RESEARCH PAPER
Risk Assessment of Highway Engineering Construction Safety Reliability Based on Two-Dimensional Cloud Model-Bayesian Network
1
North China University of Water Resources and Electric Power, China
2
Central China Regional Headquarters of Powerchina Road-Bridge CO., LTD., China
3
Western Regional Headquarters of Powerchina Road-Bridge Group CO., LTD., China
4
Southeast Regional Headquarters of Powerchina Road-Bridge Group CO., LTD., China
Submission date: 2025-04-09
Final revision date: 2025-06-17
Acceptance date: 2025-08-03
Online publication date: 2025-09-07
Publication date: 2025-09-07
Eksploatacja i Niezawodność – Maintenance and Reliability 2026;28(1):208923
HIGHLIGHTS
- Proposing a risk assessment method combining the 2D cloud model and Bayesian network.
- Constructing a fault tree model for risk and 2D comprehensive cloud risk model.
- Bi-directional inference is performed by the Bayesian network.
KEYWORDS
risk assessmentBayesian networktwo-dimensional cloud modelhighway engineeringConstruction safety reliability
TOPICS
ABSTRACT
In response to the difficulty of quantifying the fuzziness and randomness of safety risks in highway engineering construction using traditional methods, which leads to insufficient reliability, this study proposes a risk assessment method for highway engineering construction safety reliability based on the two-dimensional cloud model and Bayesian network. An assessment system containing 4 categories and 18 indicators was constructed using the Delphi method, a fault tree model was established and transformed into a Bayesian network. Based on the risk matrix method, a two-dimensional cloud model was constructed and the comprehensive cloud of bottom events was calculated. By assessing the occurrence probability of the bottom events and the severity of their consequences, the a priori probabilities of the risk indicators were calculated. Bidirectional inference in the Bayesian network was then used to determine the probability of the top event and identify key factors.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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