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.
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
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.
Md. Samsuzzaman Sobuz, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi et al.· Scientific Reports· 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.
Recycled aggregate concrete (RAC) is a sustainable alternative construction material to reduce natural resource exploitation and manage construction and demolition waste. However, predicting the mechanical performance of RAC remains a challenge due to the high variability of recycled aggregate properties. The purpose of this study is to develop a machine learning model to predict the compressive strength of recycled aggregate-based concrete and compare its performance with normal concrete. The dataset used consists of 2165 samples (1600 normal concrete and 565 recycled aggregate concrete) collected from various scientific publications. Three tree-based machine learning algorithms (Random Forest, XGBoost, and LightGBM) were implemented and optimized using RandomizedSearchCV with 5-fold cross-validation. The results showed that LightGBM provided the best performance with R² = 0.92, MAE = 2.45 MPa, and RMSE = 3.52 MPa on the test set. This model is able to predict the compressive strength of normal concrete (R² = 0.92) and recycled aggregate concrete (R² = 0.91) with almost the same accuracy, indicating strong generalization. Feature importance analysis revealed that curing age, cement content, and water content are the most important factors in compressive strength prediction, while for RAC, recycled aggregate water absorption (WRCA) also makes a significant contribution. Error analysis shows that residuals are random and normally distributed without systematic bias. This model can reliably predict concrete compressive strength in the range of 20-60 MPa with an average error of ±3-4 MPa and can be integrated into mix proportioning design software to improve the efficiency of the design process and support the use of sustainable construction materials.
Suji’at, Eko Wahyu Abryandoko, Ocha Silvia Kencana et al.· Journal of Novel Engineering...· 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.
Compressive strength is the single most important design parameter governing the safety, serviceability, and economy of concrete structures, yet its determination through standard 7-, 14-, or 28-day destructive cylinder/cube testing is slow, costly, and unable to assess concrete already cast in place. This study develops and evaluates a Random Forest (RF) regression model to predict the compressive strength of concrete directly from eight standard mix-design parameters — cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and curing age — using Yeh's (1998) benchmark dataset of 1,030 experimentally tested concrete mixtures. Following data cleaning, exploratory correlation analysis, an 80:20 train-test split, and five-fold GridSearchCV hyperparameter tuning, the optimized Random Forest model is benchmarked against Linear Regression, Ridge Regression, and Support Vector Regression using the coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The Random Forest model achieves the strongest predictive performance of the models tested, substantially outperforming the linear baselines and confirming that concrete strength development is governed by non-linear interactions among mix constituents. Feature importance analysis further shows that curing age and cement content are the dominant predictors, while water content exerts a clear negative influence consistent with Abrams' Law, and coarse/fine aggregates contribute comparatively little, consistent with their role as largely inert fillers. These findings demonstrate that Random Forest regression offers a fast, accurate, and interpretable, non-destructive alternative to conventional strength testing, with practical value for mix-design optimization, quality control, and early-stage structural decision-making.
M. Selvakumar, S. Geetha, P. K. Kumar et al.· International journal of com...· 0 citations