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Having empathy and being able to empathize refer to the ability to understand, view or feel the experiences and perspectives of others. In a work context, the ability of different actors to empathize with others can have positive effects in the design, organization and operations of complex systems. This article explores the value of empathy within safety-critical work systems and discusses the role of empathy as an entry point for user-centred approaches. We use the maritime domain to illustrate why developing empathic skills and knowledge has relevance and added-value for (1) maritime design and designers, and (2) future remote maritime operations and operators of unmanned vessels. We detail our emerging approaches and methods for developing empathy as a tool to enhance interdisciplinary understanding and knowledge sharing with the overall goal of improving Human Factors utilization in applied work contexts.

While society benefits from Arctic shipping, it is necessary to recognize that ship operations in Arctic waters pose significant risks to people, the environment, and property. To support the management of those risks, this article presents a comprehensive approach addressing both short-term operational risks, as well as risks related to long-term extreme ice loads. For the management of short-term operational risks, an extended version of the Polar Operational Limit Assessment Risk Indexing System (POLARIS) considering the magnitude of the consequences of potential adverse events is proposed. For the management of risks related to long-term extreme ice loads, guidelines are provided for using existing analytical, numerical, and semi-empirical methods. In addition, to support the design of ice class ship structures, the article proposes a novel approach that can be used in the conceptual design phase for the determination of preliminary scantlings for primary hull structural members.

Publisher Copyright: © 2022 IEEE. For the offshore energy industry, virtual environment technology can enhance conventional training by teaching basic offshore safety protocols such as onboard familiarization and emergency evacuation. Virtual environments have the added benefit of being used to investigate the impact of different training approaches on competence. This pilot study uses decision tree modeling to examine the efficacy of two pedagogical approaches, simulation-based mastery learning (SBML) and lecture-based training (LBT), in a virtual environment. Decision trees are an inductive reasoning approach that can be used to identify learners' egress strategies in offshore emergencies after training. The efficacy of the virtual training is evaluated in three ways: 1) analyzing participants' performance scores in test scenarios; 2) comparing the decision tree depiction of participant's understanding of emergency egress to the intended learning objectives; and 3) comparing the decision strategies developed under a different pedagogical approach. A comparison of the resulting decision trees from the SBML training with trees generated from the LBT showed that the different training methods influenced the participants' egress strategies. The SBML approach resulted in concise decision trees and better route selection strategies when compared to the LBT training. This pilot study demonstrates the diagnostic capabilities of decision trees as training assessment tools and recommends integrating decision trees into virtual training to better support the learning needs of individuals and deliver adaptive training scenarios.

The objective of this review was to examine and map the literature on the use of the Functional Resonance Analysis Method (FRAM) in health care research.The FRAM is a resilient health care tool tat offers an approach to deconstruct complex systems by mapping health care processes to identify essential activities, how they are interrelated, and the variability that emerges, which can strengthen or compromise outcomes. Insight into how the FRAM has been operationalized in health care can help researchers and policy-makers understand how this method can be used to strengthen health care systems.This scoping review included research and narrative reports on the application of the FRAM in any health care setting. The focus was to identify the key concepts and definitions used to describe the FRAM; the research questions, aims, and objectives used to study the FRAM; the methods used to operationalize the FRAM; the health care processes examined; and the key findings.A three-step search strategy was used to find published and unpublished research and narrative reports conducted in any country. Only papers published in English were considered. No limits were placed on the year of publication. CINAHL, MEDLINE, Embase, PsycINFO, Inspec Engineering Village, ProQuest NursingAllied Health were searched originally in June 2020 and again in March 2021. A search of the gray literature was also completed in March 2021. Data were extracted from papers by two independent reviewers using a data extraction tool developed by the reviewers. Search results are summarized in a flow diagram, and the extracted data are presented in tabular format.Thirty-one papers were included in the final review, and most (n = 25; 80.6%) provided a description or definition of the FRAM. Only two (n = 2; 6.5%) identified a specific research question. The remaining papers each identified an overall aim or objective in applying the FRAM, the most common being to understand a health care process (n = 20; 64.5%). Eleven different methods of data collection were identified, with interviews being the most common (n = 21; 67.7%). Ten different health care processes were explored, with safety and risk identification (n = 8; 25.8%) being the most examined process. Key findings identified the FRAM as a mapping tool that can identify essential activities or functions of a process (n = 20; 64.5%), how functions are interdependent or coupled (n = 18; 58.1%), the variability that can emerge within a process (n = 20; 64.5%), discrepancies between work as done and work as imagined (n = 20; 64.5%), the resiliency that exists within a process (n = 12; 38.7%), and the points of risk within a process (n = 10, 32.3%). Most papers (n = 27; 87.1%) developed models representing the complexity of a process.The FRAM aims to use a systems approach to examine complex processes and, as evidenced by this review, is suited for use within the health care domain. Interest in the FRAM is growing, with most of the included literature being published since 2017 (n = 24; 77.4%). The FRAM has the potential to provide comprehensive insight into how health care work is done and how that work can become more efficient, safer, and better supported.

