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Title: Feature-based evidential reasoning for probabilistic risk analysis and prediction
Authors: Wang, Ying
Zhang, Limao
Keywords: Engineering::Civil engineering
Issue Date: 2021
Source: Wang, Y. & Zhang, L. (2021). Feature-based evidential reasoning for probabilistic risk analysis and prediction. Engineering Applications of Artificial Intelligence, 102, 104237-.
Project: 04MNP000279C120 
Journal: Engineering Applications of Artificial Intelligence
Abstract: Risk analysis plays an important role in quality control in engineering projects for the consideration of time, cost, safety, and the environment. This study proposes a feature-based evidential reasoning approach for probabilistic risk analysis and prediction, incorporating the learning process of belief degrees and estimation of the judgment quality. Firstly, classifiers are trained to estimate the probabilistic risk from sub-groups of factors. Secondly, the judgment from each classifier is evaluated according to the classifier's performance which is characterized by the importance weight and reliability. Finally, the judgments from classifiers are fused via evidential reasoning to give the overall probabilistic risk classification result. The proposed approach displays superior performance on the dataset from Wuhan Metro with a 16% increase in precision, a 6% increase in recall, and an 8% increase in F1-score, compared to the direct model without information fusion. The fused model achieves a classification accuracy of 0.86 on the testing samples, which is better than the direct model. Besides, the model shows good error tolerance for wrongly classified results from classifiers without information fusion. The model has an acceptable performance even when the dataset is challenging to conduct classification tasks due to high overlapping areas in the attribute space.
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2021.104237
Schools: School of Civil and Environmental Engineering 
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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