Durability-informed life cycle assessment of concrete through machine learning for service life prediction

Sustainable concrete infrastructure cannot be achieved through prescriptive mix design or carbon accounting frameworks that neglect material deterioration. Despite advances in durability science, machine learning (ML), service-life modeling, and life-cycle assessment (LCA), these domains remain misaligned because they quantify performance using incompatible metrics. Durability research characterizes performance through transport-controlled degradation processes; ML infers performance statistically across heterogeneous datasets; service-life analysis defines performance by the timing of corrosion limit states; and LCA evaluates performance using functional units (FUs) often decoupled from degradation…