Predicting Architectural Decay by AI Applied to 3D Survey

The paper presents a predictive framework for monitoring architectural decay by integrating 3D survey data with artificial intelligence techniques. The methodology is structured as a Digital Decision System (DDS) that combines multi-source data acquisition (laser scanning, photogrammetry, mobile mapping, and low-cost sensors) with machine learning models to forecast degradation trends. Following a Knowledge Discovery in Database (KDD) pipeline—comprising data collection, preprocessing, transformation, data mining, and interpretation—the system uses linear regression to model the relationship between temporal data and measurable degradation phenomena. The approach is validated through experimental applications, demonstrating how predictive models can support conservation strategies, optimize monitoring processes, and assist decision-making in architectural restoration.