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.

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.

In this chapter, the concept of functional signatures is presented as a way to understand complex industrial operations. Visualization of functional signatures can be used to improve tractability of complex operations, which can be valuable for safety analysis. Two techniques for visualizing functional signatures are presented: (1) cyclic functional signatures and (2) linear functional signatures. Both techniques are seen as valuable and selection of technique can be left to user preference. The two visualization techniques are demonstrated through an application of an ice management operation for an offshore petroleum installation performed in a simulated ship environment.