文献类型：期刊文献

英文题名：Risk Management of Green Building Development: An Application of a Hybrid Machine Learning Approach Towards Sustainability

作者：Zhu, Yanqiu[1];Chen, Hongan[1];Ma, Jun[2];Pan, Fei[3]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[2]Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China;[3]Univ Shanghai Sci & Technol, Sch Management, Shanghai 200093, Peoples R China

年份：2025

卷号：17

期号：14

外文期刊名：SUSTAINABILITY

收录：;WOS:【SSCI(收录号:WOS:001535975900001)，SCI-EXPANDED(收录号:WOS:001535975900001)】；

基金：This paper was supported by the National Natural Science Foundation of China (Grant Number: 72202137, 71872111).

语种：英文

外文关键词：fuzzy analytic hierarchy process (FAHP); green building risk management; machine learning; particle swarm optimization (PSO); decision support

摘要：Despite the rapid adoption of green buildings as a sustainable development strategy, robust, data-driven approaches for assessing and predicting project risks remain limited. This study proposes an innovative hybrid framework combining the fuzzy analytic hierarchy process (FAHP), multilayer perceptron neural networks (MLPNNs), and particle swarm optimization (PSO) to quantify and forecast the impact of critical risks on green buildings' performance. Drawing on structured input from 30 domain experts in Shenzhen, China, ten risk categories were identified and prioritized, with economic, market, and functional risks emerging as the most influential. Using these expert-derived weights, an MLP was trained to predict the effects of the top five risks on four core performance metrics-cost, time, quality, and scope. PSO was applied to optimize the model's architecture and hyperparameters, improving its predictive accuracy. The optimized framework achieved RMSE values ranging from 0.06 to 0.09 and R2 values of up to 0.95 across all outputs, demonstrating strong predictive capability. These results substantiate the framework's effectiveness in generating actionable, quantitative risk predictions under uncertainty.