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

Artikel i vetenskaplig tidskrift, 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.