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http://dx.doi.org/10.9711/KTAJ.2020.22.5.575

Performance comparison of machine learning classification methods for decision of disc cutter replacement of shield TBM  

Kim, Yunhee (Dept. of Civil and Environmental Engineering, Dongguk University)
Hong, Jiyeon (Dept. of Civil and Environmental Engineering, Dongguk University)
Kim, Bumjoo (Dept. of Civil and Environmental Engineering, Dongguk University)
Publication Information
Journal of Korean Tunnelling and Underground Space Association / v.22, no.5, 2020 , pp. 575-589 More about this Journal
Abstract
In recent years, Shield TBM construction has been continuously increasing in domestic tunnels. The main excavation tool in the shield TBM construction is a disc cutter which naturally wears during the excavation process and significantly degrades the excavation efficiency. Therefore, it is important to know the appropriate time of the disc cutter replacement. In this study, it is proposed a predictive model that can determine yes/no of disc cutter replacement using machine learning algorithm. To do this, the shield TBM machine data which is highly correlated to the disc cutter wears and the disc cutter replacement from the shield TBM field which is already constructed are used as the input data in the model. Also, the algorithms used in the study were the support vector machine, k-nearest neighbor algorithm, and decision tree algorithm are all classification methods used in machine learning. In order to construct an optimal predictive model and to evaluate the performance of the model, the classification performance evaluation index was compared and analyzed.
Keywords
Shield TBM; Disc cutter; Machine learning; Classification method;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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