Abstract
The availability and reliability of tunnel infrastructure lies on the health of tunnel structure, therefore, scientific maintenance is needed to enhance the tunnel’s structure reliability and reduce the inspection and maintenance cost through optimized maintenance schedule. This paper aims to develop a data-driven framework for the operation and maintenance of tunnels, which consists of eight stages: maintenance data profile, tunnel failure mode and effect analysis (FMEA), data processing, feature engineering, anomaly detection, failure inspection, failure prediction and maintenance schedule. These Stages are interpreted into fifteen steps in a sub-system with data fusion techniques used for the whole process of tunnel components maintenance. The proposed framework for tunnel’s maintenance ensures the safety and reliability of tunnel’s operation, improves the efficiency of failure detection and reduces the maintenance cost as well.
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Sun, Y., Hu, M., Zhou, W., Xu, W. (2019). A Data-Driven Framework for Tunnel Infrastructure Maintenance. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018. ATCI 2018. Advances in Intelligent Systems and Computing, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-319-98776-7_54
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DOI: https://doi.org/10.1007/978-3-319-98776-7_54
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