Risk aggregation is the process of combining multiple individual risks to develop a better understanding of the overall risk on a system. Different risks can have different consequences and different units of measure. This study contributes to the process of risk aggregation by presenting a general method to combine multiple consequences posed by an Arctic ship accident. The method considers ecological and socio-economic consequences of a potential oil spill, and life-safety consequences of a potential ship evacuation. Existing models for each consequence type are adopted. Individual consequence types are monetized and combined to quantify total consequence cost for a given accident scenario. A framework is proposed to assign a qualitative rating for total consequence severity. The qualitative scales of the framework are established using the quantitative method. Total consequence severity is evaluated for different ship types and regions in the Canadian Arctic. Results indicate that Arctic ship accidents involving oil tankers in environmentally sensitive regions and cruise ships in regions associated with long response times are worst-case scenarios, with similar total consequence severity levels. Implications for safe Arctic shipping are that on the basis of total consequence severity, mitigating the potential consequence severity of Arctic cruise operations is of near equal priority to that of Arctic tanker operations. Evaluating total consequence severity of potential Arctic ship accidents provides decision-makers and risk analysts with a data-driven tool to integrate multidisciplinary knowledge for the assessment, management, and communication of Arctic shipping risks.

Arctic shipping is growing driven by a demand for natural resources, climate change, and technological development, among other factors. While this provides many benefits for society, it also entails risks for people, the environment, and property. The purpose of this article is to assist ship designers, operators, owners, and other stakeholders in managing those risks by defining a comprehensive approach to scenario-based risk management for Arctic waters. The approach covers both the management of short-term operational risks, as well as of risks related to a ship’s long-term extreme (design) ice loads and structural response. For operational risk management, a further developed version of the established Polar Operational Limitations Assessment Risk Indexing System (POLARIS) method is defined. In contrast to the established method, the further developed version considers the consequences of potential accidental events. For managing risks related to a ship’s long-term extreme ice loads and structural response, guidelines are provided for the application of existing methods of assessing ice loads, including analytical, numerical, and semi-empirical methods. In addition, to support the design of ice class ship structures, a new approach based on closed-form expressions is defined that can be used in the conceptual design phase to determine preliminary scantlings of primary hull structural members (e.g., transverse web frames).

Ship evacuations in Arctic waters expose crew and passengers to the potential for severe life-safety consequences. Effective planning and allocation of Arctic SAR services should be supported by evidence-based assessments of the consequence severity of ship evacuations. This paper provides an evaluation of life-safety consequence severity for ship evacuation scenarios in the Canadian Arctic. Exposure time is a predominant factor influencing consequence severity of Arctic ship evacuations. The study integrates existing models for exposure time estimation [1,2] and life-safety consequence [3]. Exposure time is estimated for air- and marine-based SAR assets, considering associated capacities, speeds, and operating ranges. Evacuation scenario factors include ship type, number of POB, and geographic location. The methodology provides Arctic SAR service providers with a tool for planning effective resource allocation and response efforts. As a base case, exposure times and consequence severities are estimated for SAR response with a single asset. The effect of deploying multiple SAR assets is demonstrated. Results indicate that the deployment of air-based SAR assets contributes to reduced exposure times and mitigation of life-safety consequence severity. Evacuation of a passenger vessel is a worst-case scenario. Evacuations of high POB vessels require the deployment of multiple SAR assets to prevent potential disastrous life-safety consequences.

