Toward Eco-Intelligent Concrete for Resilient Urban Infrastructure: Explainable Surrogate Optimization and LLM-Assisted Low-Clinker Mix Design

Abstract

Concrete mix design increasingly requires rapid screening of mixture proportions against mechanical and resource-efficiency targets. This study develops an explainable, cement-reduction-oriented computational screening framework based on the UCI Concrete Compressive Strength dataset. The dataset records Portland cement, fly ash, blast furnace slag, aggregates, water, superplasticizer, curing age, and compressive strength, but does not report clinker factor, material-specific emission factors, or durability performance. Cement dosage is therefore minimized only as a surrogate objective; the study does not claim quantified embodied-carbon optimization. A LightGBM surrogate was trained using raw mixture variables and domain-informed ratios. On the held-out test set, the model achieved an R2 of 0.946 and a mean absolute error of 2.639 MPa. Shapley Additive Explanations were used to examine the statistical influence of mixture variables, with the water-to-binder ratio emerging as the dominant predictor. Constraint-filtered Monte Carlo sampling and non-dominated sorting were then used to screen Pareto-efficient binder allocations. For a 28-day target of 45 MPa, repeated searches identified candidates with a mean Portland cement dosage of 164.4 kg/m3, 44.2% below the mean of empirical mixtures in the same strength band. The evaluated numerical modules were embedded in a Streamlit prototype in which a DeepSeek large language model performs only intent parsing and report generation. The main contribution is this tool-augmented separation of language interaction from deterministic engineering computation. The resulting mixtures remain computational candidates and should next be validated experimentally and assessed using material-specific life-cycle carbon and durability data.