Increased population necessitates an expansion of infrastructure and urbanization, resulting in growth in the construction industry. A rise in population also results in an increased plastic waste, globally. Recycling plastic waste is a global concern. Utilization of plastic waste in concrete can be an optimal solution from recycling perspective in construction industry. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and tensile strengths of plastic concrete. For predicting the strength of concrete produced with plastic waste, this research integrates machine learning algorithms (individual and ensemble techniques), including bagging and adaptive boosting by including weak learners. For predicting the mechanical properties, 80 cylinders for compressive strength and 80 cylinders for split tensile strength were casted and tested with varying percentages of irradiated plastic waste, either as of cement or fine aggregate replacement. In addition, a thorough and reliable database, including 320 compressive strength tests and 320 split tensile strength tests, was generated from existing literature. Individual, bagging and adaptive boosting models of decision tree, multilayer perceptron neural network, and support vector machines were developed and compared with modified learner model of random forest. The results implied that individual model response was enriched by utilizing bagging and boosting learners. A random forest with a modified learner algorithm provided the robust performance of the models with coefficient correlation of 0.932 for compressive strength and 0.86 for split tensile strength with the least errors. Sensitivity analyses showed that tensile strength models were least sensitive to water and coarse aggregates, while cement, silica fume, coarse aggregate, and age have a substantial effect on compressive strength models. To minimize overfitting errors and corroborate the generalized modelling result, a cross-validation K-Fold technique was used. Machine learning algorithms are used to predict mechanical properties of plastic concrete to promote sustainability in construction industry.

The purpose of this article is to demonstrate the potential of gene expression programming (GEP) in anticipating the compressive strength of circular CFRP confined concrete columns. A new GEP model has been developed based on a credible and extensive database of 828 data points to date. Numerous analyses were carried out to evaluate and validate the presented model by comparing them with those presented previously by different researchers along with external validation comparison. In comparison to other artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and the adaptive neuro-fuzzy interface system (ANFIS), only GEP has the capability and robustness to provide output in the form of a simple mathematical relationship that is easy to use. The developed GEP model is also compared with linear and nonlinear regression models to evaluate the performance. Afterwards, a detailed parametric and sensitivity analysis confirms the generalized nature of the newly established model. Sensitivity analysis results indicate the performance of the model by evaluating the relative contribution of explanatory variables involved in development. Moreover, the Taylor diagram is also established to visualize how the proposed model outperformed other existing models in terms of accuracy, efficiency, and being closer to the target. Lastly, the criteria of external validation were also fulfilled by the GEP model much better than other conventional models. These findings show that the presented model effectively forecasts the confined strength of circular concrete columns significantly better than the previously established conventional regression-based models.

The traditional method to obtain optimum bitumen content and the relevant parameters of asphalt pavements entails time-consuming, complicated and expensive laboratory procedures and requires skilled personnel. This research study uses innovative and advanced machine learning techniques, i.e., Multi-Expression Programming (MEP), to develop empirical predictive models for the Marshall parameters, i.e., Marshall Stability (MS) and Marshall Flow (MF) for Asphalt Base Course (ABC) and Asphalt Wearing Course (AWC) of flexible pavements. A comprehensive, reliable and wide range of datasets from various road projects in Pakistan were produced. The collected datasets contain 253 and 343 results for ABC and AWC, respectively. Eight input parameters were considered for modeling MS and MF. The overall performance of the developed models was assessed using various statistical measures in conjunction with external validation. The relationship between input and output parameters was determined by performing parametric analysis, and the results of trends were found to be consistent with earlier research findings stating that the developed predicted models are well trained. The results revealed that developed models are superior and efficient in terms of prediction and generalization capability for output parameters, as evident by the correlation coefficient (R) (in this case >0.90) for both ABC and AWC.

The pervasive challenge of heavy metal pollution in water necessitates urgent interventions to mitigate chromium contamination. It poses significant threats to human health, aquatic ecosystems, and the environment. This research delves into the potential of date palm waste as a precursor of adsorbent for extraction of chromium from contaminated water, underscoring its potential in heavy metal remediation. Date palm waste, traditionally regarded as agricultural waste, underwent modification employing N-(3-chloro-2-hydroxypropyl) trimethylammonium chloride resulting in quaternized date palm waste (QDPW) as a prospective adsorbent for chromium extraction. The characterization techniques, including FTIR, SEM/EDX, BET, XRD, TGA/DTA, and element analysis, were employed to examine the structural and surface properties of both DPW and QDPW fibers. Optimal chromium uptake was achieved at a solution pH 6.5 over two hours, with a peak removal efficiency of 88% and an adsorption capacity of 22.26 mg/g. The equilibrium isotherms studies are consistent with Langmuir, Redlich-Peterson, and Sips isotherms, presenting a coefficient of determination (R²) of 0.989, 0.981, and 0.983, respectively. Moreover, the pseudo-second-order model was mostly aligned with the kinetics data with R² value of 0.999. These findings showed that the quaternized date palm waste can be considered a good option to recycle the date palm waste and reduce the environmental threats in aqueous solution.

Groundwater is an essential resource for drinking and agricultural purposes in the Al-Jouf region, Saudi Arabia. The main objective of this study is to assess groundwater quality for drinking and irrigation purposes in the Al-Jouf region. Physicochemical characteristics of groundwater were determined, including total dissolved solids (TDS), pH, electric conductivity (EC), hardness, and various anions and cations. The groundwater quality index (WQI) was calculated to determine the suitability of groundwater for drinking purposes. The EC, sodium percentage (Na⁺ %), magnesium hazard (MH), sodium adsorption ratio (SAR), potential salinity (PS), and Kelley’s ratio (KR) were assessed to evaluate the suitability of groundwater for irrigation. Effective statistical tests and Feed-forward neural network (FFNN) modeling were applied to reveal the correlation between parameters and predict WQI. The results indicated that approximately all samples are appropriate for drinking and irrigation uses except samples of the Al Qaryat region. The ionic abundance ranking was Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ for cations, and Cl⁻ > SO₄²⁻ > NO₃⁻ for anions. Moreover, the groundwater is dominated by alkali metals (K⁺ and Na⁺) and controlled by the rock–water interaction process. The indicators of groundwater quality for irrigation and drinking according to the following criteria (Na⁺ %, SAR, KR, MH, PS, WQI (WHO), and WQI (BIS)) can be predicted by the FFNN with root mean square errors (RMSE) of 0.136, 0.070, 0.022, 0.073, 2.45 × 10⁻³, 1.45 × 10⁻², and 1.18 × 10⁻², respectively, and R² of 0.99, 1.00, 0.99, 0.99, 1.00, 1.00, and 1.00, respectively.

This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R², RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R², RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.