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

Yanqiu Zhu (), Hongan Chen, Jun Ma and Fei Pan
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Yanqiu Zhu: School of Business, East China University of Science and Technology, Shanghai 200237, China
Hongan Chen: School of Business, East China University of Science and Technology, Shanghai 200237, China
Jun Ma: School of Management, Shanghai University, Shanghai 200444, China
Fei Pan: School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China

Sustainability, 2025, vol. 17, issue 14, 1-28

Abstract: 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 R 2 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.

Keywords: fuzzy analytic hierarchy process (FAHP); green building risk management; machine learning; particle swarm optimization (PSO); decision support (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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