Aug 2026· Materials· Vol 19, pp. 3497· 0 citations· 39 references
Medicine
TL;DR
An inverse mix design framework combining machine-learning forward prediction with grey wolf optimization with laboratory validation showed the method’s feasibility, and supports sustainable pavement engineering by facilitating higher steel slag utilization, contributing to CO2 reduction and natural aggregate conservation.
Abstract
Pavement mix design for steel slag relies largely on empirical Marshall tests requiring numerous specimens and lengthy cycles. To address this, an inverse mix design (IMD) framework combining machine-learning forward prediction with grey wolf optimization (GWO) was developed. A dataset of 300 samples with 13 input features and 2 output indicators was compiled. Three algorithms—XGBoost, CatBoost, and random forest (RF)—were compared, and model interpretability was analyzed using SHAP and ALE. CatBoost achieved the best overall performance. SHAP identified steel slag f-CaO content and replacement ratio as the dominant factors governing moisture susceptibility. GWO search errors for all three design scenarios were below 0.24%. Laboratory validation showed a mean deviation of 1.02% between target and measured values, confirming the method’s feasibility. The method also supports sustainable pavement engineering by facilitating higher steel slag utilization, contributing to CO2 reduction and natural aggregate conservation.
A Streamlit-based graphical user interface was developed to enable real-time prediction and MS–ITS trade-off visualization, providing a reference for preliminary mix design of asphalt concrete, and results indicate that mineral fibers are more suitable for improving the balanced performance of MS and ITS.
J. Xing, Xiao Tan, Mu Guo et al.· Materials· 0 citations
This study applies several machine learning models to predict the Marshall stability of Stone Mastic Asphalt mixtures incorporating steel slag as a partial replacement for conventional coarse aggregates, with CatBoost providing the best performance.
T. Nguyen, Hoang-Long Nguyen, N. Trần et al.· Journal of Science and Trans...· 0 citations
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.
Junyi Zhang, Haidong Yang, Guo Hu et al.· Buildings· 0 citations
Global demand for sustainable construction materials and concerns about environmental pollution from by-products of manufacturing industries have intensified research for viable alternatives to aggregates in concrete production. This research investigated steel slag aggregate (ssa) as a partial replacement for coarse aggregates. Samples of 150 mm concrete cubes and 100 x 100 x 500 mm prisms were prepared at a 1:2:4 mix ratio and a water-cement ratio of 0.5, with ssa replacing coarse aggregates at 0%, 15%, 30%, 45%, and 60% by weight for 7, 14, and 28 days compressive and flexural strength tests. In addition, a validated random forest (rf) and multiple linear regression (mlr) algorithm were developed to predict the compressive strength of samples. The analysis of ssa for x-ray fluorescence (xrf) revealed a high 34.125% silicon dioxide (sio₂) composition and a minimum of 0.196% for strontium oxide (sro), a specific gravity of 3.07, 1680 kg/m³ bulk density, 21.28% and 8.37% aggregate crushing value and impact value were evaluated, respectively. The 15%, 30%, 45%, and 60% ssa replacement samples exhibited improved strengths of 15.63 n/mm², 17.07 n/mm², 18.30 n/mm², and 19.10 n/mm², while the flexural strengths increased up to 45% ssa (4.45 n/mm²) before declining at 60% (3.85 n/mm²). The mean absolute error (mae), mean square error (mse) and a coefficient of determination (r²) for rf were 1.20, 2.07 and 0.85, while mlr recorded 1.46, 3.77 and 0.72, respectively. The xrf suggests an improved aggregate bonding potential, and the physical characterisation revealed that ssa was within the acceptable limits for structural applications. The compressive and flexural strengths increased with ssa content up to 45%, after which strength properties declined, offering optimal mechanical performance. The mlr achieved a robust prediction accuracy with an r² value of 0.85. In conclusion, the research supports the potential of industrial by-products in promoting greener and more cost-effective construction practices
Akintayo Adeniji, W. Kupolati, Everardt A. Burger et al.· Kufa journal of Engineering· 0 citations
Recycled aggregate concrete (RAC) mix design requires simultaneous consideration of mechanical performance, environmental impacts, and economic costs, yet these objectives are often evaluated separately. This study developed an integrated framework combining machine-learning-based strength prediction, life-cycle assessment, life-cycle cost analysis, constrained three-objective optimization, and preference-sensitive decision analysis. Using 407 RAC mixtures, Optuna-tuned Random Forest, XGBoost, and LightGBM models were compared, and SHAP was applied for interpretation. LightGBM achieved the best test performance, with an R2 of 0.8822, an RMSE of 4.0276 MPa, and an MAE of 2.8865 MPa. The water-to-cement ratio, sand ratio, and superplasticizer dosage were the three leading predictors, together accounting for 68.5% of the normalized SHAP importance. A 100-generation NSGA-II optimization produced 150 feasible Pareto solutions spanning 33.87–75.25 MPa in compressive strength, 456.80–616.38 CNY/m3 in life-cycle cost, and 248.84–395.00 kg CO2e/m3 in net carbon emissions. Higher-strength solutions generally required more cement and lower water-to-cement and recycled aggregate replacement ratios. Equal-weight TOPSIS selected P006, whereas the SMAA–TOPSIS simulation identified P007 as the alternative with the highest first-rank acceptability of 35.92%. By treating compressive strength as an explicit objective rather than a predefined constraint, the framework maps the continuous strength–cost–carbon trade-off within a volumetrically feasible mix-design space and identifies preference-dependent RAC design strategies.
The modern construction industry faces significant challenges in developing sustainable concrete materials while maintaining structural quality requirements. Conventional trial-and-error methods for concrete mix design are time-consuming, costly, and often result in high variability in concrete quality. This study presents an integrated framework that combines machine learning techniques for concrete compressive strength prediction with genetic algorithm optimization to determine optimal mix compositions containing fly ash and blast furnace slag. Two predictive models were developed using the UCI Machine Learning Repository concrete dataset comprising 1,030 samples: Artificial Neural Network (ANN) Ensemble and Support Vector Regression (SVR). The ANN model demonstrated superior performance, achieving R² values ranging from 0.7475 to 0.8372, RMSE values between 6.11 and 7.94 MPa, and classification accuracy of 86.92% for concrete quality categorization across three classes (Class I: <20 MPa, Class II: 20-35 MPa, Class III: >35 MPa). In comparison, the SVR model achieved competitive but slightly lower performance with R² values of 0.7491-0.8378 and classification accuracy of 80.37%. The stability and generalizability of both models were confirmed through five-fold cross-validation. Subsequently, genetic algorithm optimization was applied to determine optimal mix compositions for each quality class while ensuring compliance with Indonesian National Standards (SNI 2847:2019, SNI 2461:2011, and SNI 8297:2016). The optimization process successfully produced concrete mix designs that achieved target compressive strengths of 14.95 MPa for Class I, 27.48 MPa for Class II, and 59.99 MPa for Class III. This framework demonstrates significant potential for developing sustainable concrete with optimal performance while meeting applicable technical standards, thereby contributing to a reduced carbon footprint in the construction industry through strategic utilization of supplementary cementitious materials.
