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Prof. Dr. Brian Veitch
Faculty of Engineering and Applied Science, Memorial University of Newfoundland, 40 Arctic Ave, St. John's, NL A1B 3X7, Canada

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0 Risk
0 Safety
0 Training
0 Simulation

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Journal article
Published: 18 February 2021 in Journal of Marine Science and Engineering
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Traditional techniques for accident investigation have hindsight biases. Specifically, they isolate the process of the accident event and trace backward from the event to determine the factors leading to the accident. Nonetheless, the importance of the contributing factors towards a successful operation is not considered in conventional accident modeling. The Safety-II approach promotes an examination of successful operations as well as failures. The rationale is that there is an opportunity to learn from successful operations, in addition to failure, and there is an opportunity to further differentiate failure processes from successful operations. The functional resonance analysis method (FRAM) has the capacity to monitor the functionality and performance of a complex socio-technical system. The method can model many possible ways a system could function, then captures the specifics of the functionality of individual operational events in functional signatures. However, the method does not support quantitative analysis of the functional signatures, which may demonstrate similarities as well as differences among each other. This paper proposes a method to detect anomalies in operations using functional signatures. The present work proposes how FRAM data models can be converted to graphs and how such graphs can be used to estimate anomalies in the data. The proposed approach is applied to human performance data obtained from ice-management tasks performed by a cohort of cadets and experienced seafarers in a ship simulator. The results show that functional differences can be captured by the proposed approach even though the differences were undetected by usual statistical measures.

ACS Style

Syed Danial; Doug Smith; Brian Veitch. A Method to Detect Anomalies in Complex Socio-Technical Operations Using Structural Similarity. Journal of Marine Science and Engineering 2021, 9, 212 .

AMA Style

Syed Danial, Doug Smith, Brian Veitch. A Method to Detect Anomalies in Complex Socio-Technical Operations Using Structural Similarity. Journal of Marine Science and Engineering. 2021; 9 (2):212.

Chicago/Turabian Style

Syed Danial; Doug Smith; Brian Veitch. 2021. "A Method to Detect Anomalies in Complex Socio-Technical Operations Using Structural Similarity." Journal of Marine Science and Engineering 9, no. 2: 212.

Journal article
Published: 07 September 2020 in Journal of Marine Science and Engineering
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The digital transformation of the offshore and maritime industries will present new safety challenges due to the rapid change in technology and underlying gaps in domain knowledge, substantially affecting maritime operations. To help anticipate and address issues that may arise in the move to autonomous maritime operations, this research applies a human-centered approach to developing decision support technology, specifically in the context of ice management operations. New technologies, such as training simulators and onboard decision support systems, present opportunities to close the gaps in competence and proficiency. Training simulators, for example, are useful platforms as human behaviour laboratories to capture expert knowledge and test training interventions. The information gathered from simulators can be integrated into a decision support system to provide seafarers with onboard guidance in real time. The purpose of this research is two-fold: (1) to capture knowledge held by expert seafarers, and (2) transform this expert knowledge into a database for the development of a decision support technology. This paper demonstrates the use of semi-structured interviews and bridge simulator exercises as a means to capture seafarer experience and best operating practices for offshore ice management. A case-based reasoning (CBR) model is used to translate the results of the knowledge capture exercises into an early-stage ice management decision support system. This paper will describe the methods used and insights gained from translating the interview data and expert performance from the bridge simulator into a case base that can be referenced by the CBR model.

ACS Style

Jennifer Smith; Fatemeh Yazdanpanah; Rebecca Thistle; Mashrura Musharraf; Brian Veitch. Capturing Expert Knowledge to Inform Decision Support Technology for Marine Operations. Journal of Marine Science and Engineering 2020, 8, 689 .

AMA Style

Jennifer Smith, Fatemeh Yazdanpanah, Rebecca Thistle, Mashrura Musharraf, Brian Veitch. Capturing Expert Knowledge to Inform Decision Support Technology for Marine Operations. Journal of Marine Science and Engineering. 2020; 8 (9):689.

Chicago/Turabian Style

Jennifer Smith; Fatemeh Yazdanpanah; Rebecca Thistle; Mashrura Musharraf; Brian Veitch. 2020. "Capturing Expert Knowledge to Inform Decision Support Technology for Marine Operations." Journal of Marine Science and Engineering 8, no. 9: 689.

