There is growing interest in the use of recycled materials in the building sector as a way to reduce waste and improve environmental sustainability. Polyethylene terephthalate (PET) is a thermoplastic polymer that has attracted the attention of researchers due to its application in the building industry. In the recent past, many studies have reported on the application of PET aggregates in structural lightweight concrete. This paper presents a review of the findings of 20 studies conducted between 2010 and 2022 randomly. The preliminary findings highlighted include the method of production of PET aggregates and their physical/thermal properties. The review extended further to focus on the extent of incorporation, physical properties, strength properties, and durability of concrete with PET aggregates. The substitution of PET aggregates up to 20% reflected positively on the compressive strength, while tensile and flexural strength had positive responses up to 10%. Water absorption as a measure of concrete durability increased with the addition of PET aggregates. A meta-analysis of these findings was performed using hypothesis testing (t-test and f-test) to identify significant differences between the experimental outcomes of PET incorporation in concrete. Experimental procedures with greater tolerance for PET inclusion and satisfactory concrete properties were highlighted. The paper recommends the formulation of hybrid mix proportions, developed from experimental designs with noteworthy inclusion of PET aggregates and those that attained high values of desired concrete properties. Furthermore, optimization should be performed to provide robust mix designs for high-strength or lightweight concrete with PET aggregates.

This paper investigates the effect of the size and shape of coarse aggregates on the compressive strength of concrete. Concrete strength is affected by the surface texture, grading and maximum aggregate size. Six different sizes of coarse aggregate have been selected for both angular and rounded coarse aggregate. The coarse aggregates were used in the production of concrete and tested for workability, density and compressive strength. The specimen was cured for 3, 7, 14, 21 and 28 days by full water immersion. The results indicated that under the same curing conditions and water-cement ratio, the compressive strength of concrete produced with both angular and rounded aggregates increased with increasing aggregate size, up to an aggregate size of 14 mm. The optimum compressive strength of 27.58 N/mm2 and 25.88 N/mm2 were achieved at 28 days curing and 14 mm aggregate size for concrete with angular and rounded aggregates respectively. Coarse aggregates with angular shape result in concretes with better compressive strength than coarse aggregates with a rounded shape. The model equation developed to predict the compressive strength of rounded aggregate has R2 value of 95.66%, and the higher the value of R2, the better the model fits the data.

The effect of Metakaolin and ground Scoria on various properties of concrete were investigated and optimised using Response Surfaces Methodology (RSM) in this study. Seven batches of concretes were cast at water to cement ratio of 0.5 and 5% fixed Metakaolin with 0, 5, 10, 15 and 20% Ground Scoria replaced cement. The resulting concrete then was tested for Slump. The Concrete cubes were cast and cured for 3, 7, 14, 28 and 60days before Water absorption and Compressive strength test were carried out. at all replacement levels of Metakaolin/Scoria content, Workability, Water absorption, Density and Compressive strength decreased when compared with the control concrete. However, Water absorption, Density and Compressive strength slightly increased with increase in curing age. The models developed were quite accurate as the percentages of error were in a good agreement and can explain the variability in Metakaolin/Ground Scoria concrete. Numerical method of optimisation was applied to determine the optimum mix proportions for Metakaolin/Ground Scoria. The optimum mix of concrete was obtained by addition of 8.60% GS after curing for 12-days with 0.8 desirability. Based on the result of optimisation, incorporating the optimum values of 13.6% (5%MK+8.6%GS) metakaolin and ground Scoria in concrete as cement substitute for every one kilogram of cement, can potentially result in reduction of CO2 emissions by 0.07-0.1224kg.

