The exponential growth in industrialization, urbanization, and population has subsequently increased the accumulation of different wastes to hazardous levels. Among these, electronic waste (E-waste) poses a serious threat to the environment with its production rising due to technological advancements worldwide. Therefore, recycling E-waste as an alternate aggregate replacement material in the construction industry can be advantageous in managing this waste stream. This study aims to present a comprehensive insight into the integrated applications of E-waste concrete composite materials in the construction industry including applications to increase the environmental sustainability of concrete structures. This review starts with an illustration of the environmental issues caused by E-waste, elucidates its sources and prevailing recycling practices, and investigates its utility as supplementary cementitious materials. This discussion is further followed by an analysis on recycled E-waste plastic aggregate concrete (RPAC) composites, covering their fresh properties, mechanical properties, thermal properties, durability, and serviceability performance. Besides, this paper also explores the evaluation of the utilization of E-waste fibers (E-fiber) as well as the application of E-waste glass (E-glass) from cathode ray tubes (CRT) in concrete. Based on the detailed review, the positive aspects and the restrictions associated with utilizing E-waste in construction have been highlighted.

The addition of powders from waste construction materials as partial cement substitute in concrete represents a significant step toward green concrete construction. High temperatures have a substantial influence on concrete strength, resulting in a reduction in mechanical properties. The prediction of the impacts of waste powders on concrete strength is an important topic in sustainable construction. Such models are needed to understand the complex interactions between waste materials’ powders and concrete strength. In this study, three machine learning approaches, extreme gradient boosting (XGBoost), random forest (RF), and M5P, were used for constructing the prediction model for the impact of elevated temperatures on the compressive strength of concrete modified by marble and granite construction waste powders as partial cement replacements in concrete. Dataset of 324 tested cubic specimens with four input variables, waste granite powder dose (GWP), waste marble powder (MWP), temperature (T), and duration (D) were chosen for developing the prediction models. The output was the concrete compressive strength (CS). MWP and GWP ranged between 0 and 9%, temperatures were ranged between 25 °C and 800 °C, and duration up to 2 h. Hyperparameters in the RF and XGB models were optimized using grid search. K-fold cross-validation and several statistical measures, including R²MAPE, RMSE, and MAE, were utilized to validate and check the accuracy of the proposed models. The developed models were evaluated against experimental data and previously established models. The XGB model demonstrated the highest R² of 0.9989, alongside the lowest prediction errors: MAE of 0.1351 MPa, RMSE of 0.1842 MPa, and MAPE of 0.48%. The results showed that the XGB prediction model for the concrete compressive strength outperformed the other proposed models. The SHAP analysis, Individual Conditional Expectation (ICE), and Partial Dependence Plots (PDP) revealed that GWP and MWP positively influence the compressive strength, while the temperature exerts the most negative influence on predicting the compressive strength. Finally, a graphical user interface (GUI) for the compressive strength of concrete containing GWP and MWP subjected to elevated temperatures has been created, which may be of considerable assistance, guidance, and efficiency in research and construction industry contexts.

