Research on a GA-XGBoost and LSTM-Based Green Material Selection Model for Ancient Building Renovation

This study aims to address the challenge of balancing historical preservation and sustainable material selection in ancient building renovations, particularly in regions with unique climatic conditions like Hunan Province. The research proposes a hybrid model integrating Genetic Algorithm-optimized Extreme Gradient Boosting (GA-XGBoost) and Long Short-Term Memory (LSTM) networks. The GA-XGBoost component optimizes hyperparameters to predict material performance, while the LSTM network captures temporal dependencies in environmental and material degradation data. A multi-objective optimization framework is developed to simultaneously prioritize preservation integrity and green performance. The methodology is validated through a case study on an ancient architectural complex in Rucheng, Hunan Province. Key results demonstrate that the hybrid model achieves superior accuracy in material selection, with an 18–23% reduction in embodied energy (compared to conventional AHP-TOPSIS methods) and a 21.9% improvement in prediction accuracy (versus standalone XGBoost with default hyperparameters). A multi-objective optimization framework is developed to simultaneously prioritize preservation integrity and green performance, with Pareto-optimal solutions identifying material combinations that balance historical authenticity (achieving 92% substrate compatibility) with substantial sustainability gains (18–23% embodied energy reduction). The model also identifies optimal material combinations, such as lime-pozzolan mortars with rice husk ash additives, which enhance moisture buffering capacity by 28% (relative to traditional lime mortar benchmarks) while maintaining 92% compatibility with original substrates (based on ASTM C270 compatibility tests). The findings highlight the model’s effectiveness in bridging heritage conservation and modern sustainability requirements. The study contributes a scalable and interpretable framework for green material selection, offering practical implications for cultural heritage projects worldwide. Future research directions include expanding the model’s applicability to other climate zones and integrating circular economy principles for broader sustainability impact. Preliminary analysis indicates the framework’s adaptability to other climate zones through adjustment of key material property weightings.