The cement industry is a major contributor to global carbon dioxide (CO₂) emissions, necessitating the development of sustainable construction materials with reduced environmental impact. This study proposes a novel integrated framework that combines industrial waste-based low-carbon concrete development with machine-learning-driven strength prediction and mix optimization. Low-carbon concrete mixes were produced by partially replacing ordinary Portland cement with fly ash and ground granulated blast furnace slag (GGBS) at various replacement levels. Experimental investigations were conducted to evaluate compressive strength development at different curing ages. A dataset comprising 300 experimental observations was subsequently employed to develop and compare three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost), for compressive strength prediction. The results demonstrate that appropriately designed low-carbon concrete mixtures can achieve comparable or superior long-term strength while substantially reducing cement consumption and associated CO₂ emissions. Among the evaluated models, XGBoost exhibited the highest predictive accuracy, indicating its suitability for sustainable concrete mix optimization. The novelty of this study lies in integrating experimental low-carbon concrete design, environmental assessment, and advanced machine learning techniques within a unified framework to enhance structural performance and sustainability simultaneously. The proposed approach provides an efficient pathway to reduce trial-and-error experimentation and accelerate the adoption of eco-friendly concrete in modern construction practices.
The rapid rise in global population and industrial activity has intensified environmental challenges, particularly carbon dioxide (CO₂) emissions from the cement and concrete industry. Biochar, a carbon-rich byproduct of biomass pyrolysis, has emerged as a promising solution for sustainable construction by enhancing carbon sequestration and improving mechanical performance when partially substituting cement. This study integrates experimental evidence with advanced machine learning (ML) techniques to evaluate the compressive strength, cost-efficiency, and carbon footprint of biochar-incorporated concrete. A comprehensive dataset of nine input parameters, including cement, aggregates, silica fume, fly ash, biochar, water, superplasticizer, and curing age was modeled using multiple ML approaches. Among the models tested, the hybrid XGB-Histogram Gradient Boosting (XGB-HistGB) model consistently achieved the best overall performance, with a testing R2 of 0.958, the lowest mean absolute error (3.03), and minimal prediction bias. This model outperformed standalone algorithms and other hybrids, providing reliable accuracy across compressive strength, cost, and embodied CO₂ predictions. SHAP and partial dependence analyses confirmed fine aggregate, curing age, and superplasticizer as the most influential parameters, while biochar dosage required careful optimization to balance strength retention with sustainability benefits. A user-friendly graphical interface was also developed, enabling real-time prediction of compressive strength, material cost, and CO₂ emissions based on user-defined mix proportions. Overall, the findings demonstrate that biochar can be effectively integrated into sustainable concrete formulations, and the XGB-HistGB model offers a powerful AI-driven predictive framework to optimize both structural performance and environmental outcomes.
M. Uddin, Md. Samsuzzaman Sobuz, Mohamed Ghalla et al.· Scientific Reports· 0 citations
The increasing demand for sustainable construction materials has accelerated the incorporation of industrial by-products such as fly ash, ground granulated blast furnace slag (GGBFS), silica fume, rice husk ash, marble dust, and waste glass powder into composite concrete. These supplementary cementitious materials not only reduce environmental impacts associated with cement production but also enhance specific mechanical and durability properties of concrete. However, predicting the mechanical performance of composite concrete remains a complex challenge due to the nonlinear interactions among constituent materials, curing conditions, mix proportions, and environmental factors. Recent advancements in machine learning (ML) have provided innovative solutions for accurately predicting concrete properties while minimizing experimental costs and time.
This study presents a comprehensive review and conceptual framework for machine learning-based prediction of mechanical properties of composite concrete incorporating industrial by-products. A systematic examination of recent studies published between 2018 and 2026 is conducted to evaluate the effectiveness of various ML algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting Machines (GBM), Extreme Gradient Boosting (XGBoost), and Deep Learning models. The findings reveal that ensemble learning techniques frequently outperform traditional statistical methods, achieving prediction accuracies exceeding 90% in several applications. Furthermore, the review identifies critical challenges such as data scarcity, lack of standardized datasets, model interpretability issues, and limited real-world implementation.
The study proposes a conceptual framework integrating sustainable concrete design with advanced machine learning methodologies. It highlights future research opportunities involving explainable artificial intelligence, hybrid optimization algorithms, digital twins, and Internet of Things (IoT)-enabled monitoring systems. The research contributes to sustainable construction practices by demonstrating how intelligent prediction models can support material optimization, resource conservation, and reduced carbon emissions in the construction industry.
Keywords: Machine Learning, Composite Concrete, Industrial By-Products, Mechanical Properties Prediction, Sustainable Construction, Artificial Intelligence, Smart Materials
Ruchita K. Ingole· International Journal of Cre...· 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
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
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.
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.
