The increasing complexity, age, and exposure of civil structures around the world demand more reliable and proactive monitoring systems. As components deform, displace or lose functionality due to ageing degradation or to extraordinary actions like earthquakes or flooding, the structural system evolves silently -or rapidly- towards its limits. Traditional monitoring methods are often reactive and fragmented, failing to provide an integrated, real-time view of the structure’s true condition. This work presents a predictive solution for structural assessment based on the measured shape of the structure, captured through a proprietary optical sensing system. The system translates real-time shape measurements into structured variables that represent the physical behavior of the structure. These variables are processed by a detection engine developed in-house, which integrates artificial intelligence and probabilistic reasoning to estimate the structural condition and its future evolution. The proposed solution has been trained with numerical models and data from real bridges under different scenarios, continuously improving its ability to detect critical patterns and anticipate limit states. The system can transform raw shape data into actionable indicators for decision-making. This predictive engine offers a scalable and truly proactive system that can be adapted to different typologies and use cases. CALSENS is currently applying this methodology in instrumented bridges and controlled experiments, moving towards a robust and reliable monitoring ecosystem that combines sensing, modeling and structural interpretation in a single intelligent platform.