In this research, an experimental method was used to evaluate the effects of a novel digital Decision Support System (DSS) for maritime ice management operations. This was done using a marine bridge simulator to determine whether the use of this technology can improve ice management performance. The experiment used the DSS to assist seafaring cadets in simulated emergency ice management operations. The operation scenarios saw the cadets use a supply vessel under their control to clear sea ice from the lifeboat launch zone of an offshore installation. The DSS allowed participants to request assistance, which was provided through text-based instructions and visual replay of a previous approach from a past study. The cadets usually adopted the strategy recommended by the DSS, but did not demonstrate statistically significant performance improvements, which we attribute to their lack of ship handling experience. This research provides insight into the efficacy of decision support technology in presenting advice for ice management activities. It also describes an effective method for evaluating this technology.

Autonomous systems are becoming more common in the maritime industries. Some marine operations are more straightforward to automate than others. For example, the navigation of local vessels can be more easily automated on account of well-established standard operating procedures and relatively predictable operating conditions. Complex operations, such as ice management for ocean going ships, are less amenable to automation. Ice management is complex in nature and often takes place under challenging circumstances that include harsh weather, time constraints, and multiple information sources. The conditions are dynamic, and it is difficult to predict precisely how the circumstances will evolve. Successful ice management depends both on prior managerial planning and on-the-job adjustments made by the seafarers in dynamic operational conditions. To reach the level of autonomy equivalent to expert seafarers, the machine needs to assess, reason, decide, and act like them. One way to achieve this is to train the machine by the experts. This paper presents how the Functional Resonance Analysis Method (FRAM) can facilitate such training by connecting the human (expert seafarers) and the machine. A FRAM model of the ice-management operation will be used to 1) understand the ice management operation from experts and visualize it, 2) identify the salient features used and the on-job-adjustments made by experts during the operation, and 3) guide the digitization of the collected knowledge for ice-management automation.

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.

The assessment of lifeboat coxswain performance in operational scenarios representing offshore emergencies has been prohibitive due to risk. For this reason, human performance in plausible emergencies is difficult to predict due to the limited data that is available. The advent of lifeboat simulation provides a means to practice in weather conditions representative of an offshore emergency. In this paper, we present a methodology to create probabilistic models to study this new problem space using Bayesian Networks (BNs) to formulate a model of competence. We combine expert input and simulator data to create a BN model of the competence of slow-speed maneuvering (SSM). We demonstrate how the model is improved using data collected in an experiment designed to measure performance of coxswains in an emergency scenario. We illustrate how this model can be used to predict performance and diagnose background information about the student. The methodology demonstrates the use of simulation and probabilistic methods to increase domain awareness where limited data is available. We discuss how the methodology can be applied to improve predictions and adapt training using machine learning.

This paper proposes an Object-Oriented Bayesian Network model to study oil spill mitigative measures using cost-effectiveness as a decision-making factor. The cost-effectiveness is defined as the ratio of dollar spent to the efficiency of oil removal in the spill area. The proposed model considers the complexity, lack of data, and uncertainties in an oil spill and response modelling scenario for Arctic shipping accident occurrence. The crude oil release, slick formation, weathering and transport, ecological impact, and response scenarios are modelled using Bayesian Network. Application of the model is demonstrated using a hypothetical scenario of an oil spill involving a ship in the Arctic region. The results show that a combination of in-situ burning, mechanical and manual recovery, and use of dispersants would cost the highest, while the use of in-situ burning, and dispersants gives the cheapest option. The study provides a deeper understanding of oil spill dynamics and effectiveness of the response techniques. The proposed model could serve as a useful tool for oil spill response decision-making.

