Machine Learning-Assisted Prediction and Multi-Objective Optimisation of Rice Husk Ash and Micro Fine Slag Stabilised Black Cotton Soil for Sustainable Pavement Subgrades

Development of machine learning models to estimate strength characteristics of combined cellulose xanthate and fibre reinforced soft clayey soil used as subgrade

Precision Assessment of Data-Driven Supervised Machine-Learning Models for Predicting Compressive Strength of Sustainable Waste Foundry Sand Concrete

The rapid rate of urbanization and industrialization has driven the excessive use of natural resources like river sand and gravel, raising significant sustainability concerns. Waste foundry sand (WFS), a discarded by-product of ferrous and nonferrous metal casting industries, offers a promising substitute for natural sand in concrete. This study focuses on predicting the compressive strength (CS) of WFS-infused concrete by analyzing the impact of various factors, such as cement content, WFS proportion, supplementary cementitious materials (SCMs), water, aggregate composition, and superplasticizer (SP) usage. A data set comprising 401 mix ratios and their corresponding strengths was developed using systematic literature review approach and analyzed using advanced machine-learning (ML) models, including extreme gradient boosting (XGB), categorial boosting (CatB), light gradient boosting, gradient boosting, decision tree, k -nearest neighbor, adaptive boosting, bagging regressor, and random forest. The data set was divided into training and testing subsets, and statistical evaluations were performed to determine correlations between input parameters and strength. Among the models, XGB and CatB demonstrated the highest accuracy ( R 2 = 0.98 and 0.97 for training data; R 2 = 0.83 and 0.86 for testing data, respectively). Shapley additive explanations (SHAP) and partial dependence plot (PDP) analysis revealed that water content and curing age significantly enhanced compressive strength. Furthermore, the developed graphical user interface will help to practically estimate the compressive strength of WFS concrete without any experimental trials.

Machine learning-driven prediction of compressive strength in sustainable geopolymer concrete using artificial neural network and random forest models

ABSTRACT Geopolymer concrete is a sustainable substitute for ordinary Portland cement which minimizes carbon dioxide emissions and effectively utilizes the waste from industries. Proper predictive estimating compressive strength can assist in the mix deign optimization, structural reliability. The article presents a machine learning-based framework to predict the compressive strength of geopolymer concrete made with multiple industrial by-products as binders. This study investigated the subsequent strength of concrete when subjected to fly ash, ground granulated blast furnace slag, metakaolin, silica fume, and rice husk ash. A database was developed containing 243 experimentally prepared samples with different mix proportions. The study conducted the compressive strength prediction by implementing Artificial Neural Network (ANN) and Random Forest (RF) models in Python. The performance of model was analyzed through the coefficient of determination (R2) and mean absolute error (MAE) and root mean square error (RMSE). The RF model was found to be superior to the ANN model with R2 = 0.97, MAE = 1.9969, RMSE = 3.0586, which was an accurate result whereas ANN model was lower accurate R2 = 0.78. The results show that techniques using ensemble learning can capture complex non-linear relationships, reduce experimental efforts and assist in developing efficient and sustainable geopolymer concrete mix designs.

Machine Learning Prediction and Interpretation of Soil−Water Characteristic Curves of Biochar-Amended Soils

Biochar is a porous, carbon-rich soil amendment that can enhance soil water retention capacity by modifying pore structure and physicochemical properties. Understanding the soil−water characteristic curve (SWCC) of biochar-amended soils is essential for evaluating their hydrological behavior and promoting the application of biochar in engineering practice. Given the demonstrated feasibility and accuracy of machine learning methods for predicting soil parameters, this study employed six machine learning models, namely, decision tree, random forest, XGBoost, LightGBM, CatBoost, and artificial neural network, to predict the SWCC of biochar-amended soils based on a constructed dataset. Feature importance analysis and partial dependence analysis were further conducted to reveal the influence patterns of key variables. The results indicate that all six models exhibit good predictive capability, with gradient boosting models (XGBoost, CatBoost, and LightGBM) performing best. Suction is the dominant factor controlling the volumetric water content variation, while soil particle-size distribution and dry density provide the physical basis for water retention. Biochar content, pyrolysis temperature, and feedstock type further modulate the water retention capacity of amended soils. Overall, the findings demonstrate that machine learning approaches can effectively predict the SWCC of biochar-amended soils and provide insights into the controlling mechanisms of soil water retention.

Optimization of Concrete Mix Composition Containing Fly Ash and Slag Using a Machine Learning Algorithm for Compressive Strength Prediction

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.

Prediction of mechanical and durability properties of PET and rice husk ash concrete using an explainable hybrid machine learning framework

The growing need for sustainable construction materials has led to increasing interest in concrete made with recycled and waste-derived components such as polyethylene terephthalate (PET) and rice husk ash (RHA). Accurately predicting the mechanical and durability properties of such materials remains challenging due to the complex interactions among mix constituents. To address this, this study develops a hybrid machine learning framework that combines a TFT-inspired attention-based feature interaction module with XGBoost, optimized using the Aquila Optimizer (AO). The model is further enhanced with Bayesian uncertainty quantification through Monte Carlo Dropout and dual explainable AI techniques (SHAP and LIME) to improve transparency and reliability. The model is trained on an experimental dataset of ten concrete mix designs, which is expanded using Gaussian Noise Injection, SMOTER, and Conditional GAN-based augmentation to improve learning robustness. It predicts six key properties: compressive strength, splitting tensile strength, flexural strength, water absorption, modulus of elasticity, and rapid chloride permeability. The proposed framework achieves high predictive accuracy (R² = 0.9992, RMSE = 0.00205, MAE = 0.0009), outperforming benchmark models. Interpretation results indicate that PET content and cement proportion are the most influential variables, while the uncertainty analysis provides reliable confidence bounds for each prediction. The model is specifically applicable to concrete properties at a curing age of 90 days. The TFT component is used in a non-temporal manner to capture feature interactions in tabular data, making the framework suitable for complex material systems where interpretability and prediction reliability are essential.