An Evaluation Method for Multisource Information Fusion in Tunneling Water Inrush Disasters
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 10
Abstract
During tunnel construction, a substantial volume of data is generated. However, presently, there is a challenge in effectively integrating those data to conduct a precise risk assessment of water inrush. This study develops a novel multisource information fusion method that merges a probabilistic support vector machine, cloud model, evidential reasoning (ER) rule, and Monte Carlo (MC) simulation method to support water-inrush risk assessment under uncertainty. Different models train a variety of information sources to analyze the water-inrush risk value. The evaluation of each model’s judgment is based on its performance, which is determined by its reliability and the significance of its weights. Finally, these multiple assessment results are fused at the decision level to achieve an overall water-inrush risk evaluation using the ER rule. The MC simulation method was used to model the uncertainty and randomness underlying the limited number of observations. The Heshan tunnel case in China is used to demonstrate the feasibility and effectiveness of the developed method. The outcomes suggest that the multisource information fusion technique proposed is capable of (1) demonstrating a good ability to handle high conflict information; (2) exhibiting an outstanding assessment performance (90% accuracy) compared to that of single-source assessment models (lower than 70% accuracy); and (3) performing strongly with bias, as it can achieve acceptable assessment accuracy under 5% bias. Therefore, the newly proposed method for fusing multiple sources of information can serve as a practical reference for water-inrush risk assessment and management.
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Data Availability Statement
The datasets produced and/or analyzed during the current study are not publicly accessible but can be obtained from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Science Foundation for Outstanding Youth of Hunan Province (No. 2021JJ10063), the Hunan Provincial Department of Transportation Science and Technology Progress and Innovation Project (No. 202115), and the Science and Technology Research and Development Project of China Railway Guangzhou Bureau Group Co. LTD (No. 2021K094-Z). All financial support is greatly appreciated.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 150 • Issue 10 • October 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Nov 22, 2023
Accepted: May 6, 2024
Published online: Jul 30, 2024
Published in print: Oct 1, 2024
Discussion open until: Dec 30, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction methods
- Continuum mechanics
- Disaster risk management
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Geotechnical engineering
- Model accuracy
- Models (by type)
- Motion (dynamics)
- Risk management
- Simulation models
- Solid mechanics
- Tunnels
- Uncertainty principles
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