Civil Engineering Faculty Publications
Document Type
Article
Publication Date
3-2026
Abstract
Conventional asphalt concrete has a limited lifespan due to cracking, deformation, and environmental degradation, driving the development of fiber-reinforced asphalt concrete (FRAC). However, key gaps remain in current data-driven FRAC studies due to small and homogeneous datasets, “black-box” machine learning models, and trade-offs between mechanical-sustainable performance, failing to provide a transparent understanding of features governing FRAC behaviors. This paper proposes a framework integrating explainable artificial intelligence and life cycle assessment (LCA) to advance mechanical and sustainable design of FRAC. A dataset of 2490 laboratory samples covers 15 input features and 3 mechanical outputs. Eight machine learning models, along with a voting ensemble strategy, were optimized using Genetic algorithm for hyperparameter tuning. The optimized voting ensemble achieved an average prediction performance of R2 = 0.87, RMSE = 1.09, MAPE = 11.96%, and MAE = 0.60 across the three mechanical targets, indicating robust and reliable predictive capability. SHapley Additive exPlanations (SHAP) analysis and linear non-gaussian acyclic causal inference quantified global/local feature impacts and pairwise interactions. LCA evaluated economic and environmental impacts and derived strength-normalized sustainability metrics. Finally, an interactive graphic user interface platform was developed for predictions, SHAP interpretations, and LCA outcomes. This data-driven approach establishes a paradigm for intelligent FRAC design, harmonizing mechanical performance with sustainability.
Recommended Citation
Tan, Xiao, Jianglei Xing, Soroush Mahjoubi, Pengwei Guo, Ziyao Wei, Yuan Wang, Jie Ren, Li Ai, Weina Meng, and Yi Bao. "Explainable machine learning and life cycle assessment for sustainable design of fiber-reinforced asphalt concrete." Journal of Cleaner Production 547 (2026): 147759. https://doi.org/10.1016/j.jclepro.2026.147759
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Journal of Cleaner Production
DOI
10.1016/j.jclepro.2026.147759
Comments
http://creativecommons.org/licenses/by/4.0/