Article
Published: 08 June 2020 in WMU Journal of Maritime Affairs
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Lifeboat training is normally performed in controlled conditions to minimize the risk to trainees and equipment. Participants are given limited or no opportunity to practice skills in operational scenarios that represent offshore emergencies. For this reason, human performance in plausible emergencies is difficult to predict due to the limited data that is available. Simulation provides a means to collect novel data on human performance and learning in situations that are otherwise prohibitive due to risk. In this study, we use simulator data to shape knowledge of the problem space of lifeboat coxswain training and skill transfer. We use Bayesian inference to produce human performance probabilities (HPPs) to model the performance of lifeboat coxswains as they practice lifeboat tasks for the first time. Data collected in an experiment are used (1) to generate probability distributions to predict the amount of practice needed for new coxswains to achieve competence on lifeboat launching and maneuvering tasks, (2) to study how skills learned in training transfer to a new scenario, and (3) to make comparisons between task difficulty. The methodology can be applied to other problems to assess training effectiveness and improve instructional design. Models can be continuously strengthened with additional data to improve predictive accuracy. Probability distributions can be used to assess competence in new scenarios and to diagnose strengths and weaknesses using machine learning.

ACS Style

Randy Billard; Mashrura Musharraf; Brian Veitch; Jennifer Smith. Using Bayesian methods and simulator data to model lifeboat coxswain performance. WMU Journal of Maritime Affairs 2020, 19, 1 -18.

AMA Style

Randy Billard, Mashrura Musharraf, Brian Veitch, Jennifer Smith. Using Bayesian methods and simulator data to model lifeboat coxswain performance. WMU Journal of Maritime Affairs. 2020; 19 (3):1-18.

Chicago/Turabian Style

Randy Billard; Mashrura Musharraf; Brian Veitch; Jennifer Smith. 2020. "Using Bayesian methods and simulator data to model lifeboat coxswain performance." WMU Journal of Maritime Affairs 19, no. 3: 1-18.

Journal article
Published: 23 April 2020 in Applied Sciences
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The International Code for Ships Operating in Polar Waters (Polar Code) was adopted by the International Maritime Organization (IMO) and entered into force on 1 January 2017. It provides a comprehensive treatment of topics relevant to ships operating in Polar regions. From a design perspective, in scenarios where ice exposure and the consequences of ice-induced damage are the same, it is rational to require the same ice class and structural performance for such vessels. Design requirements for different ice class vessels are provided in the Polar Code. The Polar Operational Limit Assessment Risk Indexing System (POLARIS) methodology provided in the Polar Code offers valuable guidance regarding operational limits for ice class vessels in different ice conditions. POLARIS has been shown to well reflect structural risk, and serves as a valuable decision support tool for operations and route planning. At the same time, the current POLARIS methodology does not directly account for the potential consequences resulting from a vessel incurring ice-induced damage. While two vessels of the same ice class operating in the same ice conditions would have similar structural risk profiles, the overall risk profile of each vessel will depend on the magnitude of consequences, should an incident or accident occur. In this paper, a new framework is presented that augments the current POLARIS methodology to model consequences. It has been developed on the premise that vessels of a given class with higher potential life-safety, environmental, or socio-economic consequences should be operated more conservatively. The framework supports voyage planning and real-time operational decision making through assignment of operational criteria based on the likelihood of ice-induced damage and the potential consequences. The objective of this framework is to enhance the safety of passengers and crews and the protection of the Arctic environment and its stakeholders. The challenges associated with establishing risk perspectives and evaluating consequences for Arctic ship operations are discussed. This methodology proposes a pragmatic pathway to link ongoing scientific research with risk-based methods to help inform recommended practices and decision support tools. Example scenarios are considered to illustrate the flexibility of the methodology in accounting for varied risk profiles for different vessel types, as well as incorporating input from local communities and risk and environmental impact assessments.

ACS Style

Thomas Browne; Rocky Taylor; Brian Veitch; Pentti Kujala; Faisal Khan; Doug Smith. A Framework for Integrating Life-Safety and Environmental Consequences into Conventional Arctic Shipping Risk Models. Applied Sciences 2020, 10, 2937 .

