River sand supplies are decreasing due to overexploitation and illicit sand mining. One ton of Portland cement production (the main binder in concrete) emits about one ton of carbon dioxide into the atmosphere. Thus, this study replaced conventional cement and river sand (R sand) with recycled waste materials (fly ash and manufactured sand (M sand)). The concrete mix proportions were designed using M40 grade, and the Ordinary Portland cement (OPC) and R sand were replaced with 0–85 wt% of fly ash and 0-100 wt% of M sand. The concrete samples were tested for compressive strength after 3–90 days of curing. Furthermore, machine learning (ML) techniques were engaged to predict the compressive strength of the concrete samples using Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Gaussian Process Regression (GPR). Besides, the concrete samples containing fly ash, M sand, and R sand were characterized for microstructures and elemental compositions using SEM-EDS. The results revealed improved concrete compressive strength by incorporating fly ash and M sand. After 28 days of curing, OPC and R sand were partially replaced with 25 and 50 wt% of fly ash and M sand attained the designed strength of M 40 grade concrete. XGBoost model yielded the most accurate performance metrics for forecasting the compressive strength in training and testing phases with R² values equal to 0.9999 and 0.9964, respectively, compared to LSTM, SVM, and GPR. Thus, the XGBoost approach can be a viable technique for forecasting the strength of concrete incorporating fly ash and M sand. SEM-EDS analyses revealed compact formations with high calcium and silicon counts. Thus, the XGBoost approach can be a viable technique for forecasting the strength of concrete incorporating fly ash and M sand.

The rapid increase in global waste production, particularly Polymer wastes, poses significant environmental challenges because of its nonbiodegradable nature and harmful effects on both vegetation and aquatic life. To address this issue, innovative construction approaches have emerged, such as repurposing waste Polymers into building materials. This study explores the development of eco-friendly bricks incorporating cement, fly ash, M sand, and polypropylene (PP) fibers derived from waste Polymers. The primary innovation lies in leveraging advanced machine learning techniques, namely, artificial neural networks (ANN), support vector machines (SVM), Random Forest and AdaBoost to predict the compressive strength of these Polymer-infused bricks. The polymer bricks’ compressive strength was recorded as the output parameter, with cement, fly ash, M sand, PP waste, and age serving as the input parameters. Machine learning models often function as black boxes, thereby providing limited interpretability; however, our approach addresses this limitation by employing the SHapley Additive exPlanations (SHAP) interpretation method. This enables us to explain the influence of different input variables on the predicted outcomes, thus making the models more transparent and explainable. The performance of each model was evaluated rigorously using various metrics, including Taylor diagrams and accuracy matrices. Among the compared models, the ANN and RF demonstrated superior accuracy which is in close agreement with the experimental results. ANN model achieves R ² values of 0.99674 and 0.99576 in training and testing respectively, whereas RMSE value of 0.0151 (Training) and 0.01915 (Testing). This underscores the reliability of the ANN model in estimating compressive strength. Age, fly ash were found to be the most important variable in predicting the output as determined through SHAP analysis. This study not only highlights the potential of machine learning to enhance the accuracy of predictive models for sustainable construction materials and demonstrates a novel application of SHAP to improve the interpretability of machine learning models in the context of Polymer waste repurposing.

This study investigates the effects of strength and durability of concrete for different water-cement ratios, aggregate contents, and partial replacement of biomedical waste ash at 5%, 10%, 15%, 20%, and 25% by weight of cement. At 7, 14, and 28 days, the control mix showed inferior mechanical properties, particularly compressive strength, compared to concrete mixtures containing Biomedical Waste Ash (BWA). The replacement of cement by 5% and 10% increased the compressive strength but it is decreasing from 15%. Additionally, BWA modified concrete demonstrated a slower water absorption rate and minimal weight loss under acid test curing conditions, indicating enhanced durability. The economic and environmental benefits of incorporating biomedical waste into concrete promote sustainable construction practices. Using three machine learning approaches—K-Nearest Neighbors (KNN), Random Forest (RF), and CatBoost—the compressive strength of concrete with biomedical waste ash was simulated. Cement, biomedical waste, water absorption, slump, and the water-to-cement ratio were key input variables. Among the models tested, the RF model emerged as the most accurate, with a predictive performance of R² = 0.9945 and RMSE = 0.7080. Its unparalleled reliability, consistency, and accuracy in predicting compressive strength make it a top choice for this task.

