The degradation of mortar-rendered facades is a persistent challenge in urban environments, affecting both the aesthetic and structural integrity of buildings. With the growing prevalence of climate-induced stresses and urbanization, the need for advanced methodologies to predict and mitigate facade defects has become increasingly critical. This study builds upon foundational research, such as Gaspar and de Brito’s probabilistic mapping of defect sensitivity in mortar renders, by introducing updated techniques and tools to analyze, model, and predict facade degradation patterns. The research integrates field data from diverse climatic and geographic regions with modern computational methodologies, including machine learning, Bayesian probabilistic analysis, and GIS-based mapping. These tools enable a more comprehensive understanding of the types and progression of facade defects, such as cracking, dampness, and surface deterioration, across key structural components like continuous walls, parapets, and ground-level areas. Special attention is given to the interactions between material properties, environmental conditions, and construction techniques.

mortar rendered facades,probabilistic defect mapping,facade degradation patterns,climate-resilient,materials advanced computational analysis