Background: The use of incinerated bottom ash (IBA) as a sustainable construction material offers potential environmental benefits but introduces complex interactions with cement chemistry. Magnesium phosphate cement (MPC), known for its rapid hardening and superior bonding, can be optimized through the controlled incorporation of IBA. However, limited studies have addressed how the chemical components of IBA affect the compressive strength of MPC, particularly using data-driven approaches. Methods: A database of 396 experimental samples was compiled from previous studies considering mix proportions, oxide compositions, and curing conditions. Four ensemble machine learning algorithms—Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), Gradient Boosting Regressor (GBR), and Random Forest (RFR)—were employed to predict compressive strength. Model robustness was validated through 5-fold cross-validation. Feature interpretation was achieved using SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) to quantify individual and interactive effects of chemical and physical parameters. Results: The XGB model achieved the highest predictive accuracy, with mean training and testing R2 values greater than 0.90 and 0.80, and the lowest mean absolute percentage error of 16.71%. SHAP analysis identified curing age as the most dominant factor, followed by FA/C, W/C, and MgO/PO4 ratios. IBA content and specific oxides such as Fe2O3 and Al2O3 contributed positively to strength within optimal ranges. PDP confirmed nonlinear dependencies, indicating a 26% reduction in strength as W/C increased from 0.1 to 0.6, while extended curing up to 28 days improved performance substantially. Conclusion: The integration of SHAP and PDP provided a transparent interpretation of feature interactions in IBA-modified MPC. The developed XGB model demonstrated strong generalization and interpretability. The combined modeling approach offers a reliable predictive framework for optimizing IBA incorporation in sustainable binder systems and advancing eco-efficient material design.

The production of waste marble is the ultimate concern of the construction industry's recent success in satisfying the huge need for marble stones. This study predicts the compressive strength of waste marble powder concrete using machine learning methods XG Boost, AdaBoost, Cat-Boost, Gradient-Boosting, Light Gradient-Boosting, and decision tree. For this purpose, a comprehensive dataset was developed from previously published literature, incorporating input features such as cement, fine and coarse aggregates, superplasticizers, silica fume, marble dust, and water, with compressive strength (CS) as the output parameter. Based on the outcomes, XG Boost model outperforms other models to predict CS with R 2 values of 0.999 and 0.915 on the train and test stage, respectively. The decision tree model also shows consistent performance with R 2 > 0.85 for both train and testing phases. In addition, experimental assessment was also conducted to verify the outcomes of machine learning (ML) modeling and quantify the CS of concrete specimens having marble dust as binder replacement (5-20% by weight). In addition, feature importance analysis highlights that superplasticizers, cement, and silica fume were the most weighted factors that affected CS. According to partial dependency plot (PDP) analysis, combined data on concrete strength versus cement concentration shows that the strength of the material increases from 37.5 MPa to 57.5 MPa when the cement concentration increases from 300 kg/m 3 to 500 kg/m 3. Furthermore, microstructural evaluation revealed a dense and well-compacted matrix in concrete containing waste marble powder, with improved bonding between the binder and aggregates.

This study presents a hybrid approach combining experimental evaluation and machine learning modeling for the compressive strength (CS) estimation of natural fiber-reinforced concrete (NFRC), which utilizes jute, coir, and bamboo fibers. A dataset of 444 concrete mix designs was compiled from the literature using seven input variables. Six advanced machine learning (ML) algorithms: Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGB), Random Forest, Gradient Boosting, Decision Tree, and K-Nearest Neighbors were trained and tested with proper hyperparameter optimization. Among these, the LGBM model demonstrated superior accuracy in prediction with an R² of 0.8637 and the lowest root mean square error of 4.19 MPa on the testing sets. To ensure interpretability and mix design optimization, SHapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses were incorporated. The dominant predictors of CS were found to be cement content, water, coarse aggregate, and supplementary cementitious materials, while the curing period and fiber content also showed a small but meaningful effect. To further validate the modeling outcomes, experimental investigations were conducted by developing 10 mix combinations with varying percentages of coir fiber, which were then subjected to compressive strength and scanning electron microscopy tests. The outcomes also validated that the incorporation of natural fiber up to 0.75% gradually increased the CS to a maximum of 41.3% after 28 days of curing, while further addition reduced performance. The dual-approach-based (ML and experimental) outcomes could assist in the sustainable advancement of the infrastructure industry as a potential solution for cost-effective, large-scale production of NFRC.