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Continuous Automatic Detection of Cracks in Tunnels Using Machine Learning and Artificial Intelligence Techniques for Safety Monitoring
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2020-07-01
[PDF-3.01 MB]
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Abstract:Structural monitoring of the inner walls of a tunnel, both during construction and use, is important for safety, performance, liability, and cost. The goal of this project is to develop a fully automated system to perform continuous monitoring of tunnels during and after construction. The following intertwined aspects to this problem require further study: investigation and development of (a) a suitable technique for relevant data acquisition (this usually involves imaging the interior walls of the tunnel, but the exact method for doing this that is reliable, cost efficient, and easily automated is an open problem); (b) tools and techniques to acquire the data in an efficient and automated manner and communicate the information to proper processing center; (c) suitable techniques for post processing of acquired data to generate an intermediate observation space on which detection algorithms may be applied; (d) suitable algorithms and techniques for pattern detection and classification; (5) the software suite needed for data processing from post processed data to implement selected pattern detection and classification algorithms/techniques, assess their performance and develop recommendations.
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