Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data

<p>This Reprint presents a comprehensive collection of cutting-edge research on structural identification and damage evaluation through the integration of physics-based models with data-driven approaches. The compilation addresses one of the most critical challenges in structural health monitoring: combining the theoretical rigor of physics-based numerical models with the adaptive capabilities of modern data science techniques.</p><p>The featured studies demonstrate innovative methodologies that bridge traditional finite element model updating approaches with advanced machine learning algorithms physics-informed neural networks and Bayesian inference techniques. Researchers explore novel applications including deep learning-enhanced stress identification in prestressed structures automated concrete crack detection using computer vision and real-time structural assessment through digital twin technologies.</p><p>Key contributions encompass deterministic and stochastic finite element model updating physics-guided machine learning for damage detection hybrid modeling frameworks for structural systems and uncertainty quantification in structural assessment. The Reprint showcases practical implementations across diverse structural types from high-rise buildings and bridge systems to specialized infrastructure components like lightning rod structures and prestressed concrete girders.</p>