Low- and non-ice-class ship-ice interactions are modelled with a shared-energy approach, which typically models the internal mechanics with nonlinear finite element methods. For applications like the preliminary design phase and quick operational assessments of the ship’s structural capabilities, a finite element shared-energy approach can be time consuming and information intensive, therefore, an analytical share-energy algorithm is proposed. The proposed algorithm applies the upper bound energy methodology by equating the external collision energy, determined with the Popov collision model (Popov, et al., 1967), to the sum of the internal ice and structural response energies. The distribution of the internal energy, between the ice and the structure, is determined by iterating through possible shared contact forces until the sum of the internal response energies equals the external energy introduced into the system. The ice-crushing energy is modelled with Daley’s (1999) energy based ice collision force models, and the internal structural strain energy is modelled through a combination of classical beam theory and design of experiments methodology. The proposed model is benchmarked against a finite element ice wedge-ship grillage structure interaction.

In training, task proficiency is typically assessed through relevant performance measures. Such measures provide information about how effectively the individual can perform the task, but they give...

Situation awareness is the first and most important step in emergency management. It is a dynamic step involving evolving conditions and environments. It is an area of active research. This study presents a Markov Logic Network to model SA focusing on fire accidents and emergency evacuation. The model has been trained using empirical data obtained from case studies. The case studies involved human participants who were trained for responding to emergencies involving fire and smoke using a virtual environment. The simulated (queried) and empirical findings are reasonably consistent. The proposed model enables implementing an agent that exploits environmental cues and cognitive states to determine the type of emergency currently being faced. Considering each emergency type as a situation, the model can be used to develop a repertoire of situations for agents so that the repertoire can act as an agent’s experience for later decision-making.

Lifeboats are essential life-saving equipment for all types of water-going vessels and offshore platforms. Lifeboat simulators have been created specifically for offshore personnel to practice in conditions that are normally too risky for live training. As simulation training is a relatively new alternative, there is a need to assess how training performed with a simulator compares with conventional training. This study was performed to evaluate how skills acquired with different training approaches transferred to an emergency scenario. Over a period of one year, participants received quarterly training in one of three ways: using live boats, computer-based training or a simulator. Following training, participants were evaluated on their ability to launch and manoeuvre a lifeboat in a plausible emergency. The study results suggest a benefit to performing training with realistic lifeboat controls and practicing using representative emergency scenarios. Insights are provided on how training can be modified to increase competence.

The retention of safety-critical egress skills is an essential part of emergency preparedness on offshore petroleum platforms. Virtual environment (VE) training has been shown to be an effective method for teaching basic onboard familiarization and offshore emergency evacuation procedures. This technology has the potential to train crews before they are deployed offshore. This paper investigates the long-term retention and maintenance of emergency egress competence obtained using a virtual offshore platform. In particular, the research aimed to answer two questions: (1) what egress skills can be remembered after a period of 6 months? and (2) how effective is a VE-based retraining program at maintaining egress skills? A two-phased experiment was designed to first teach basic egress skills and subsequently assess skill retention after a 6- to 9-month period. The first phase of the experiment used a simulation-based mastery learning (SBML) pedagogical approach to teach naive subjects the necessary spatial and procedural skills to evacuate safely. In the second phase of the experiment, the same 36 participants were tested after the retention interval on their ability to respond to a series of egress test scenarios. Participants who had trouble remembering the egress procedures were provided retraining on deficient skills. The results of the experiment indicate that emergency egress skills (both spatial and procedural knowledge) are susceptible to skill decay. This paper will highlight the skills that were most susceptible to skill fade after a period of 6 to 9 months and discuss the efficacy of the retraining participants received to return to competence.

The Piper Alpha disaster (1988) witnessed 167 casualties. The offshore safety guidelines developed afterward highlighted the need for effective and regular training to overcome the problems in evacuation procedures. Today, virtual environments are effective training platforms due to high-end audio/visual and interactive capabilities. These virtual environments exploit agents with human-like steering capabilities, but with limited or no capacity to learn routes. This work proposes a sequential route learning methodology for agents that resembles the way people learn routes. The methodology developed here exploits a generalized stochastic Petri-net based route learning model iteratively. The simulated results are compared with the route learning strategies of human participants. The data on human participants were collected by the authors from an earlier study in a virtual environment. The main contribution lies in modeling people’s route learning behavior over the course of successive exposures. It is found that the proposed methodology models human-like sequential route learning if there are no easy detours from the original escape route. Although the model does not accurately capture individual learning strategies for all decision nodes, it can be used as a model of compliant, rule-following training guides for a virtual environment.

