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Faisal Khan

Bio: Faisal Khan is an academic researcher from Memorial University of Newfoundland. The author has contributed to research in topic(s): Risk analysis & Risk assessment. The author has an hindex of 70, co-authored 705 publication(s) receiving 21281 citation(s). Previous affiliations of Faisal Khan include Royal Hobart Hospital & Australian Maritime College.
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Thomas Browne1, Thomas Browne2, Trung Tien Tran2, Brian Veitch2  +3 moreInstitutions (2)
Abstract: Development of effective marine policy necessitates evidence-based, data-driven evaluations of the effects of regulatory constraints on operations. This is essential to better understand implications of policy decisions on complex socio-technical systems. This paper demonstrates a generalized methodology for evaluating operational implications associated with implementing maritime regulations. The method combines a ship performance model, regulatory constraint models, and multi-criteria pathfinding and optimization algorithms to evaluate and compare the operational implications of different regulatory constraints. The method is applied to Arctic shipping. The Polar Operational Limit Assessment Risk Indexing System (POLARIS) and the Arctic Ice Regimes Shipping System (AIRSS) are considered. POLARIS and AIRSS are regulatory guidelines used to assign structural safety constraints on ships in ice. Four approaches for assigning structural safety constraints are modelled: 1) POLARIS, 2) AIRSS, 3) speed limits established through a first-principles ship-ice interaction model, and 4) navigation in the absence of structural safety constraints. Operational implications are measured as distance, voyage time, and fuel consumption. Route optimization is validated against expert opinion of Arctic ship captains. Results indicate AIRSS is the more conservative regulatory guideline, yet associated with decreased voyage time and fuel consumption. Implications for marine policy and safe navigation are that, while POLARIS offers flexibility to operate in more severe ice conditions, it increases voyage time, fuel consumption, and the risk of vessel damage. Competent Arctic seafarers are critical for safe and efficient operations. The generalized methodology provides marine policy-makers and industry stakeholders with a means to evaluate operational implications of maritime regulations.

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Mingqi Bai1, Yi Liu1, Meng Qi2, Nitin Roy3  +3 moreInstitutions (5)
Abstract: The past two decades have seen a rise in university laboratory accidents in China. Although there is a growing awareness due to higher reporting and media coverage, the evaluation and understanding of common hazards and deficiencies in university laboratories remains to be addressed. Aiming to enhance safety in laboratory-related activities, this study analyzed the current status and challenges of university laboratory safety in China and presented future directions to reduce accidents using engineering and administrative controls. A descriptive statistical analysis of 110 publicly reported university laboratory accidents in mainland China since 2000 was performed to investigate the proximate causes of the accidents, and further, to identify potential deficiencies existing in the current safety management of laboratories. It was found that human factors were the most contributing cause and the training element was a vulnerable competency in laboratory safety management. Based on the results, a comparative analysis between the underlying reasons for the poor safety situation and the efforts that have been made has brought the challenges and possible solutions for safety improvements in university laboratories. By suggesting top-down and bottom-up approaches, the present study provides valuable insights and serves as a reference for universities and relevant authorities to enhance safety in university laboratories.

Journal ArticleDOI
Jingyu Zhu1, Guoming Chen1, Zhiming Yin2, Faisal Khan3  +1 moreInstitutions (4)
TL;DR: This study presents an integrated methodology that considers the interaction among the drilling risk factors and assesses the blowout risk throughout the deepwater drilling operation's lifecycle and demonstrates the methodology's effectiveness in assessing and evaluatingblowout risk during the drilling operation lifecycle.
Abstract: Offshore drilling operations face technological and operational challenges combined with harsh environmental conditions. The well blowouts are the most feared offshore process operational accident. Many methodologies have been proposed to assess the blowout risk. Most of these studies consider the risk factors' independence and focus on a specific drilling life cycle stage. This study presents an integrated methodology that considers the interaction among the drilling risk factors and assesses the blowout risk throughout the deepwater drilling operation's lifecycle. This integrated methodology is developed based on the index-based risk evaluation system, which comprises hazard identification, interaction analysis, indices weights, and risk evaluation. The Decision Making Trial and Evaluation Laboratory method is used to identify and assess risk factors' interaction. The uncertainty associated with the data is addressed using the Z-numbers method. The risk indices are dynamic to capture the hazards during the lifecycle of the drilling operation. The application of the methodology is tested on a deepwater drilling operation. The application demonstrates the methodology's effectiveness in assessing and evaluating blowout risk during the drilling operation lifecycle.

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Xi Chen1, Neil Bose1, Mario Brito2, Faisal Khan1  +2 moreInstitutions (2)
Abstract: Risk analysis for autonomous underwater vehicles (AUVs) is essential to assist decision making for safer operations. This study aims to provide a systematic review of risk analysis research to enhance the safety performance of AUVs. Forty-two domain articles were retrieved and analyzed. Critical risk factors and causal relationships of AUV operations were identified. A comparative analysis of evolving methods and models was performed by categorizing them as qualitative, semi-quantitative, and quantitative. Future trends of research in this field were also outlined. The study observes that as AUV technologies gradually mature, environmental factors, human factors, and their interactive impacts are gathering more attention. Quantitative risk analysis methods have recently played a key role in improving the accuracy and handling the uncertainties of risk estimation. The study recommends devoting efforts to dynamic risk analysis, addressing limited historical data, intelligent risk analysis, and multi-vehicles risk analysis for future works. This study is expected to help AUV stakeholders gain comprehensive insights into fundamental concepts and evolving methods for risk analysis of AUVs. At the same time, it is expected to highlight future directions to bridge existing gaps.

