Restoring Pugin: Toward Predictive Conservation of Historical Buildings Using a Digital Twin Approach

Conservation of historic buildings has long relied on traditional, reactive methods that address deterioration only after it occurs, often leading to irreversible damage. This study introduces an innovative approach that integrates Digital Twin (DT) technology with advanced machine learning algorithms to enable predictive and data-driven conservation. Focusing on Nottingham Cathedral, a Grade II listed Gothic Revival building, this research developed a 3D Historic Building Information Model (HBIM) enhanced with real-time environmental monitoring of temperature, humidity, and air quality. The collected data were analysed using MATLABR2024a to train and evaluate several predictive algorithms, including Long Short-Term Memory (LSTM), Backpropagation Neural Network (BPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Nonlinear Autoregressive Exogenous (NARX) models. The NARX model achieved the highest accuracy (Root Mean Square Error (RMSE) = 0.19) in forecasting indoor environmental conditions. Findings indicate that maintaining an indoor temperature increase of 4–6 °C can effectively reduce relative humidity below 60%, minimising deterioration risks. The study demonstrates how integrating DT and machine learning offers a proactive framework for environmental optimisation and long-term preservation of heritage assets, moving conservation practice from reactive restoration toward predictive conservation.