Optimization and predictive measurement of compressive strength of iron ore slag modified concrete using data-driven supervised machine learning algorithms.
An integrated machine learning-experimental framework to predict the compressive strength (CS) of concrete incorporating ternary industrial wastes glass powder, marble powder, and iron ore slag is developed and the hybrid XGB-GBR model demonstrates the highest balanced performance.
Abstract
This study develops an integrated machine learning-experimental framework to predict the compressive strength (CS) of concrete incorporating ternary industrial wastes glass powder, marble powder, and iron ore slag. For this purpose, a dataset comprising 366 mix ratios and corresponding CS values was compiled from various sources for analysis. Advanced machine learning (ML) algorithms, including extreme gradient boosting (XGB), gradient boosting, and random forest (RF), were employed alongside hybrid techniques such as XGB-GBR and XGB-RF to evaluate the influence of these materials on strength. Based on the outcomes of the analysis, the hybrid XGB-GBR model demonstrates the highest balanced performance for both training (R2 = 0.911) and testing (R2 = 0.869) data sets. For validating the ML modeling and developing an interactive graphical user interface (GUI), experimental evaluation of CS and scanning electron microscopy was conducted. Additionally, feature importance modeling and optimization identified curing age and coarse aggregate as the most influential factors that would impact the model prediction. The contribution of this research lies in the combined modeling and experimental evaluation of a ternary waste concrete system, along with the development of a GUI. This deployable GUI will enhance the industrial applicability of ML-based concrete optimization by reducing material costs, minimizing trial batching, and supporting sustainable mix design practices.
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.
M. H. R. Sobuz, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi et al.· Journal of Structural Design...· 0 citations
Sustainable concrete incorporating waste glass materials has emerged as a promising solution to reduce environmental impacts associated with cement production and natural aggregate depletion. Accurate prediction of compressive strength (CS) is essential for optimizing such mixtures and ensuring structural reliability. In this study, five machine learning models: Random Forest (RF), K-Nearest-Neighbors (KNN), Adaptive-Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), and Extreme-Gradient-Boosting (XGBoost) were developed and optimized using Grid Search to predict the CS of concrete containing glass powder (GP) and glass sand (GS). A dataset of 270 experimental samples was utilized, incorporating eight input parameters, including curing duration, cement content, GP, GS, water, density, sand, and basalt. Among the models, LightGBM demonstrated superior predictive performance during testing, achieving a determination coefficient (R² = 0.964) and Root-Mean-Square-Error (RMSE = 2.05 MPa), followed by XGBoost (R² = 0.955) and RF (R² = 0.953). In contrast, KNN and AdaBoost exhibited comparatively lower performance. SHAP and Partial Dependence Plot (PDP) analyses identified curing duration and cement content as the most influential parameters, while water and GP exhibited negative effects on CS. To enhance practical applicability, the LightGBM model was deployed through a user-friendly GUI, enabling rapid and reliable prediction of CS and providing an accurate, interpretable, and practical decision-support tool for sustainable concrete mixture design.
Abdelrahman Shams, S. R. Wani, Eman Mousa et al.· Scientific Reports· 0 citations
This study provides a robust, interpretable, and generalizable ML framework for optimizing nano-silica concrete mix design and highlights the strong potential of ML, particularly ensemble models combined with explainable AI techniques, to improve prediction reliability, reduce trial-and-error experimentation, and support more cost-efficient and sustainable concrete design.
Yousif J. Bas, Jamal I. Kakrasul, Kamaran S. Ismail et al.· Engineering Research Express· 0 citations
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.
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
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.