AMA Style

Thomas Browne, Rocky Taylor, Brian Veitch, Pentti Kujala, Faisal Khan, Doug Smith. A Framework for Integrating Life-Safety and Environmental Consequences into Conventional Arctic Shipping Risk Models. Applied Sciences. 2020; 10 (8):2937.

Chicago/Turabian Style

Thomas Browne; Rocky Taylor; Brian Veitch; Pentti Kujala; Faisal Khan; Doug Smith. 2020. "A Framework for Integrating Life-Safety and Environmental Consequences into Conventional Arctic Shipping Risk Models." Applied Sciences 10, no. 8: 2937.

Journal article
Published: 21 February 2019 in Journal of Offshore Mechanics and Arctic Engineering
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Data scarcity has always been a significant challenge in the domain of human reliability analysis (HRA). The advancement of simulation technologies provides opportunities to collect human performance data that can facilitate both the development and validation paradigms of HRA. The potential of simulator data to improve HRA can be tapped through the use of advanced machine learning tools like Bayesian methods. Except for Bayesian networks, Bayesian methods have not been widely used in the HRA community. This paper uses a Bayesian method to enhance human error probability (HEP) assessment in offshore emergency situations using data generated in a simulator. Assessment begins by using constrained noninformative priors to define the HEPs in emergency situations. An experiment is then conducted in a simulator to collect human performance data in a set of emergency scenarios. Data collected during the experiment are used to update the priors and obtain informed posteriors. Use of the informed posteriors enables better understanding of the performance, and a more reliable and objective assessment of human reliability, compared to traditional assessment using expert judgment.

ACS Style

Mashrura Musharraf; Allison Moyle; Faisal Khan; Brian Veitch. Using Simulator Data to Facilitate Human Reliability Analysis. Journal of Offshore Mechanics and Arctic Engineering 2019, 141, 021607 .

AMA Style

Mashrura Musharraf, Allison Moyle, Faisal Khan, Brian Veitch. Using Simulator Data to Facilitate Human Reliability Analysis. Journal of Offshore Mechanics and Arctic Engineering. 2019; 141 (2):021607.

Chicago/Turabian Style

Mashrura Musharraf; Allison Moyle; Faisal Khan; Brian Veitch. 2019. "Using Simulator Data to Facilitate Human Reliability Analysis." Journal of Offshore Mechanics and Arctic Engineering 141, no. 2: 021607.

Journal article
Published: 17 January 2019 in Journal of Offshore Mechanics and Arctic Engineering
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The research investigates the influence of human expertise on the effectiveness of ice management operations. The key contribution is an experimental method for investigating human factor issues in an operational setting. Ice management is defined as a systematic operation that enables a marine operation to proceed safely in the presence of sea ice. In this study, the effectiveness of ice management operations was assessed in terms of ability to modify the presence of pack ice around an offshore structure. This was accomplished in a full-mission marine simulator as the venue for a systematic investigation. In the simulator, volunteer participants from a range of seafaring experience levels were tasked with individually completing ice management tasks. Recorded from 36 individuals' simulations, we compared ice management effectiveness metrics against two independent variables: (i) experience level of the participant, categorized as either cadet or seafarer and (ii) ice severity, measured in ice concentration. The results showed a significant difference in ice management effectiveness between experience categories. We examined what the seafarers did that made them more effective and characterized their operational tactics. The research provides insight into the relative importance of vessel operator skills in contributing to effective ice management, as well as how this relative importance changes as ice conditions vary from mild to severe. This may have implications for training in the nautical sciences and could help to inform good practices in ice management.

ACS Style

Erik Veitch; David Molyneux; Jennifer Smith; Brian Veitch. Investigating the Influence of Bridge Officer Experience on Ice Management Effectiveness Using a Marine Simulator Experiment. Journal of Offshore Mechanics and Arctic Engineering 2019, 141, 1 .

AMA Style

Erik Veitch, David Molyneux, Jennifer Smith, Brian Veitch. Investigating the Influence of Bridge Officer Experience on Ice Management Effectiveness Using a Marine Simulator Experiment. Journal of Offshore Mechanics and Arctic Engineering. 2019; 141 (4):1.