Self-healing concrete is an innovative solution to improve durability issues in traditional concrete structures. The review focuses on a novel approach in self-healing concrete technology by partially replacing cement with agro-industrial waste, which has received less attention in existing literature. The use of agro-industrial waste aims at increasing the environmental sustainability of concrete production, it also introduces unique properties that contribute to the self-healing process. The literature of various agro-industrial waste materials like coffee husk ash (CHA), rice husk ash (RHA), sugarcane bagasse ash (SCBA), fly ash, and ground granulated blast furnace slag (GGBS), as a potential replacement for cement. The waste materials that act as supplementary cementitious materials and possess inherent healing properties due to their chemical composition. To evaluate the processes governing this precipitation, this paper discusses the impact of incorporating agro-industrial waste on bacterial concrete's mechanical, durability, and self-healing performance. The present work sheds a light on various factors of bacterial concrete such as types of bacteria and dosage, mix proportion and the outcome of mechanical and durability tests. Furthermore, the study emphasizes the need for comprehensive investigations on self-healing concrete's long-term performance and scalability with agro-industrial waste. The limited availability of studies on use for future research to explore deeper into the connection between agro-industrial waste and self-healing concrete, opening the door to more durable and sustainable building materials. Graphical abstract

The present study concentrates on the performance evaluation of calcined and uncalcined cashew nut-shell ash (UCCNA and CCNA) with treated total recycled concrete aggregate (TRCA) in self-compacting concrete. The achievement of sustainable self-compacting concrete (SCC) is possible by the implication of four stages, which includes TRCA treatment process, gradation selection process through Bailey aggregate grading technique, by considering TRCA replacement percentage with an increment of 25% and up to 100% and by considering UCCNA or CCNA replacement with an increment of 5% and up to 20%. Hardened and fresh properties of SCC have been performed and analyzed based on the compliance requirements of SCC. In addition finding results through microstructure assessment was in line with the findings of the hardened and fresh properties of SCC. In addition, quality and dynamic instability assessments of SCC were analyzed through ultrasonic pulse velocity and drying shrinkage aspects. Besides CO₂, the emission rate and the efficiency rate of SCC, composites were analyzed in detail. Overall findings revealed that CCNA-based SCC mixes performed effectively than UCCNA-based SCC; specifically, incorporation of 75% of TRCA with 15% CCNA was found to be optimal. But with regard to shrinkage performance UCCNA found to be better by imputing less shrinkage compared to CCNA-based SCC mixes. Further with regard to efficiency rate of SCC composites revealed the gain of maximum efficiency of about 0.156 MPa/kg CO₂/m³ and 0.160 MPa/kg CO₂/m³ for 15% and 20% CCNA-based SCC mixes.

In recent years, three dimensional concrete printing (3DCP) has gained traction as a promising technology to mitigate the carbon footprint associated with construction industry. However, despite its environmental benefits, studies frequently overlook its impact on social sustainability and its overall influence on project success. This research investigates how strategic decisions by firms shape the tradeoffs between economic, environmental, and social sustainability in the context of 3DCP adoption. Through interviews with 20 Indian industry leaders, it was found that companies primarily invest in 3DCP for automation and skilled workforce development, rather than solely for environmental reasons. The lack of incentives for sustainable practices in government procurement regulations emerges as a significant barrier to the widespread adoption of 3DCP. Our study identifies five key strategies firms employ to promote sustainability through 3DCP and proposes actionable measures for government intervention to stimulate its advancement. Addressing these issues is crucial for realizing the full societal and environmental benefits of 3DCP technology.