The growing interest for Arctic and Antarctic shipping activities due to the decreasing ice cover will also increase the risks of accidents on these waters. The design of ships for ice has traditionally been based on the practical experience without a clear link to the physics of the ship-ice interaction. The rules are, however, getting more towards the goal based approach, which require good knowledge of all the various element important for design. Risk based ship design (RBSD) is also widely applied e.g. for the passengers ships. Therefore, the scope of this paper is the review of the knowledge necessary for RBSD for Arctic conditions. The main focus is on ice loads and ship structures. Accident prevention and environmental consequences of oil spills are also discussed, but more briefly. In risk analysis, there is a recent focus on the treatment of uncertainty, or conversely, the strength of knowledge underlying the risk quantification. In light of this, the review is performed with specific focus on the strength of evidence of the different fields of knowledge needed to perform RBSD in ice conditions. The results indicate that the risk based design for Arctic operations is challenging as the ice environment, together with all the possible ship-ice contact scenarios, are complicated to define properly, especially on proper probabilistic terms, The main challenges are still related how to describe the ship-ice interaction parameters such as ship-ice contact characteristics, pressure distributions, and load levels in all the various ice conditions. In addition, the possible environmental consequences of the accidents need further research. Finally, human factors need to be incorporated in risk analysis techniques.

The offshore oil industry functions in a team work culture, in which operations depend not only on individuals’ competency, but also on team skills. Team skills are even more necessary when it comes to handling emergency conditions as they challenge personnel on board with high risk, time pressure, and complexity. This raises the need for training that goes beyond conventional training programs and incorporates team skills exercises. The major difficulty to design such training is that it involves practicing emergency exercises with a potentially large number of participants. Such large-scale team exercises suffer from both organizational and educational drawbacks. One solution to this problem is to use artificial agents that can reproduce the behavior of the team members. This paper presents a behavior model that can simulate the response of general personnel during emergency situations. The variability in human behavior is modeled using different performance influencing factors (PIFs). Empirical evidence is used to identify the sources of variability that are encoded in the agents to allow a realistic range of human behaviors. Though variability can come from both physical and mental differences, the focus of this paper is on the later. Focus is given to across-subject variability rather than within-subject variability.

Functional Resonance Analysis Method (FRAM) is a function-based approach to model complex socio-technical systems and to manage variability. The current FRAM related tools are unable to capture qualitative and quantitative characteristics of variability as well as temporal variations. This study presents in detail a dynamic FRAM-based tool, which is called DynaFRAM. It is introduced to address the variability-related deficiencies of the FRAM related tools. It aims to capture variability in complex operations. It is a dynamic tool developed to capture time related variations in complex operations. This increases the attractiveness of the DynaFRAM for complex operations where specialists and practitioners make decisions in complicated situations. The ability of the DynaFRAM is demonstrated by examining a healthcare related case study. Although the ability of the DynaFRAM is assessed through capturing variations in healthcare operations, it can be applied to other domains in a similar manner.•The DynaFRAM is a dynamic FRAM-based tool.•It is able to captures different characteristics of variability.•It facilitates understanding and analysis of variability in complex operations., Graphical abstract Image, graphical abstract

This paper investigates the linkage between the acute impacts on apex marine mammals with polar cod responses to an oil spill. It proposes a Bayesian network-based model to link these direct and indirect effects on the apex marine mammals. The model predicts a recruitment collapse (for the scenarios considered), causing a higher risk of mortality of polar bears, beluga whales, and Narwhals in the Arctic region. Whales (adult and calves) were predicted to be at higher risk when the spill was under thick ice, while adult polar bears were at higher risk when the spill occurred on thin ice. A spill over the thick ice caused the least risk to whale and adult polar bears. The spill's timing and location have a significant impact on the animals in the Arctic region due to its unique sea ice dynamics, simple food web, and short periods of food abundance.

