerical simulations, experimental validations, and engineering applications. Relevant themes include fractal and fractional modeling of seismic waves and soil-structure interaction; fractional-order seismic control and isolation systems; AI-based seismic hazard analysis and structural health monitoring; multi-scale and data-driven computational methods; and resilience assessment of civil infrastructure under extreme seismic events. Contributions that integrate fractal, fractional, and AI techniques to enhance seismic performance, safety, and resilience of buildings, bridges, and geotechnical systems are particularly encouraged. This Research Topic aims to promote cross-disciplinary innovations and provide state-of-the-art references for researchers and engineers in earthquake engineering, structural engineering, geotechnical engineering, and computational civil engineering. Topic editors: Ji-Huan He, Dragan Marinkovic Source: https://www.frontiersin.org/research-topics/80732/advances-in-fractal-fractional-and-ai-driven-methods-for-earthquake-engineering