@article{Nizar_Miwa_Uchida_2024,
	author = {Nizar, Adi Mas and Miwa, Takashi and Uchida, Makoto},
	title = {Simulator-based Human Reliability Analysis using Bayesian Network: A Case Study on Situation Awareness in Engine Resources Management},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {18},
	number = {3},
	pages = {593-599},
	year = {2024},
	url = {./Article_Simulator-based_Human_Reliability_Nizar,71,1433.html},
	abstract = {Situational awareness (SA) is regarded as one of the important non-technical skills in constructing the seafarers’ ability in daily decision-making and performing tasks, especially in Engine Resources Management (ERM). In maritime accidents that are mainly human error, insufficient SA is the specific factor that contributes to most of incidents. To quantitatively assess SA reliability, a Bayesian network of seafarers’ performance in attaining SA in engine supervisory control is constructed. The adaptation of simulator data helped as combination along using subject matter expert input, which is a common practice in constructing human reliability analysis. Additionally, the simulator data can serve as the updating function when new data is observed. The result shows that the model can provide promising results as compared with expert expectation. Such kind of model can support the evaluation of the engine operation onboard, and mitigation can be provided to reduce the probability of human error. },
	doi = {10.12716/1001.18.03.13},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {Situation Awareness (SA), Bayesian Networks, Human Reliability, Human Reliability Assessment (HRA), Maritime Operation, Engine Room Resource Management, Human Error Probability (HEP), Cognitive Reliability and Error Analysis (CREAM)}
}