Bentonite is one of the SiO₂-rich pozzolanic clay types that has been enormously employed as a cost-effective and eco-friendly supplementary cementitious material in ordinary Portland cement (OPC) concrete. However, the use of bentonite in geopolymer concrete (GPC) has not been explored very widely. Further, the research available on the effect of utilizing treated bentonite in GPC is limited. The practical application of GPC is also very limited due to its significant shrinkage and high brittleness compared to OPC concrete. There are several studies available that have highlighted the use of polypropylene fibers (PPF) in improving the mechanical properties of GPC by reducing drying shrinkage and enhancing ductility. However, the effect of PPF on the durability properties of GPC needs to be addressed. Further, the effect of the combined integration of bentonite and PPF on the mechanical and durability properties of GPC has not been reported yet. The aim of this study is, therefore, to investigate the individual and combined effect of bentonite and PPF on the workability, mechanical properties, and durability of fly ash (FA)-based GPC. Bentonite replaced 10% of FA, and PPF was added at varying proportions (0.5%, 0.75%, and 1%) for raw and treated bentonite. Slump test was used to assess workability, while compressive, tensile, and flexural tests were utilized to evaluate the mechanical properties. Water absorption, acid attack, and abrasion resistance tests were used to evaluate durability. The results showed that bentonite and PPF significantly enhance mechanical properties, especially when combined with treated bentonite, with the highest improvement observed for mixtures with 1% PPF. The compressive strength was improved by an extent of 10% and 18% for raw bentonite-GPC and treated bentonite-GPC, respectively, compared to the control mix without bentonite. The durability test results revealed that water absorption of raw and treated bentonite-GPC mixtures at the age of 90 days was decreased by 16% and 21%, respectively, compared to the control mix (without bentonite). The mass loss of raw and treated bentonite-GPC mixtures in sulphuric acid solution was 5% and 10% lower, respectively, than the control mix (without bentonite). The mass loss of raw and treated bentonite-GPC mixtures in abrasion resistance tests was 6% and 12% lower, respectively, than the control mix (without bentonite). For durability performance, mixtures with 0.5% PPF perform the best, while higher PPF contents negatively impact the GPC durability.

The construction industry’s development trend has resulted in a large volume of demolished concrete. Improving the efficiency of the proper use of this waste as a recycled aggregate (RA) in concrete is a promising solution. In this study, we utilized response surface methodology (RSM) and three machine learning (ML) techniques—the M5P algorithm, the random forest (RF) algorithm, and extreme gradient boosting (XGB)—to optimize and predict the compressive strength (CS) of RA concrete containing fly ash (FA), silica fume (SF), and polypropylene fiber (PPF). To build the models, the results regarding 529 data points were used as a dataset with varying numbers of input parameters (out of a total of ten). The CS quadratic model under RSM exhibited acceptable prediction accuracy. The best CS was found with a 100% volume of RA consisting of coarse aggregate, 1.13% PPF by volume of concrete, 7.90% FA, and 5.30% SF as partial replacements of binders by weight. The XGB model exhibited superior performance and high prediction accuracy, with a higher R² and lower values of errors, as depicted by MAE, RMSE, and MAPE, when compared to the other developed models. Furthermore, SHAP analysis showed that PPF had a positive impact on predicting CS, but the curing age and superplasticizer dose had the highest positive impact on predicting the CS of RA concrete.

This study experimentally investigates the radiation shielding performance, characterized by the linear attenuation coefficient (LAC) values, of concrete mixes incorporating lead monoxide (NL), nano-magnetite (NM), and nano-granodiorite (NG) as partial cement replacements (1–5%) under ambient and elevated temperatures (400–800 °C). The primary objective was to develop robust predictive models for the LAC values using Response Surface Methodology (RSM). Results showed that exposure to elevated temperatures notably reduced the LAC of the control concrete, especially after heating to 800 °C. Incorporating nanomaterials effectively mitigated this deterioration, maintaining higher shielding efficiency. NL mixes showed the greatest improvement, with LAC increases of about 42.6% and 37.4% after exposure to 600 °C and 800 °C, respectively. NM mixes ranked second, enhancing LAC by 26.7% and 24.2% at the same temperatures, while NG provided moderate improvement of around 9.9% at 800 °C (4% replacement). The superior performance of NL and NM mixes is attributed to their high density and atomic number, which promote photon absorption and scattering within the concrete matrix, as well as the improved microstructural integrity resulting from the filler effect of the uniformly dispersed nanoscale particles. Analysis of Variance (ANOVA) confirmed the high statistical significance and predictive power of the developed model, demonstrating an exceptional correlation between predicted and experimental data. The response surface analysis revealed that the nanomaterial dose and type are the dominant factor for enhancing LAC values, exhibiting a strong positive correlation, whereas elevated temperature has a detrimental effect. The observed curvature in the response surfaces confirmed significant non-linear and interaction effects between the input parameters. Optimization results demonstrated that a 5% nano-dose at various temperatures maximizes shielding performance, with NL identified as the most effective material, demonstrating superior radiation shielding properties.