Supervised learning algorithms are a recent trend for the prediction of mechanical properties of concrete. This paper presents AdaBoost, random forest (RF), and decision tree (DT) models for predicting the compressive strength of concrete at high temperature, based on the experimental data of 207 tests. The cement content, water, fine and coarse aggregates, silica fume, nano silica, fly ash, super plasticizer, and temperature were used as inputs for the models’ development. The performance of the AdaBoost, RF, and DT models are assessed using statistical indices, including the coefficient of determination (R²), root mean squared error-observations standard deviation ratio (RSR), mean absolute percentage error, and relative root mean square error. The applications of the above-mentioned approach for predicting the compressive strength of concrete at high temperature are compared with each other, and also to the artificial neural network and adaptive neuro-fuzzy inference system models described in the literature, to demonstrate the suitability of using the supervised learning methods for modeling to predict the compressive strength at high temperature. The results indicated a strong correlation between experimental and predicted values, with R² above 0.9 and RSR lower than 0.5 during the learning and testing phases for the AdaBoost model. Moreover, the cement content in the mix was revealed as the most sensitive parameter by sensitivity analysis.

Supervised machine learning and its algorithms are a developing trend in the prediction of rockfill material (RFM) mechanical properties. This study investigates supervised learning algorithms—support vector machine (SVM), random forest (RF), AdaBoost, and k-nearest neighbor (KNN) for the prediction of the RFM shear strength. A total of 165 RFM case studies with 13 key material properties for rockfill characterization have been applied to construct and validate the models. The performance of the SVM, RF, AdaBoost, and KNN models are assessed using statistical parameters, including the coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data (RSR). The applications for the abovementioned models for predicting the shear strength of RFM are compared and discussed. The analysis of the R² together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R² = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R² = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R² = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R² = 0.6233, NSE = 0.6180, RMSE = 0.3693, and RSR = 0.6181). Furthermore, the sensitivity analysis result shows that normal stress was the key parameter affecting the shear strength of RFM.

Naturally occurring mutations in morphogenetic protein 15 (BMP15) are associated with decreased ovulation rate (OR), litter size (LS), and sterility. It is of a great interest to elucidate BMP15 gene in Cholistani sheep breed to uplift socio-economic status and the knowledge of Cholistani sheep breeding in Southern Punjab, Pakistan. In our study, a total of 50 infertile Cholistani sheep aged between 2-6 years and having no blood relation were screened for BMP15 mutations. For this purpose, a high-quality DNA was extracted from the blood of sheep followed by primer designing, Polymerase Chain Reaction (PCR) amplification, DNA sequencing, and in silico analyses. Out of total 50 samples, 9 samples including case 1 (T3), case 2 (T8), case 3 (T17), case 4 (T22), case 5 (T25), case 6 (T33), case 7 (T40), case 8 (T44), and case 9 (T47) were found positive for a variety of already reported and novel BMP15 mutations. Further in silico analyses of the observed mutations have shown the functional impact of these mutations on different characteristics (molecular weight, theoretical PI, estimated half-life, instability index, sub-cellular localization, and 3D confirmation) of the encoded proteins, possibly altering the normal functionality. In a nutshell, findings of this study have confirmed the possible essential role of the BMP15 mutations in the infertility of the Cholistani sheep.

The direct shear test is a widely used method for determining soil and material shear strength parameters. This study examines the impact of shear box size on the shear strength properties of poorly graded sands collected from Lahore, Pakistan. Sand samples were classified using the Unified Soil Classification System and tested at dry densities ranging from 1.601 to 1.825 g/cm3 using shear boxes with 60 mm, 100 mm, and 160 mm diameters. Tests were performed at a constant rate of 1.25 mm/min under normal stresses of 23.42, 35.76, and 48.11 kPa. Results indicate that increasing box size leads to higher cohesion and lower friction angles, while increasing soil density enhances both parameters. Regression relationships were developed to relate small-scale and large-scale test results. These findings underscore the importance of accounting for scale effects in laboratory shear testing and offer guidance on extrapolating small-scale results to design applications. The study highlights the significance of considering container size effects in the interpretation of laboratory shear tests. Future research may explore varying soil types and moisture contents to generalise these findings further. Understanding these scale effects is crucial for improving the accuracy of geotechnical assessments and ensuring safe construction practices.

Hydroelectric power generation is one of the most significant and dependable renewable energy sources for Pakistan. With a few minor adjustments and a range of design options, micro hydropower plants may be built on an existing canal to restore or boost irrigation water delivery and generate electricity by choosing the appropriate turbine. The design of the Machai hydropower plant was examined in this study using the Manning equation to establish the power canal's design specifications. The Manning roughness coefficient is calculated to find the section factor. Hydraulic mean depth, top width, height, area, and channel width may all be determined using the section factor. Based on the outcomes of the TURBNPRO program, the Kaplan turbine installed in the powerhouse was selected. To evaluate the stability of embankments, SLIDE software was employed. The study concludes that micro-hydropower is a technically viable and environmentally friendly method of producing electricity in remote areas. Particularly for isolated off-grid locations, Machai and other micro-hydropower devices provide a cost-effective and ecologically friendly energy alternative. Compared to diesel generators or coal-fired power plants, micro-hydropower is a more environmentally beneficial choice since it emits no greenhouse gases when in operation.