Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A fuzzy set approach to fault tree and reliability analysis
Fuzzy Sets and Systems
Ranking fuzzy numbers using ω-weighted valuations
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A fuzzy group decision making approach for bridge risk assessment
Computers and Industrial Engineering
The evidence framework applied to classification networks
Neural Computation
A fuzzy expert system for aviation risk assessment
Expert Systems with Applications: An International Journal
Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas
Engineering Applications of Artificial Intelligence
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There is an intrinsic risk associated with tunnel construction, particularly in urban areas where a number of third party persons and properties are involved. Due to the limited availability of data for accidents and the complexity associated with their causation, it is therefore necessary to combine available historical data and expert judgment to consider all relevant factors to undertake a realistic risk analysis. Thus, this paper presents a hybrid approach that can be used to undertake a probabilistic risk assessment of the risks associated with tunneling and its likelihood to damage to existing properties using the techniques of Bayesian Networks (BN) and a Relevance Vector Machine (RVM). A causal framework that integrates the techniques is also proposed to facilitate the development of the proposed model. The developed risk model is applied to a real tunnel construction project in Wuhan, China. The results derived from the project demonstrated the model's ability to accurately assess risks during tunneling, specifically the identification of accident scenarios and the quantification of the probability and severity of possible accidents. The potential of this risk model to be used as a decision-making support tool was also explored.