Deadline Date: 30 April 2027
With the accelerating pace of urbanization and the large-scale construction of major infrastructure projects, engineering infrastructure systems are evolving toward greater scale, complexity, and multifunctionality. During long-term service, these structures are inevitably exposed to various complex environmental actions, including earthquakes, fires, explosions, impacts, fatigue, corrosion, extreme weather, and coupled multi-hazard effects, making issues related to structural safety, stability, and resilience increasingly prominent. Catastrophic structural failures are typically characterized by suddenness, strong nonlinearity, multiscale interactions, and multiphysics coupling. As a result, traditional experimental and theoretical approaches face considerable challenges in revealing failure mechanisms, reproducing full-process structural responses, and predicting structural performance under complex operating conditions.
Recent advances in numerical simulation, high-performance computing, digital twins, data-driven approaches, and artificial intelligence technologies have provided new theoretical foundations and technical pathways for investigating catastrophic failures and evaluating the safety performance of engineering infrastructure. In particular, intelligent algorithms such as machine learning, deep learning, generative artificial intelligence, and physics-informed neural networks (PINNs) are promoting the transformation of conventional structural analysis methods from empirical-driven approaches toward data–physics fusion paradigms. These developments offer important support for high-precision simulation, real-time prediction, and intelligent decision-making in complex structural failure scenarios.
This special issue focuses on recent advances in simulation methods and intelligent algorithms for catastrophic failures in engineering infrastructure. We warmly invite researchers and practitioners to contribute original research articles, review papers, and case studies. Topics of interest include, but are not limited to, the following:
a) Simulation and analysis of catastrophic mechanisms and failure modes in engineering structures
b) Multi-Scale and Multi-Physics coupled simulation methods for structural catastrophe
c) Artificial intelligence-based structural damage identification and safety assessment
d) Integrated modeling based on data-driven and physics-driven approaches
e) Digital twin technology and intelligent operation and maintenance of engineering infrastructure
f) Probabilistic risk analysis and structural resilience assessment
g) Intelligent optimization design and reliability analysis of engineering structures
h) Simulation and analysis of catastrophic behavior in advanced materials and composite structures
Editorial board
