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1 – 3 of 3Grace W.Y. Wang, Zhisen Yang, Di Zhang, Anqiang Huang and Zaili Yang
This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.
Abstract
Purpose
This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.
Design/methodology/approach
This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.
Findings
The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.
Research limitations/implications
The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.
Practical implications
The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.
Originality/value
Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.
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Keywords
Bingqian Chen, Anqiang Wang, Qing Guo, Jiayin Dai and Yongshou Liu
This paper aims to solve the problem that pipes conveying fluid are faced with severe reliability failures under the complicated working environment.
Abstract
Purpose
This paper aims to solve the problem that pipes conveying fluid are faced with severe reliability failures under the complicated working environment.
Design/methodology/approach
This paper proposes a dynamic reliability and variance-based global sensitivity analysis (GSA) strategy with non-probabilistic convex model for pipes conveying fluid based on the first passage principle failure mechanism. To illustrate the influence of input uncertainty on output uncertainty of non-probability, the main index and the total index of variance-based GSA analysis are used. Furthermore, considering the efficiency of traditional simulation method, an active learning Kriging surrogate model is introduced to estimate the dynamic reliability and GSA indices of the structure system under random vibration.
Findings
The variance-based GSA analysis can measure the effect of input variables of convex model on the dynamic reliability, which provides useful reference and guidance for the design and optimization of pipes conveying fluid. For designers, the rankings and values of main and total indices have essential guiding role in engineering practice.
Originality/value
The effectiveness of the proposed method to calculate the dynamic reliability and sensitivity of pipes conveying fluid while ensuring the calculation accuracy and efficiency in the meantime.
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