Enhancing green building decision-making with a hybrid fuzzy AHP-TOPSIS model for material selection
Weiran Cheng, Minglong Hu, Chaonan Wu
Abstract
Sustainable material selection is essential for minimizing environmental impact, resource depletion, and energy consumption in construction. We propose a hybrid fuzzy AHP-TOPSIS model to evaluate and rank four material alternatives based on nine sustainability criteria across three environmental, economic, and social dimensions. Fuzzy AHP determines criteria weights based on expert judgments, while TOPSIS ranks materials based on their relative closeness to the ideal sustainable solution. The results indicate that fly ash-based geopolymer concrete (GPC) ranked first ( C i = 0.885) due to its low carbon footprint and high recyclability, followed by cross-laminated timber (CLT) ( C i = 0.873), autoclaved aerated concrete (AAC) ( C i = 0.832), and recycled concrete aggregate (RCA) ( C i = 0.791). Sensitivity analysis confirmed the robustness of the rankings, demonstrating the model’s adaptability to different sustainability priorities. However, expert judgment introduces subjectivity, and integrating real-time sustainability data, such as material lifecycle emissions and resource availability updates, could enhance decision-making accuracy. This hybrid model offers a structured, transparent, and adaptable decision-making framework, ensuring transparency in the weighting process, material ranking, and overall selection methodology, thereby contributing to data-driven sustainable material selection for green building applications.