Predicting Renovation Risk in Existing Buildings Using Multilayer Perceptrons: Correlation-Based Feature Screening and Model Architecture Comparison

Renovation projects in existing university buildings involve considerable uncertainty due to incomplete documentation, aging building systems, phased execution, and the need to maintain ongoing educational activities during construction. This study investigates multilayer perceptron (MLP) architectures as an exploratory proof-of-concept for mapping an expert-based renovation risk index expressed as a continuous value between 0 and 1. The analysis was based on 122 real renovation cases described by 13 input variables representing quantitative factors and encoded qualitative characteristics related to technical and organizational project conditions. Data preprocessing included qualitative data encoding and min–max normalization. The models were trained and evaluated using an 80/10/10 hold-out split with validation-based early stopping. Two MLP architectures developed in MATLAB R2025b were compared to assess the effect of network depth on predictive performance. Model performance was evaluated using the coefficient of determination (R2) and mean squared error (MSE). The ­model with two hidden layers achieved R2 ≈ 0.58 and MSE ≈ 0.065, whereas the model with four hidden layers achieved R2 ≈ 0.86 and MSE ≈ 0.010. An ordinary multiple linear regression model, fitted to the full dataset as a reference linear analysis, showed weak explanatory power (R2 = 0.151). The results suggest that, within this exploratory dataset and the adopted hold-out procedure, the deeper MLP architecture achieved a closer fit to the available data than the shallower architecture. The findings provide a feasibility-oriented contribution to risk-informed planning in public building renovation projects, while requiring confirmation through repeated resampling and external validation.