Predicting façade deterioration using machine learning approach with drone imagery and microclimate data

Journal article, 2025

Accurate prediction of facade deterioration due to microclimate effects is crucial for sustainable building management and preservation. This paper introduces a methodology for full facade risk assessment using image-based empirical evidence from real buildings. The PAIR methodology-Prepare, Analyze, Integrate, Relate-combines drone imagery, computer vision, weather data, and three-dimensional neighborhood models to create a database that organizes facade deterioration data into sections. This database supports a machine learning model to predict facade deterioration risks. A case study of 16 brick facade in Gothenburg, Sweden, demonstrated the model's strong performance (R2 = 0.978, MSE = 0.0003) on the test sample. However, performance declined on an excluded validation facade (R2 =-0.467, MSE = 0.024) due to limited training data and inaccuracies from prior maintenance. Despite these limitations, the methodology provides a computationally efficient alternative to full-scale hygrothermal modeling for assessing deterioration risk across entire facade.