OBJECTIVE The objective of this review was to examine and map the literature on the use of the Functional Resonance Analysis Method (FRAM) in health care research. INTRODUCTION The FRAM is a resilient health care tool that offers an approach to deconstruct complex systems by mapping health care processes to identify essential activities, how they are interrelated, and the variability that emerges, which can strengthen or compromise outcomes. Insight into how the FRAM has been operationalized in health care can help researchers and policy-makers understand how this method can be used to strengthen health care systems. INCLUSION CRITERIA This scoping review included research and narrative reports on the application of the FRAM in any health care setting. The focus was to identify the key concepts and definitions used to describe the FRAM, the research questions, aims, and objectives used to study the FRAM, the methods used to operationalize the FRAM, the health care processes examined, and the key findings. METHODS A three-step search strategy was used to find published and unpublished research and narrative reports conducted in any country. Only papers published in English were considered. No limits were placed on the year of publication. CINAHL, MEDLINE, Embase, PsycINFO, Inspec Engineering Village, ProQuest Nursing & Allied Health were searched originally in June 2020 and again in March 2021. A search of the gray literature was also completed in March 2021. Data were extracted from papers by two independent reviewers using a data extraction tool developed by the reviewers. Search results are summarized in a flow diagram, and the extracted data are presented in tabular format. RESULTS Thirty-one papers were included in the final review, and most (n = 25; 80.6%) provided a description or definition of the FRAM. Only two (n = 2; 6.5%) identified a specific research question. The remaining papers each identified an overall aim or objective in applying the FRAM, the most common being to understand a health care process (n = 20; 64.5%). Eleven different methods of data collection were identified, with interviews being the most common (n = 21; 67.7%). Ten different health care processes were explored, with safety and risk identification (n = 8; 25.8%) being the most examined process. Key findings identified the FRAM as a mapping tool that can identify essential activities or functions of a process (n = 20; 64.5%), how functions are interdependent or coupled (n = 18 58.1%), the variability that can emerge within a process (n = 20; 64.5%), discrepancies between work as done and work as imagined (n = 20; 64.5%), the resiliency that exists within a process (n = 12; 38.7%), and the points of risk within a process (n = 10, 32.3%). Most papers (n = 27; 87.1%) developed models representing the complexity of a process. CONCLUSIONS The FRAM aims to use a systems approach to examine complex processes and as evidenced by this review, is suited for use within the health care domain. Interest in the FRAM is growing, with most of the included literature being published since 2017 (n = 24; 77.4%). The FRAM has the potential to provide comprehensive insight into how health care work is done and how that work can become more efficient, safer, and better supported.

Ballast tanks are expected to be coated according to the IMO Performance Standard for Protective Coating regulations (PSPC15), in addition to the paint application requirements of the paint producer. In general, a coating system should consist of minimum two spray coats of light-colored epoxy coating on flat surfaces with a Nominal total Dry Film Thickness (NDFT) of 320 μm and 90% of all thickness measurements greater than, or equal to the NDFT and none of the remaining measurements below 0.9 x NDFT (the “90/10 rule”). Allegedly, the value of 320 μm in this PSPC15 rule may be misconstrued as a benchmark for coating application on flat surfaces, eventually leading to a non-PSPC15 compliance due to the resulting variation in coating thickness violating this 90/10 rule. This study indicates that over the years, the arithmetic mean in-situ DFT appears to be 498±18 μm and that too high and low thicknesses, below 288 μm and above 800 μm, were noted in the field. Analysis of a survey of ballast tank coating performance of ships indicates that too low thicknesses appear to be negatively impacting the average theoretical ballast tank performance. However, when an application mean DFT benchmark of 525 μm is used, the coating will almost surely comply to the 90/10 rule and the risk of falling below the 288 μm threshold is small, less than 2% in most cases. Consequently, using 320 μm as a mean DFT benchmark could result in a non-PSPC15 compliance with the in-situ ascertained coating thickness variation as this does not exclude coating thicknesses below 288 μm, which may then result in a significantly less than average theoretical coating performance. If the coating application is performed very evenly, the benchmark may be reduced to 429 μm with a probability of falling below 288 μm reduced to 0.1%. It should therefore be emphasized that the PSPC15 requirement is a coating system framework description, and that the requirement should be broadened to include a mean DFT as a coating applicator benchmark together with a clearly specified minimum and maximum DFT, in order to avoid any misinterpretations.