1 citations

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Abstract: The stochastic nature of microbial corrosion creates spatial interdependencies among random corrosion parameters and their failure modes. These interdependencies need to be captured for robust offshore system reliability prediction considering complex multispecies biofilms. This research paper presents a hybrid methodology for the prediction of system reliability, considering multiple failure modes’ interdependencies. The methodology integrates the Bayesian Network with Copula-based Monte Carlo (BN-CMC) simulation. The BN captures the dynamic interactions among physio-chemical parameters and microbes to predict the corrosion rate of an offshore system. The random corrosion parameters dependencies and the failure modes that define the performance functions under microbial corrosion are modeled using CMC. The methodology is assessed with an example, and the impact of dynamic interactions of the parameters and their failure modes on the system reliability is investigated. The results reveal that the system's probability of failure differs diversely as the degree of dependencies among the random corrosion parameters and their failure modes increases. The proposed methodology can predict the failure indexes that could aid system integrity management for a sustainable offshore operation experiencing microbial corrosion.

7 citations

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Abstract: Structural Reliability analysis (SRA) is one of the prominent fields in civil and mechanical engineering. However, an accurate SRA in most cases deals with complex and costly numerical problems. Machine learning-based (ML) techniques have been introduced to the SRA problems to deal with this huge computational cost and increase accuracy. This paper presents a review of the development and use of ML models in SRA. The review includes the most common types of ML methods used in SRA. More specifically, the application of artificial neural networks (ANN), support vector machines (SVM), Bayesian methods and Kriging estimation with active learning perspective in SRA are explained, and a state-of-the-art review of the prominent literature in these fields is presented. Aiming towards a fast and accurate SRA, the ML techniques adopted for the approximation of the limit state function with Monte Carlo simulation (MCS), first/second-order reliability methods (FORM/SORM) or MCS with importance sampling well as the methods for efficiently computing the probabilities of rare events in complex structural systems. In this regard, the focus of the current manuscript is on the different models’ structures and diverse applications of each ML method in different aspects of SRA. Moreover, imperative considerations on the management of samples in the Monte Carlo simulation for SRA purposes and the treatment of the SRA problem as pattern recognition or classification task are provided. This review helps the researchers in civil and mechanical engineering, especially those who are focused on reliability and structural analysis or dealing with product assurance problems.

Journal ArticleDOI
Ling Li1, Dongdong Wu1, Tiejun Wang1, Yunqiang Wang2  +1 moreInstitutions (3)
Abstract: Soil moisture data were obtained from the Watershed Allied Telemetry Experimental Research Network (Waternet), located in a mountainous region of northwest China, to evaluate the impacts of environmental factors on regional soil moisture spatiotemporal patterns. Based on the temporal stability analysis method, the results showed that soil moisture spatial distributions in the study region were more correlated with elevation (a local control) than with precipitation (a regional control), highlighting the importance of local factors in controlling regional soil moisture spatial patterns. Moreover, the spatial variance of the absolute soil moisture content (e.g., the total soil moisture spatial variability) at the Waternet stations was decomposed into time-invariant and time-variant components. The results showed that the spatial variability in the time-invariant component (e.g., the temporal average soil moisture content) was the primary component in the total soil moisture spatial variability. More importantly, the temporal evolutions of the time-variant components and their contributions to the total soil moisture spatial variance were also affected by local factors, particularly by elevation in the study region. Overall, this study provides further observational evidence, which suggests that depending on specific regional conditions, local factors through affecting both time-invariant and time-variant components can outweigh regional factors in controlling soil moisture spatial variability at regional scales.

Journal ArticleDOI
Xinhong Li1, Jia Ruichao1, Renren Zhang1, Shangyu Yang2  +1 moreInstitutions (3)
Abstract: Corrosion is an important reason for the structural degradation of offshore oil pipelines, which may cause serious economic loss and environmental pollution. Nowadays the digitalized devices make a number of monitoring data become available. The prediction of corrosion degradation based on monitoring data becomes an efficient tool to prevent corrosion failure of offshore oil pipelines. This paper integrates KPCA and BRANN techniques to develop a novel data-driven model for corrosion degradation prediction of offshore oil pipelines. The model can eliminate the redundant information from the original monitoring data and improve the robustness by regularization constraints. KPCA is applied to reduce the dimension of the factors affecting pipeline corrosion, and the extracted principal components of corrosion variables are inputted in BRANN to build a corrosion degradation prediction model. The data with dimension reduction are divided into training set and validation set. The model is compared with BRANN alone and KPCA-LMANN model, which indicates KPCA-BRANN model presents superiority in the robustness and prediction accuracy (MSE = 0.46%; R2=0.99). The proposed model can be used as an online prediction module of digitized process safety system, and support the reliability assessment and maintenance planning of corroded subsea pipelines.

Journal ArticleDOI
Jaemin Jeong1, Jaewook Jeong1Institutions (1)
Abstract: The construction industry involves a variety of construction works, all of which differ in terms of the frequency and probability of fatal incidents and types of accidents. Accordingly, the...

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Author's H-index: 70

No. of papers from the Author in previous years