To minimize the environmental risks and for sustainable development, the utilization of recycled aggregate (RA) is gaining popularity all over the world. The use of recycled coarse aggregate (RCA) in concrete is an effective way to minimize environmental pollution. RCA does not gain more attraction because of the availability of adhered mortar on its surface, which poses a harmful effect on the properties of concrete. However, a suitable mix design for RCA enables it to reach the targeted strength and be applicable for a wide range of construction projects. The targeted strength achievement from the proposed mix design at a laboratory is also a time-consuming task, which may cause a delay in the construction work. To overcome this flaw, the application of supervised machine learning (ML) algorithms, gene expression programming (GEP), and artificial neural network (ANN) was employed in this study to predict the compressive strength of RCA-based concrete. The linear coefficient correlation (R²), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to investigate the performance of the models. The k-fold cross-validation method was also adopted for the confirmation of the model’s performance. In comparison, the GEP model was more effective in terms of prediction by giving a higher correlation (R²) value of 0.95 as compared to ANN, which gave a value of R² equal to 0.92. In addition, a sensitivity analysis was conducted to know about the contribution level of each parameter used to run the models. Moreover, the increment in data points and the use of other supervised ML approaches like boosting, gradient boosting, and bagging to forecast the compressive strength, would give a better response.

The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a significant role in developing the innovative environment in the field of civil engineering. This study was based on the use of the artificial neural network (ANN), boosting, and AdaBoost ML approaches, based on the python coding to predict the compressive strength (CS) of high calcium fly-ash-based GPC. The performance comparison of both the employed techniques in terms of prediction reveals that the ensemble ML approaches, AdaBoost, and boosting were more effective than the individual ML technique (ANN). The boosting indicates the highest value of R² equals 0.96, and AdaBoost gives 0.93, while the ANN model was less accurate, indicating the coefficient of determination value equals 0.87. The lesser values of the errors, MAE, MSE, and RMSE of the boosting technique give 1.69 MPa, 4.16 MPa, and 2.04 MPa, respectively, indicating the high accuracy of the boosting algorithm. However, the statistical check of the errors (MAE, MSE, RMSE) and k-fold cross-validation method confirms the high precision of the boosting technique. In addition, the sensitivity analysis was also introduced to evaluate the contribution level of the input parameters towards the prediction of CS of GPC. The better accuracy can be achieved by incorporating other ensemble ML techniques such as AdaBoost, bagging, and gradient boosting.

The high use of gasoline and diesel vehicles results in environmental pollution and loss of natural resources. The use of electric vehicles can be socially beneficial and reduce air pollution. In this research study, the main aims are to identify the potential of Electric Vehicles (EVs) in Pakistan using a Stated Preferences (SP) survey. An SP questionnaire was designed using the theoretical framework of the Theory of Planned Behavior (TPB). This questionnaire consisted of travelers’ personal and travel characteristics and various statements designed to address the variables of TPB such as Attitudes Towards the Electric Vehicles, Subjective Norms (SN), Perceived Behavioral Control (PBC), and Behavioral Intentions (BI). The behavior items were designed in the context of travelers’ willingness to buy and use EVs. Confirmatory Factor Analyses (CFAs) were conducted to validate the designed hypotheses related to the variables of TPB. The cross-analysis revealed that there are variations in responses across different occupations, trip distances, travel modes, and vehicle ownership. The developed structural models showed that the Attitudes and PBC have a positive influence on BI and PBC, which also have a direct impact on travelers’ willingness to use EV. The SN, PBC, and Attitudes are significant predictors of behavioral intentions in the willingness to buy EV and PBC is not a direct predictor of travelers’ willingness to buy an EV. The BI acts as a mediator to explain the indirect effects of Attitudes, SN, and PBC on willingness to purchase and use an EV. In the end, a framework is also proposed for the behavior-related interventions to promote the EVs considering the socio-economic context of Pakistan.

Given the excessive demolition of structures each year, the issues related to the generated structural waste are striking. Bricks being a major constituent in the construction industry, also hold a significant proportion of the construction waste generated annually. The reuse of this brick waste in new constructions is an optimal solution considering cost-effectiveness and sustainability. However, the problems related to the substandard peak stress and ultimate strain of concrete constructed with recycled brick aggregates (CRAs) limit its use in non-structural applications. The present study intends to improve the unsatisfactory mechanical characteristics of CRAs by utilizing low-cost glass fiber chopped strand mat (FCSM) sheets. The efficacy of FCSM sheets was assessed by wrapping them around CRA specimens constructed with different concrete strengths. A remarkable increase in the peak compressive stress and the ultimate strain of the CRA specimens were observed. For low, medium, and high strength CRAs, the ultimate strain improved by up to 320%, 308%, and 294%, respectively, as compared to the respective control specimens. Several existing analytical models were utilized to predict the peak compressive stress and ultimate strain of the CRAs strengthened using FCSM sheets. None of the considered models reproduced experimental results accurately. Therefore, equations were formulated using regression predicting the peak stress and ultimate strain of the CRAs confined with FCSM sheets. The predicted values were found to correlate well with the experimental values.

Salient features of lightweight aggregate concrete (LAC) include noticeable fire resistance, high strength-to-weight ratio, and low magnitude of dead loads. Further, LAC has a low cost, eases construction practices, and possesses an environment-friendly nature. On the downside, LAC has substandard mechanical properties in comparison to normal aggregate concrete. Natural fiber-reinforced polymers (FRPs) have shown their potential in ameliorating the mechanical properties of natural aggregate concrete. So far, no study has been conducted to assess the efficacy of hemp rope confinement to strengthen lightweight aggregate concrete especially comprising rectilinear sections. This study aimed to overcome the substandard nature of LAC. A low-cost, sustainable, and environmentally green solution in the form of natural hemp rope layers is proposed. Twenty-four square concrete specimens were tested in three groups depending upon the presence and quantity of lightweight aggregates. It was found that concrete constructed with lightweight aggregates demonstrated lower ultimate compressive strength and strain as compared to normal aggregate concrete. Hemp rope-confined LAC showed enhanced ultimate compressive strength and strain. This enhancement was found to increase with the number of hemp rope layers. Several existing ultimate stress models were assessed to predict the ultimate compressive strength of the hemp rope-confined specimens. Only a single model was able to predict the ultimate compressive strength of the hemp rope-confined specimens with reasonable accuracy.