Physic-Informed Neural Networks (PINNs) are emerging as a promising methodology for integrating physical knowledge with machine learning, offering new perspectives in the modeling and analysis of complex problems in civil engineering. The goal is to combine data-driven approaches, based on extensive experimental datasets, with physics-driven models that incorporate the fundamental principles of structural mechanics. The data-driven component leverages artificial neural networks to learn nonlinear relationships between geometric parameters, loading conditions, and structural response, while the physics-driven component introduces constraints derived from equilibrium equations, compatibility conditions, and constitutive laws, enhancing coherence and interpretability. In this study, the framework is trained by combining continuous structural health monitoring data (24/7) with results from finite element method (FEM) simulations, including static, modal, dynamic, and progressive damage analyses. The model is designed to support predictive maintenance strategies and operational decision-making in the field of structural conservation, optimizing resource allocation and intervention planning. This methodology is applied to the Esedra of Musei Capitolini in Rome, a steel and glass structure subject to a continuous monitoring system using accelerometric and inclinometric sensors. In this context, the PINN model learns the dynamic behavior of the structure by integrating experimental measurements with finite element simulations. Initially developed in MatLab using a feedforward neural network, the model represents an initial implementation of a framework adaptable to different structural typologies. The framework is therefore designed to be scalable both in computational terms — through parallel simulation execution and incremental network training — and in terms of application, supporting future integration with digital twin systems and BIM platforms. The proposed approach represents a significant step toward advanced, sustainable, and scalable structural analysis, with potential applications in the continuous monitoring and predictive maintenance of historical assets and strategic infrastructure.