Jul 2026· International Journal of Creative and Open Research in Engineering and Management· Vol 02, pp. 1-9· 0 citations
Abstract
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
There has been a rise in the need of concrete leading to high consumption of cement and emission of carbon. Sustainable concrete incorporating supplementary cementitious materials (SCMs) is an alternative that is eco-friendly, but the mechanical behavior is complicated and hard to predict using the traditional tests. The work presents a framework involving machine learning because of predicting the mechanical properties of sustainable concrete, namely, compressive, split tensile, and flexural strength. Four models such as Linear Regression, Support Vector Regression, Random Forest, and Artificial Neural Network were constructed based on a data of nearly 1000 sustainable concrete mixes. To estimate the model performance, R 2, RMSE and MAE were used. The findings indicated that ANN and RF had the greatest prediction accuracy. The feature analysis established the most influential factors to be water content, cement dosage, replacement ratio of SCM, and curing age. The mix design approach proposed here is a fast, economical, and sustainable approach to the design of concrete mix.
Arti Chouksey, Santosh Reddy P, P. S et al.· 2026 International Conferenc...· 0 citations
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.
Abdullah Asiri· Rocznik Ochrona Srodowiska· 0 citations
The increasing demand for sustainable construction materials has led to the selection of supplementary cementitious materials (SCMs) to minimize the environmental burden of Portland cement manufacturing. This paper experimentally investigates the effect of varied volume of fly ash replacement on some fresh, mechanical and physical properties of M25-grade concrete and also develops machine learning-based models to predict performance. In this study, examining the effect of different fly ash replacement levels (0%-50%) on hardened concrete properties, six distinct concrete mixes were designed with constant water-to-cementitious material ratio (0.48) and 144 cylindrical specimens were casted from all mixtures to determine compressive strength, splitting tensile strength, workability and unit weight at age of 7, 14 and 28 days respectively. Although increasing fly ash content improved workability, high replacement levels reduced compressive and splitting tensile strength because of cement dilution and the relatively slow early-age pozzolanic reaction of Class F fly ash. Containing approximately 20–30% replacement levels, the best all-round performance with moderate compressive and tensile strength alongside cement mass reduction. It also employs the Random Forest and Extreme Gradient Boosting (XGBoost) regression models to predict compressive strength of a mixture from its variables. The prediction performances of the XGBoost model were more accurately represented (R² = 0.951; RMSE=1.128 MPa; MAE=0.966 MPa). The findings demonstrate the potential of ensemble-learning models to support preliminary fly ash concrete mix evaluation, although validation using larger independent datasets is necessary.
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 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.
Pololy Pradeep Kumar, V. Giridhar, B. Rajini et al.· Discover Civil Engineering· 1 citation
The high strength and sustainable construction of high strength concrete with low environmental impact have been the focal point of current research due to the high carbon footprint of ordinary Portland cement. The use of supplementary cementitious materials, such as ground granulated blast furnace slag (GGBS) and silica fume, in the production of high strength concrete is a common approach that increases the mechanical properties of the concrete while enhancing its sustainability. However, the interaction between the binder, the aggregate, and the properties of the concrete can be complex, and therefore the conventional trial-and-error approach for the design of concrete mixtures can be time-consuming and not effective. In this study, a machine learning framework was developed for the prediction and design of high strength concrete that contains GGBS and silica fume, and takes into account the detailed characteristics of the aggregate. A comprehensive database that contains information on the mixture proportions, the curing conditions, the properties of the binder, and the characteristics of the aggregate, including the grading curve, the specific gravity, the water absorption, the crushing strength, the impact strength, and the shape indices of the aggregate, was created and used for the training and the evaluation of the models. Four machine learning models, namely the artificial neural network model, the support vector machine model, the random forest model, and the gradient boosting model, were used to predict the performance indicators of the concrete. The performance indicators of the models were evaluated by using the coefficient of determination (R2), the mean absolute error (MAE), and the root mean square error (RMSE). In addition, an explainable artificial intelligence approach was used to investigate the effects of the input variables on the predicted strength of the concrete, and to identify the interactions between the variables. The results of the models were also used for a multi-objective design approach in order to find the optimum mixture composition that satisfies the strength, workability, cost, and environmental impact of the concrete. The proposed data-driven approach provides an efficient and effective design methodology for sustainable high strength concrete, and highlights the significance of the characteristics of the aggregate on the properties of GGBS–silica fume based concrete systems.
Sushma S, Dr. P S Lakshmi, Dr. Naveen Kumar S et al.· International Journal of Sci...· 0 citations