TY - JOUR
T1 - Use of fuzzy fault tree analysis and noisy-or gate bayesian network for navigational risk assessment in qingzhou port
AU - Zhao, C.
AU - Wu, B.
AU - Yip, T. L.
AU - Lv, J.
N1 - Funding Information:
The research presented in this paper was sponsored by a grant from National Key Technologies Research & Development Program (grant number 2019YFB1600600; 2019YFB1600603), National Science Foundation of China (grant number 51809206), Shenzhen Science and Technology Innovation Committee (Grant No. CJGJZD20200617102602006).
Publisher Copyright:
© 2021, Faculty of Navigation, Gdynia Maritime University. All rights reserved.
PY - 2021/12
Y1 - 2021/12
N2 - Collisions and groundings account for more than 80% among all types of maritime accidents, and risk assessment is an essential step in the formal safety assessment. This paper proposes a method based on fuzzy fault tree analysis and Noisy-OR gate Bayesian network for navigational risk assessment. First, a fault tree model was established with historical data, and the probability of basic events is calculated using fuzzy sets. Then, the Noisy-OR gate is utilized to determine the conditional probability of related nodes and obtain the probability distribution of the consequences in the Bayesian network. Finally, this proposed method is applied to Qinzhou Port. From sensitivity analysis, several predominant influencing factors are identified, including navigational area, ship type and time of the day. The results indicate that the consequence is sensitive to the position where the accidents occurred. Consequently, this paper provides a practical and reasonable method for risk assessment for navigational accidents.
AB - Collisions and groundings account for more than 80% among all types of maritime accidents, and risk assessment is an essential step in the formal safety assessment. This paper proposes a method based on fuzzy fault tree analysis and Noisy-OR gate Bayesian network for navigational risk assessment. First, a fault tree model was established with historical data, and the probability of basic events is calculated using fuzzy sets. Then, the Noisy-OR gate is utilized to determine the conditional probability of related nodes and obtain the probability distribution of the consequences in the Bayesian network. Finally, this proposed method is applied to Qinzhou Port. From sensitivity analysis, several predominant influencing factors are identified, including navigational area, ship type and time of the day. The results indicate that the consequence is sensitive to the position where the accidents occurred. Consequently, this paper provides a practical and reasonable method for risk assessment for navigational accidents.
UR - http://www.scopus.com/inward/record.url?scp=85121651288&partnerID=8YFLogxK
U2 - 10.12716/1001.15.04.07
DO - 10.12716/1001.15.04.07
M3 - Journal article
AN - SCOPUS:85121651288
SN - 2083-6473
VL - 15
SP - 765
EP - 771
JO - TransNav
JF - TransNav
IS - 4
ER -