RESULTS DISCUSSION. Even though lifeboat accident rates seem reduced today and less attention was paid compared to the previous years, it could still be dangerous and life-costing during drills and accident evacuation. So it is of importance to analyze lifeboat accidents to help improving lifeboat safety in an effective way. In this report, case analysis was presented in the beginning and scenarios were identified. This provides the basic explanations on why lifeboat accidents happened. This objective was discussed based on the corresponding accident reports. Based on this point of breakthrough, the causal factors were defined. The defined causal factors formed the foundation of the subsequent research. Following, influence diagrams were constructed. This gave a systemic understanding of the causal factors by classifying them in different levels. It can be seen that there is similarity between the influence diagrams and the human factors analysis and classification system. A coding structure is then proposed specially for the lifeboat accidents from the technical and operational aspects. This helped the construction of BBN in the next chapter. In this coding structure, accesses were also provided to help further quantitative analysis. No such coding work had been done and this work could be seen as the first one. BBNs were built in the next step based on the hybrid causal factor method. Based on the classification system in the previous chapter, technical failures were analyzed through fault trees and operational failures were analyzed through BBN. With the data limitation, it can be seen that plentiful assumptions were made to simplify and assist the quantitative analysis. Still, it provided the data required for calculating the probability of a certain failure. Previous studies had looked at accident causes and consequences without giving probability estimations as well as the consideration of interacting events. This could also be regarded as the first application of BBN on lifeboat accidents. However, this research did not manage to demonstrate the overall picture of causal factors’ probabilistic importance because of the time constraint and poor data size. Up to this step, it is able to build an analytical model for lifeboat accidents. After applying BBN on lifeboat accidents, an extra analysis of human and organizational factor was performed due to the reason that lifeboat operating heavily relies on people. It helps a systemic consideration of human and organizational ...
RESULTS DISCUSSION summarizes all the processes for defining the probabilistic information associated with each basic node of the fault trees defined for assessing the impact on primary risk receptors (namely ground and ground-water) of interest in the real/virtual site for phase 1 of the project.
RESULTS DISCUSSION. To test our adaptive strategy, we conducted two experiments for the low and high- speed resource dynamism. The low speed of resource change denotes that the GRA’s reservation amount of resource Gmax for task i can change at most on 5% of the corresponding client’s maximum amount of resource Rmax per round, while the high speed means the change up to 20%. Those quantitative choices aim to show two substantially distinctive cases, where the GRA’s reservation value changes on smaller or larger amounts per negotiation round respectively. We also evaluate our strategy for a number of other resource deviations in the following chapters. The minimum resource change denotes 0% and the uniform distribution is considered to generate these changes. The resource dynamism is modelled by the probability of tendency,
i. e. the probability that the direction of the next resource change is the same as the direction of the previous one. In this way, the most tendentious resource dynamism is when this probability is equal to 1, i.
RESULTS DISCUSSION. 3.4.1 Isotachophoresis in sub-micrometer channels