Chicago/Turabian Style

Erik Veitch; David Molyneux; Jennifer Smith; Brian Veitch. 2019. "Investigating the Influence of Bridge Officer Experience on Ice Management Effectiveness Using a Marine Simulator Experiment." Journal of Offshore Mechanics and Arctic Engineering 141, no. 4: 1.

Journal article
Published: 14 August 2018 in ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
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Offshore petroleum platforms present complex, time-sensitive situations that can make emergency evacuations difficult to manage. Virtual environments (VE) can train safety-critical tasks and help prepare personnel to respond to real-world offshore emergencies. Before industries can adopt VE training, its utility must be established to ensure the technology provides effective training. This paper presents the results of two experiments that investigated the training utility of VE training. The experiments focused particularly on determining the most appropriate method to deliver offshore emergency egress training using a virtual environment. The first experiment used lecture-based teaching (LBT). The second experiment investigated the utility of a simulation-based mastery learning (SBML) pedagogical method from the medical field to address offshore emergency egress training. Both training programs (LBT and SBML) were used to train naĂŻve participants in basic onboard familiarization and emergency evacuation procedures. This paper discusses the training efficacy of the SBML method in this context and compares the results of the SBML experimental study to the results of the LBT training experiment. Efficacy of the training methods is measured by a combination of time spent training and performance achieved by each of the training groups. Results show that the SBML approach to VE training was more time effective and produced better performance in the emergency scenarios. SBML training can help address individual variability in competence. Limitations to the SBML training are discussed and recommendations to improve the delivery of SBML training are presented. Overall, the results indicate that employing SBML training in industry can improve human reliability during emergencies through increased competence and compliance.

ACS Style

Jennifer Smith; Brian Veitch. A Better Way to Train Personnel to Be Safe in Emergencies. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg 2018, 5, 011003 .

AMA Style

Jennifer Smith, Brian Veitch. A Better Way to Train Personnel to Be Safe in Emergencies. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg. 2018; 5 (1):011003.

Chicago/Turabian Style

Jennifer Smith; Brian Veitch. 2018. "A Better Way to Train Personnel to Be Safe in Emergencies." ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg 5, no. 1: 011003.

Conference paper
Published: 28 June 2018 in Advances in Intelligent Systems and Computing
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Retention of egress skills is critical during high-stress emergencies on offshore oil and gas platforms. This paper uses a virtual offshore platform to investigate the long-term retention of emergency egress competence. A two-phased empirical experiment was designed to first teach basic egress skills and subsequently assess skill retention. The first phase of the experiment used a simulation based mastery learning (SBML) pedagogical approach to teach 36 naĂŻve subjects the necessary spatial and procedural skills to evacuate safely from an offshore platform. In the second phase of the experiment, the same participants were tested after 6 to 9 months on their ability to respond to a series of egress test scenarios. These results indicated that emergency egress skills are susceptible to skill decay. Recommendations to improve the retention of offshore egress skills are discussed.

ACS Style

Jennifer Smith; Kyle Doody; Brian Veitch. Experimental Investigation of the Retention of Emergency Egress Competence Acquired in a Virtual Environment. Advances in Intelligent Systems and Computing 2018, 43 -53.

AMA Style

Jennifer Smith, Kyle Doody, Brian Veitch. Experimental Investigation of the Retention of Emergency Egress Competence Acquired in a Virtual Environment. Advances in Intelligent Systems and Computing. 2018; ():43-53.

Chicago/Turabian Style

Jennifer Smith; Kyle Doody; Brian Veitch. 2018. "Experimental Investigation of the Retention of Emergency Egress Competence Acquired in a Virtual Environment." Advances in Intelligent Systems and Computing , no. : 43-53.

Journal article
Published: 13 June 2018 in Reliability Engineering & System Safety
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Offshore emergency conditions are dynamic in nature and personnel on board are challenged with high risk, time pressure, uncertainty, and the complexity of the situation. This paper investigates how different attributes of emergency scenarios influence people's choice of egress route subsequent to training. An empirical study was carried out in a virtual environment (VE) with 17 naïve participants. The participants were trained to muster during emergencies using a lecture based training (LBT) approach. Training sessions in LBT consisted of computer based training tutorials and simulated training scenarios. Participants’ performance was then tested in simulated testing scenarios. It was observed that given the same training, people used different sets of attributes to make decisions on the egress route. This can help to diagnose causes of poor performance and to design adaptive training lessons. Such identification can also help in the assessment of the efficacy of the training curriculum, or the pedagogical approach. To evaluate the prediction accuracy of the decision trees, the outcomes were compared to the actual observed outcomes of the participants in scenarios in the testing data set. Results show an average of 95% prediction accuracy of the decision trees.

