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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. 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. 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.