The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and money. Portland cement is frequently substituted with metakaolin (MK) because of its technical and environmental advantages. In this study, three mechanical properties of concrete with MK, i.e., compressive strength (${f^{\prime }}_{\mathrm{c}}$), splitting tensile strength ($f_{\mathrm{st}}$), and flexural strength (FS) were modelled by using four machine learning (ML) techniques: gene expression programming (GEP), artificial neural network (ANN), M5P model tree algorithm, and random forest (RF). For this purpose, a comprehensive database containing detail of concrete mixture proportions and values of ${f^{\prime }}_{\mathrm{c}}$, $f_{\mathrm{st}}$, and FS at different ages was gathered from peer-reviewed published documents. Various statistical metrics were used to compare the predictive and generalization capability of the ML techniques. The comparative study of ML techniques revealed that RF has better predictive and generalization capability as compared with GEP, ANN, and M5P model tree algorithm. Moreover, the sensitivity and parametric analysis (PA) was carried out. The PA showed that the most suitable proportions of MK as partial cement replacement were 10% for FS and 15% for both ${f^{\prime }}_{\mathrm{c}}$ and $f_{\mathrm{st}}$.

Compressive strength (CS) and splitting tensile strength (STS) are paramount parameters in the design of reinforced concrete structures and are required by pertinent standard provisions. Robust prediction models for these properties can save time and cost by reducing the number of laboratory trial batches and experiments needed to generate suitable design data. Silica fume (SF) is often used in concrete owing to its substantial enhancements of the engineering properties of concrete and its environmental benefits. In the present study, the M5P model tree algorithm was used to develop models for the prediction of the CS and STS of concrete incorporating SF. Accordingly, large databases comprising 796 data points for CS and 156 data records for STS were compiled from peer-reviewed published literature. The predictions of the M5P models were compared with linear regression analysis and gene expression programming. Different statistical metrics, including the coefficient of determination, correlation coefficient, root mean squared error, mean absolute error, relative squared error, and discrepancy ratio, were deployed to appraise the performance of the developed models. Moreover, parametric analysis was carried out to investigate the influence of different input parameters, such as the SF content, water-to-binder ratio, and age of the specimen, on the CS and STS. The trained models offer a rapid and accurate tool that can assist the designer in the effective proportioning of silica fume concrete.

This study reports pollution source apportionment of surface waters and human health risk assessment based on 18 physicochemical and traces elements from 24 water quality monitoring sites for surface and groundwater around the two trans-boundary rivers of Pakistan: The Ravi and Sutlej. The principal component analysis identified 6 principal components (76.98 % cumulative variance) which are mainly caused by untreated industrial effluents, intense agricultural activities, and irrigation tailwater discharges. For all dissolved trace elements in surface waters, health hazard indices (HI) and hazard quotients (HQ) through ingestion and dermal contact are < 1 except As and Cr through ingestion only (for both adults and children). For adults and children, the HQingestion and HI values for As, Mn, Cu (for children only) and As, Fe, Mn (for children only) are > 1, indicating that As, Mn, Fe, and Cu are the most important pollutants causing chronic risks among the selected trace elements in both shallow and deep groundwater respectively. HQingestion, HQdermal, and HI values are higher for children than that of adults which shows the high susceptibility of children to these dissolved trace elements. The carcinogenic indices for the entire surface water elements exceed 10 ⁻⁶ through dermal and ingestion pathways suggesting carcinogenic health risk to the surrounding community. Hence, to protect human health, wastewater treatment plants and best management practices should be practiced to control point source and nonpoint source pollution respectively in the understudied area.