ACS Style

Mashrura Musharraf; Jennifer Smith; Faisal Khan; Brian Veitch. Identifying route selection strategies in offshore emergency situations using decision trees. Reliability Engineering & System Safety 2018, 194, 106179 .

AMA Style

Mashrura Musharraf, Jennifer Smith, Faisal Khan, Brian Veitch. Identifying route selection strategies in offshore emergency situations using decision trees. Reliability Engineering & System Safety. 2018; 194 ():106179.

Chicago/Turabian Style

Mashrura Musharraf; Jennifer Smith; Faisal Khan; Brian Veitch. 2018. "Identifying route selection strategies in offshore emergency situations using decision trees." Reliability Engineering & System Safety 194, no. : 106179.

Journal article
Published: 07 May 2018 in Journal of Marine Science and Engineering
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The propeller jet from a ship has a significant component directed upwards towards the free surface of the water, which can be used for ice management. This paper describes a comprehensive laboratory experiment where the operational factors affecting a propeller wake velocity field were investigated. The experiment was conducted using a steady wake field to investigate the characteristics of the axial velocity of the fluid in the wake and the corresponding variability downstream of the propeller. The axial velocities and the variability recorded were time-averaged. Propeller rotational speed was found to be the most significant factor, followed by propeller inclination. The experimental results also provide some idea about the change of the patterns of the mean axial velocity distribution against the factors considered for the test throughout the effective wake field, as well as the relationships to predict the axial velocity for known factors.

ACS Style

Asif Amin; Bruce Colbourne; Brian Veitch. Experimental Investigation of Propeller Wake Velocity Field to Determine the Major Factors Affecting Propeller Wake Wash. Journal of Marine Science and Engineering 2018, 6, 50 .

AMA Style

Asif Amin, Bruce Colbourne, Brian Veitch. Experimental Investigation of Propeller Wake Velocity Field to Determine the Major Factors Affecting Propeller Wake Wash. Journal of Marine Science and Engineering. 2018; 6 (2):50.

Chicago/Turabian Style

Asif Amin; Bruce Colbourne; Brian Veitch. 2018. "Experimental Investigation of Propeller Wake Velocity Field to Determine the Major Factors Affecting Propeller Wake Wash." Journal of Marine Science and Engineering 6, no. 2: 50.

Journal article
Published: 12 April 2018 in Engineering Applications of Artificial Intelligence
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Route learning is an essential activity for a person visiting a new environment. The element of forgetting a location (called decision point) along a route, where a change in direction is needed, is of immense importance especially during emergency evacuation scenarios. It is this element that has not been given the attention it deserves in developing a route learning algorithm. This work proposes a model of route learning in a new environment based on landmarks using generalized stochastic Petri nets because landmarks based route learning has been observed as a method natural to humans. The model takes information about landmarks along a route and associated navigation commands and then chooses whether to save this information as part of the learned route or not. The selection is made by exploiting stochastic transitions for which the firing rates are dependent on the type of landmark encountered at a decision point. The final output is a route having some decision points missing; resembling the situation that humans encounter after they visit a route in a new environment. The model results closely match empirical results obtained with human subjects.

ACS Style

Syed Nasir Danial; F. Khan; B. Veitch. A Generalized Stochastic Petri Net model of route learning for emergency egress situations. Engineering Applications of Artificial Intelligence 2018, 72, 170 -182.

AMA Style

Syed Nasir Danial, F. Khan, B. Veitch. A Generalized Stochastic Petri Net model of route learning for emergency egress situations. Engineering Applications of Artificial Intelligence. 2018; 72 ():170-182.

Chicago/Turabian Style

Syed Nasir Danial; F. Khan; B. Veitch. 2018. "A Generalized Stochastic Petri Net model of route learning for emergency egress situations." Engineering Applications of Artificial Intelligence 72, no. : 170-182.

