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Khalaf Al-Ofi
Construction Engineering and Management Department, College of Environmental Design, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

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Journal article
Published: 05 October 2020 in Applied Sciences
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The unloading of petroleum products is a complex and potentially dangerous operation since the unloading system contains complex interdependency components. Any failures in one of its components lead to a cut in the petroleum supply chain. Therefore, it is important to assess and evaluate the reliability of the unloading system in order to improve its availability. In this context, this paper presents the operation philosophy of the truck unloading system, failure modes of the components within the system, and a bottom-up approach to analyze the reliability of the system. In addition, it provides reliability data, such as failure rates, and mean time between failures of the system components. Furthermore, the reliability of the whole system was calculated and is presented for different time periods. The critical components, which are major contributors towards the system reliability, were identified. To enhance the system reliability, a reliability-based preventive maintenance strategy for the critical components was implemented. In addition, the preventive maintenance scheduling was identified based on the reliability plots of the unloading system. The best schedule for preventive maintenance of the system was determined based on the reliability function to be every 45 days for maintaining the system reliability above 0.9. Findings reveal that the reliability of the unloading system was significantly improved. For instance, the system reliability at one year improved by 80%, and this ratio increased dramatically as the time period increased.

ACS Style

Awsan Mohammed; Ahmed Ghaithan; Mashel Al-Saleh; Khalaf Al-Ofi. Reliability-Based Preventive Maintenance Strategy of Truck Unloading Systems. Applied Sciences 2020, 10, 6957 .

AMA Style

Awsan Mohammed, Ahmed Ghaithan, Mashel Al-Saleh, Khalaf Al-Ofi. Reliability-Based Preventive Maintenance Strategy of Truck Unloading Systems. Applied Sciences. 2020; 10 (19):6957.

Chicago/Turabian Style

Awsan Mohammed; Ahmed Ghaithan; Mashel Al-Saleh; Khalaf Al-Ofi. 2020. "Reliability-Based Preventive Maintenance Strategy of Truck Unloading Systems." Applied Sciences 10, no. 19: 6957.

Journal article
Published: 18 July 2020 in International Journal of Environmental Research and Public Health
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Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to investigate different traffic violations (overspeeding, wrong-way driving, illegal parking, non-compliance traffic control devices, etc.) using spatial analysis and different machine learning methods. Georeferenced violation data along two expressways (S308 and S219) for the year 2016 was obtained from the traffic police department, in the city of Luzhou, China. Detailed descriptive analysis of the data showed that wrong-way driving was the most common violation type observed. Inverse Distance Weighted (IDW) interpolation in the ArcMap Geographic Information System (GIS) was used to develop violation hotspots zones to guide on efficient use of limited resources during the treatment of high-risk sites. Lastly, a systematic Machine Learning (ML) framework, such as K Nearest Neighbors (KNN) models (using k = 3, 5, 7, 10, and 12), support vector machine (SVM), and CN2 Rule Inducer, was utilized for classification and prediction of each violation type as a function of several explanatory variables. The predictive performance of proposed ML models was examined using different evaluation metrics, such as Area Under the Curve (AUC), F-score, precision, recall, specificity, and run time. The results also showed that the KNN model with k = 7 using manhattan evaluation had an accuracy of 99% and outperformed the SVM and CN2 Rule Inducer. The outcome of this study could provide the practitioners and decision-makers with essential insights for appropriate engineering and traffic control measures to improve the safety of road-users.

ACS Style

Muhammad Zahid; Yangzhou Chen; Arshad Jamal; Khalaf A. Al-Ofi; Hassan M. Al-Ahmadi. Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study. International Journal of Environmental Research and Public Health 2020, 17, 5193 .

AMA Style

Muhammad Zahid, Yangzhou Chen, Arshad Jamal, Khalaf A. Al-Ofi, Hassan M. Al-Ahmadi. Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study. International Journal of Environmental Research and Public Health. 2020; 17 (14):5193.

Chicago/Turabian Style

Muhammad Zahid; Yangzhou Chen; Arshad Jamal; Khalaf A. Al-Ofi; Hassan M. Al-Ahmadi. 2020. "Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study." International Journal of Environmental Research and Public Health 17, no. 14: 5193.

Journal article
Published: 16 July 2020 in Sustainability
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The need for resilience and an agile waste management system in Saudi Arabia is vital to control safely the rapid growth of its municipal solid waste (MSW) with minimal environment toll. Similarly, the domestic energy production in Saudi Arabia is thriving and putting a tremendous pressure on its huge reserves of fossil oil. Waste to energy (WTE) plants provides a golden opportunity for Saudi Arabia; however, both challenges (MSW mitigation and energy production) are usually looked at in isolation. This paper at first explores the potential of expanding the WTE energy production in the eastern province in Saudi Arabia under two scenarios (complete mass burn with and without recycling). Secondly, this study analyzes the effect of 3Rs (reduce, reuse, recycle) practices implementation in a residential camp (11,000 population) to influence the behavior of the camp’s citizens to reduce their average waste (kg/capita). The results of the 3R-WTE framework show a potential may reach 254 Megawatt (MW) of electricity by year 2030. The 3R system implementation in the camp reduced MSW production from 5,625 tons to 3000 tons of household waste every year, which is considered lower than what the surrounding communities to be produced in the same area.

ACS Style

Laith Hadidi; Ahmed Ghaithan; Awsan Mohammed; Khalaf Al-Ofi. Deploying Municipal Solid Waste Management 3R-WTE Framework in Saudi Arabia: Challenges and Future. Sustainability 2020, 12, 5711 .

AMA Style

Laith Hadidi, Ahmed Ghaithan, Awsan Mohammed, Khalaf Al-Ofi. Deploying Municipal Solid Waste Management 3R-WTE Framework in Saudi Arabia: Challenges and Future. Sustainability. 2020; 12 (14):5711.

Chicago/Turabian Style

Laith Hadidi; Ahmed Ghaithan; Awsan Mohammed; Khalaf Al-Ofi. 2020. "Deploying Municipal Solid Waste Management 3R-WTE Framework in Saudi Arabia: Challenges and Future." Sustainability 12, no. 14: 5711.