Purpose. The purpose of the paper is to provide open-pit mining operations with practical strategies and insights to optimize truck loading and hauling cycles, ultimately leading to enhanced productivity and economic advantages. Methods. The objectives are to minimize loading time, optimize the haul road network, enhance truck performance, and optimize dumping and return time. By diligently implementing these methods and achieving these objectives, open-pit mining operations can significantly reduce the truck cycle times, resulting in increased productivity, lower costs and improved profitability. Findings. In this case, the total loading time of the excavator and shovel is determined to be 3.98 and 2.92 minutes, respectively, while the hauling time for total loading of the open-pit floor depends mainly on the average distance and speed of 239 m and 10.1 km/hour, which results in 1.53 minutes. Originality. As a result, the total cycle time for open-pit mining is 19.765 minutes, resulting from the total loading time, hauling time for total loading, total dumping time, and total return time for empty transport of 4.265, 8.46, 0.86 and 6.18 minutes, respectively. Practical implications. By combining theoretical analysis with practical insights and site-specific considerations, the paper aims to provide a comprehensive and applicable framework for optimizing truck cycle time in open-pit mining, resulting in improved efficiency and profitability of mining operations.

This research demonstrates the results of an investigation into the California bearing ratio (CBR) of granular soils from Qassim region, Saudi Arabia, using multilinear regression (MLR), pure quadratic (PQ) models, and gene expression programming (GEP) methods utilized to develop mathematical models for estimating the CBR based on basic soil index properties. In this study, samples were collected from different borrowing pits in the Qassim area. Forty‐three samples of soil were taken and transferred to a laboratory for examination. Seven multilinear regressions and seven PQ models were investigated, while four GEP models were made. The selection of each model variable depends on soil indices, grouping into grain size distribution, Atterberg limits, and compaction parameters. The results of this analysis showed that the PQ model had a higher accuracy [coefficient of determination (R²) = 0.89, root mean square error (RMSE) = 16.006, uncertainty (U95) = 16.17, and reliability = 57%] than the multilinear regression model, which has a lower accuracy model [R² = 0.811, RMSE = 20.791, U95 = 15.569, and reliability = 51%]. The best GEP model yields [R² = 0.776, RMSE = 22.552, U95 = 15.787, and reliability = 53%]. Furthermore, sensitivity analysis was conducted to distinguish the influences of different input variables on CBR; it was found that fines percentage (F₂₀₀), maximum dry density (MDD), and optimum moisture content (OMC) are the most influential variables.

Examining the factors contributing to work zone crashes and implementing measures to reduce their occurrence can significantly improve road safety. In this research, we utilized the explainable boosting machine (EBM), a modern glass-box machine learning (ML) model, to categorize and predict work zone-related crashes and to interpret the various contributing factors. The issue of data imbalance was also addressed by utilizing work zone crash data from the state of New Jersey, comprising data collected over the course of two years (2017 and 2018) and applying data augmentation strategies such synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, and SVM-SMOTE. The EBM model was trained using augmented data and Bayesian optimization for hyperparameter tuning. The performance of the EBM model was evaluated and compared to black-box ML models such as combined kernel and tree boosting (KTBoost, python 3.7.1 and KTboost package version 0.2.2), light gradient boosting machine (LightGBM version 3.2.1), and extreme gradient boosting (XGBoost version 1.7.6). The EBM model, using borderline-SMOTE-treated data, demonstrated greater efficacy with respect to precision (81.37%), recall (82.53%), geometric mean (75.39%), and Matthews correlation coefficient (0.43). The EBM model also allows for an in-depth evaluation of single and pairwise factor interactions in predicting work zone-related crash severity. It examines both global and local perspectives, and assists in assessing the influence of various factors.

The outbreak of COVID-19 has had a profound impact on the Saudi construction industry as well as the country’s economy. The pandemic jeopardized the positive perspectives and growth in megaproject numbers, as it has amplified the constraints that prevent the construction sector from realizing growth. This research work is intended to evaluate the status of the Saudi construction industry during the COVID-19 pandemic. In order to achieve this objective, a three-phased methodology was developed. In the first part of the research, semi-structured interviews with forty industry experts were conducted. The gathered data from both, the literature review and interviews were synthesized. This process resulted in four domains to be explored: project performance measurement, workforce, supply chain, and financial management. The outcomes from phase one were then utilized to develop a questionnaire survey that was communicated to construction firms all over Saudi Arabia, for which 124 responses were received. Data analysis was carried out, and the obtained results were clarified and triangulated through a focus group discussion in the third phase of the research. The outcomes from the mixed-methods research methodology were aggregated to enrich and interpret findings and draw conclusions and recommendations. The findings indicate that the pandemic has had a total of ten core impacts. The highly impacted areas in the industry were the technical performance of projects, reduction in productivity, risk management practices, downsizing the scope of ongoing projects, reduction in new projects or contracts, material shortage, recruitment of manpower in the construction firms and affiliated projects, and the financial performance of the organization. In the meantime, it was found that the government implemented mitigation measures from which the sector benefited, where 27% of contractors obtained Saned system assistance, 51% received cash compensation and 22% received tax postponement. The scope of this study is limited to exploring the status of the Saudi construction industry (SCI) during the COVID-19 pandemic. The study findings are of added value and represent a significant contribution to the body of knowledge in the field. However, further research on the exit strategies bringing the industry to the new normal, including the use of cutting-edge technologies in the age of multi-faceted disruption would be of great importance.

Piles are used widely for stabilization of landslides. To stabilize a slope settled on bedrock with piles the required factor of safety must be checked, and pile should be designed properly. Piles should be socketed into firm rock to prevent uprooting or overturning .In this research it is aimed to look into the socketed length of pile in bedrock. Therefore the parameters that affect the factor of safety of slope/pile system such as location, length, spacing and diameter of piles are analyzed. The effect of socketed length of pile in rock on pile behavior is investigated by plotting the shear force and bending moment diagrams along pile. The optimal pile position is found to be located slightly upper of the middle of the slope. The minimum socketed length after which the factor of safety will be remained constant is found to be 0.12L where L is pile length.FLAC3D computer code based on finite difference method is used to simulate the slope/pile system.