Preprint
Published: 01 April 2018
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The propeller jet from a ship has a significant component directed upwards towards the free surface of the water, which can be used for ice management. This paper describes a comprehensive laboratory experiment where the influences of operational factors affecting a propeller wake velocity field were investigated. The experiment was done on a steady wake field to investigate the characteristics of the axial velocity of the fluid in the wake and the corresponding variability downstream of the propeller. The axial velocities and the variability recorded were time-averaged. Propeller rotational speed was found to be the most significant factor, followed by propeller inclination. The experimental results also provide some idea about the change of the patterns of the mean axial velocity distribution against the factors considered for the test throughout the effective wake field, as well as the relationships to predict the axial velocity for known factors.

ACS Style

Asif Amin; Bruce Colbourne; Brian Veitch. Experimental Investigation of propeller Wake Velocity Field for the Major Factors Affecting Propeller Wake Wash. 2018, 1 .

AMA Style

Asif Amin, Bruce Colbourne, Brian Veitch. Experimental Investigation of propeller Wake Velocity Field for the Major Factors Affecting Propeller Wake Wash. . 2018; ():1.

Chicago/Turabian Style

Asif Amin; Bruce Colbourne; Brian Veitch. 2018. "Experimental Investigation of propeller Wake Velocity Field for the Major Factors Affecting Propeller Wake Wash." , no. : 1.

Journal article
Published: 01 January 2018 in TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation
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ACS Style

Doug Smith; Brian Veitch; Faisal Khan; Rocky Taylor. Using the FRAM to Understand Arctic Ship Navigation: Assessing Work Processes During the Exxon Valdez Grounding. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation 2018, 12, 447 -457.

AMA Style

Doug Smith, Brian Veitch, Faisal Khan, Rocky Taylor. Using the FRAM to Understand Arctic Ship Navigation: Assessing Work Processes During the Exxon Valdez Grounding. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation. 2018; 12 (3):447-457.

Chicago/Turabian Style

Doug Smith; Brian Veitch; Faisal Khan; Rocky Taylor. 2018. "Using the FRAM to Understand Arctic Ship Navigation: Assessing Work Processes During the Exxon Valdez Grounding." TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation 12, no. 3: 447-457.

Journal article
Published: 01 January 2018 in Ocean Engineering
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ACS Style

Norafneeza Norazahar; Jennifer Smith; Faisal Khan; Brian Veitch. The use of a virtual environment in managing risks associated with human responses in emergency situations on offshore installations. Ocean Engineering 2018, 147, 621 -628.

AMA Style

Norafneeza Norazahar, Jennifer Smith, Faisal Khan, Brian Veitch. The use of a virtual environment in managing risks associated with human responses in emergency situations on offshore installations. Ocean Engineering. 2018; 147 ():621-628.

Chicago/Turabian Style

Norafneeza Norazahar; Jennifer Smith; Faisal Khan; Brian Veitch. 2018. "The use of a virtual environment in managing risks associated with human responses in emergency situations on offshore installations." Ocean Engineering 147, no. : 621-628.

Journal article
Published: 01 January 2018 in Reliability Engineering & System Safety
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ACS Style

Bushra Khan; Faisal Khan; Brian Veitch; Ming Yang. An operational risk analysis tool to analyze marine transportation in Arctic waters. Reliability Engineering & System Safety 2018, 169, 485 -502.

AMA Style

Bushra Khan, Faisal Khan, Brian Veitch, Ming Yang. An operational risk analysis tool to analyze marine transportation in Arctic waters. Reliability Engineering & System Safety. 2018; 169 ():485-502.

Chicago/Turabian Style

Bushra Khan; Faisal Khan; Brian Veitch; Ming Yang. 2018. "An operational risk analysis tool to analyze marine transportation in Arctic waters." Reliability Engineering & System Safety 169, no. : 485-502.

Journal article
Published: 01 June 2017 in Safety Science
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ACS Style

Norafneeza Norazahar; Faisal Khan; Brian Veitch; Scott MacKinnon. Prioritizing safety critical human and organizational factors of EER systems of offshore installations in a harsh environment. Safety Science 2017, 95, 171 -181.

AMA Style

Norafneeza Norazahar, Faisal Khan, Brian Veitch, Scott MacKinnon. Prioritizing safety critical human and organizational factors of EER systems of offshore installations in a harsh environment. Safety Science. 2017; 95 ():171-181.

