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With the rising global population and an increasing demand for enhanced built environments, the construction industry faces a critical challenge with rising greenhouse gas emissions, particularly carbon dioxide. Approaching a tipping point, these emissions could contribute to irreversible climate change, surpassing the Earth's capacity to neutralize them through the natural carbon cycle. In response, the imperative adoption of technologies capable of capturing and sequestering CO 2 becomes crucial, especially in industries like construction and building. This urgency is essential to significantly reduce the carbon footprint of cement-based material production and induce positive climate change. This review emphasizes consolidating information from recent studies on biochar derived from agro-sources used as an admixture in cement-based applications. It delves into the origins of biomass and the methods employed in its production while highlighting the advantageous impacts of biochar on carbon capture for various properties of cementitious applications. Moreover, biochar, valued for its inexpensive, carbon-efficient, and sustainability gains, has increasingly been applied in cementitious materials. This study provides an exhaustive review of the impact of engineered biochar in the development and performance of carbon-negative cement-based composites, focusing on durability, mechanical, fresh, and microstructural characteristics, as well as carbon-sequestration capacity. Notably, biochar enhances hardened biochar-cement composites' physical, durability, and microstructural characteristics, with an optimal cement replacement of 1–2% wt. Adding biochar further improves endurance against permeability, shrinkage, sulfate attacks, and chloride-induced corrosion. Biochar's potential to reduce concrete permeability is consistent across different pyrolysis temperatures. Positive effects on durability (up to 5% by wt.) are ascribed to Improved hydration and enhanced physical filling, leading to a more compact microstructure, which hinders the penetration of ions and water. • Performance of concrete can vary due to the source of biowaste. • Adding high amount of biochar in concrete leads to low mechanical strength. • Method used to produce biochar can have effect on the resultant composite. [ABSTRACT FROM AUTHOR]

• E-waste elimination. • Mechanical properties concrete incorporating E-waste. • Artificial Intelligence (AI) • ANN. • Environmental hazard. The appropriate disposal of electronic waste (E-waste) is becoming a serious concern on a global scale. The purpose of present work is to establish a link between the mix design factors and mechanical strength using the metaheuristic based artificial intelligence (AI) Technique known as gene-expression-programming (GEP). The developed dataset includes several input variables i.e., the percentage of e-waste partial substitute, water to cement ratio, specimen age, water absorption and specific gravities of the aggregates, while the compressive strength (CS), flexural strength (FS) and tensile strength (STS) are used as predictive outcome. The established models were assessed using the root mean square error (RMSE), mean absolute error (MAE), objective function, and performance index as well as the regression measure known as the coefficient of correlation (R2). All strength models showed a significant correlation (R2 = 0.94), with the minimum statistical errors (MAE 2.04, RMSE 2.54), (MAE 0.36, RMSE 0.47), and (MAE 0.43, RMSE 0.54) for CS, FS and STS respectively. Furthermore, the parametric and sensitivity analyses were considered for analyzing impact of particular input variables on the performance of outcome. The established machine learning based metaheuristic models can be utilized confidently to use e-waste concrete in a variety of construction purposes. [ABSTRACT FROM AUTHOR]

• Four ensemble M−L algorithms were employed to predict the C-S of C-M after acid attack. • A dataset of 234 points with eight input parameters was used for M−L modeling. • SHAP analysis determined the significance and interaction of input features. • Accurate predictions were made for the C-S of C-M after acid attack using M−L methods. • Bagging and random forest methods yielded more accurate results than gradient boosting and AdaBoost. This research aimed to build estimation models for the compressive strength (C-S) of cement mortar containing eggshell and glass powder after the acid attack using machine learning algorithms. A lab test data comprising 234 data points with 8 input factors was utilised for modelling. Four ensemble machine learning techniques, including gradient boosting, AdaBoost, random forest, and bagging, were employed to achieve the research's goals. In addition, to examine the influence and correlation of input factors, a SHapley Additive exExplanations (SHAP) analysis was conducted. The built estimation models well agreed with the lab test results based on R2 and the variance between actual and model estimated results (errors). Random forest and bagging exhibited superior prediction performance, with R2 of 0.982 and 0.983, respectively, than gradient boosting and AdaBoost, with R2 of 0.969 and 0.977, respectively. The comparative analysis of statistical measures also indicated superior accuracy of random forest and bagging, with mean absolute percentage error (MAPE) of 2.40%, than gradient boosting and AdaBoost, with MAPE of 2.90% and 2.60%, respectively. SHAP analysis exhibited that the highly influential factor for the acid resistance of glass and eggshell-based mortar was the 90-day C-S of the sample, followed by the quantity of glass powder, eggshell powder, sand, cement, water, superplasticizer, and silica fume. [ABSTRACT FROM AUTHOR]