Explainable machine learning and life cycle assessment for sustainable design of fiber-reinforced asphalt concrete

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.