Chicago/Turabian Style

Norafneeza Norazahar; Faisal Khan; Brian Veitch; Scott MacKinnon. 2017. "Prioritizing safety critical human and organizational factors of EER systems of offshore installations in a harsh environment." Safety Science 95, no. : 171-181.

Journal article
Published: 01 April 2017 in Journal of Environmental Chemical Engineering
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ACS Style

Mawuli Afenyo; Faisal Khan; Brian Veitch; Ming Yang. A probabilistic ecological risk model for Arctic marine oil spills. Journal of Environmental Chemical Engineering 2017, 5, 1494 -1503.

AMA Style

Mawuli Afenyo, Faisal Khan, Brian Veitch, Ming Yang. A probabilistic ecological risk model for Arctic marine oil spills. Journal of Environmental Chemical Engineering. 2017; 5 (2):1494-1503.

Chicago/Turabian Style

Mawuli Afenyo; Faisal Khan; Brian Veitch; Ming Yang. 2017. "A probabilistic ecological risk model for Arctic marine oil spills." Journal of Environmental Chemical Engineering 5, no. 2: 1494-1503.

Journal article
Published: 01 March 2017 in Ocean Engineering
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ACS Style

Mawuli Afenyo; Faisal Khan; Brian Veitch; Ming Yang. Arctic shipping accident scenario analysis using Bayesian Network approach. Ocean Engineering 2017, 133, 224 -230.

AMA Style

Mawuli Afenyo, Faisal Khan, Brian Veitch, Ming Yang. Arctic shipping accident scenario analysis using Bayesian Network approach. Ocean Engineering. 2017; 133 ():224-230.

Chicago/Turabian Style

Mawuli Afenyo; Faisal Khan; Brian Veitch; Ming Yang. 2017. "Arctic shipping accident scenario analysis using Bayesian Network approach." Ocean Engineering 133, no. : 224-230.

Journal article
Published: 01 January 2017 in Journal of Loss Prevention in the Process Industries
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ACS Style

Doug Smith; Brian Veitch; Faisal Khan; Rocky Taylor. Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches. Journal of Loss Prevention in the Process Industries 2017, 45, 88 -101.

AMA Style

Doug Smith, Brian Veitch, Faisal Khan, Rocky Taylor. Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches. Journal of Loss Prevention in the Process Industries. 2017; 45 ():88-101.

Chicago/Turabian Style

Doug Smith; Brian Veitch; Faisal Khan; Rocky Taylor. 2017. "Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches." Journal of Loss Prevention in the Process Industries 45, no. : 88-101.

Journal article
Published: 01 October 2016 in Marine Pollution Bulletin
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Highlights•Novel fugacity based model for fate and transport modeling for icy condition•Testing and validation of the model•Application to ice and cold region AbstractImproved understanding of ecological risk associated with Arctic shipping would help advance effective oil spill prevention, control, and mitigation strategies. Ecological risk assessment involves analysis of a release (oil), its fate, and dispersion, and the exposure and intake of the contaminant to different receptors. Exposure analysis is a key step of the detailed ecological risk assessment, which involves the evaluation of the concentration and persistence of released pollutants in the media of contact. In the present study, a multimedia fate and transport model is presented, which is developed using a fugacity-based approach. This model considers four media: air, water, sediment, and ice. The output of the model is the concentration of oil (surrogate hydrocarbons-naphthalene) in these four media, which constitutes the potential exposure to receptors. The concentration profiles can subsequently be used to estimate ecological risk thereby providing guidance to policies for Arctic shipping operations, ship design, and ecological response measures.

ACS Style

Mawuli Afenyo; Faisal Khan; Brian Veitch; Ming Yang. Dynamic fugacity model for accidental oil release during Arctic shipping. Marine Pollution Bulletin 2016, 111, 347 -353.

AMA Style

Mawuli Afenyo, Faisal Khan, Brian Veitch, Ming Yang. Dynamic fugacity model for accidental oil release during Arctic shipping. Marine Pollution Bulletin. 2016; 111 (1):347-353.

Chicago/Turabian Style

Mawuli Afenyo; Faisal Khan; Brian Veitch; Ming Yang. 2016. "Dynamic fugacity model for accidental oil release during Arctic shipping." Marine Pollution Bulletin 111, no. 1: 347-353.