Resilience is a concept that can be used to bring additional understanding to safety management, to complement traditional approaches. The additional understanding will enable more-informed...

Independent safe navigation in ice-covered water is difficult. Icebreaker assistance is required for sailing through ice- covered waters. This poses an additional risk of collision. The study proposes a modified Human Factor Analysis and Classification (HFACS) framework to identify and classify contributing risk factors during a convoy. HFACS integration with Nagel-Schrekenberg (NaSch) model considers an operator’s behaviour and links it with the occurrence of various risk factors. The study finds significant influence in risk from small changes in two new factors, viz., crew reduction and crew overload. For example, based on the sensitivity analysis, it is determined that about a 17% contribution of crew reduction and about a 24% of contribution of crew overload increase the contribution of risk taking by an amount of approximately 93% in the overall risk of accidents. The accident probabilities obtained here will be helpful in decision making concerning safe operations during a convoy.

Operators and regulators of offshore and maritime domains should adopt evidence-based safety training to prepare the workforce for emergency egress. This paper uses pedagogical frameworks and data mining tools to identify training gaps in mandatory offshore safety training, and offers evidence-based virtual environment (VE) training solutions. A VE training setting was used as a human behavior laboratory to provide trainees with artificial experience and record their learning progress in the context of evacuating a virtual offshore petroleum platform during a series of credible emergencies. A longitudinal study was conducted to collect data at three critical learning stages: skill acquisition, retention, and transfer. The empirical evidence identified strengths and deficiencies in the VE training. The modeling provided a more comprehensive assessment of the VE training and demonstrated the utility of data-mining tools for future adaptive training applications.

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.

With the advancement of simulation-based training, intelligent agents that can display human-like behavior have become common. From military combat simulations to nuclear power plant simulation, agents have been widely used to facilitate team training (as team mates, opponents, or both). Credibility of these agents is vital to ensure a sound training process. Credibility of the agents largely depends on the credibility of the underlying human behavior representation (HBR) model. This is why validation of the HBR model is necessary to ensure realistic agent behavior. However, the non-deterministic nature of the HBR and the subjectivity in experts’ judgment during the validation process make HBR model validation more challenging compared to physics based models. This paper presents the validation process of an HBR model of general personnel created for use in an offshore emergency training simulator. Three types of agents (naive, in-between, and ideal) are created in the simulator using the HBR model. The paper discusses the use of empirical evidence as referents, along with subject matter experts. A two-level three factor experiment was conducted using 36 participants. Several performance metrics were collected during the experiment, including route selected for evacuation, time to muster, time spent running, interaction with fire doors and watertight doors, interaction with hazards, and reporting to the muster station. Data collected during the experimental study have been used in this paper to demonstrate how the use of empirical evidence can facilitate HBR validation. High-level tasks performed during HBR validation are discussed in detail. Special emphasis is given on acceptability criteria testing to ensure that the HBR model performs adequately under different operating conditions. Results show that the proposed HBR model meets the acceptability criteria requirement for all types of agents. In general, the ideal agents exhibited safe behavior during offshore emergency egress, whereas the naive and in-between agents showed erroneous behavior at times. For example, during the simulation runs of a critical emergency scenario where the primary egress route was obstructed by a hazard, the ideal agents either waited and listened to the public address announcement and followed an alternative egress route (60% cases), or they initially chose their preferred route but re-routed immediately after encountering the hazard (40% cases). In all cases, the in-between agents started with their preferred route and re-routed after encountering the hazard, and the naive agents proceeded with their preferred route even when the route was compromised.