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http://dx.doi.org/10.9711/KTAJ.2019.21.5.641

Prediction of replacement period of shield TBM disc cutter using SVM  

La, You-Sung (Dept. of Civil and Environmental Engineering, Dongguk University)
Kim, Myung-In (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.21, no.5, 2019 , pp. 641-656 More about this Journal
Abstract
In this study, a machine learning method was proposed to use in predicting optimal replacement period of shield TBM (Tunnel Boring Machine) disc cutter. To do this, a large dataset of ground condition, disc cutter replacement records and TBM excavation-related data, collected from a shield TBM tunnel site in Korea, was built and they were used to construct a disc cutter replacement period prediction model using a machine learning algorithm, SVM (Support Vector Machine) and to assess the performance of the model. The results showed that the performance of RBF (Radial Basis Function) SVM is the best among a total of three SVM classification functions (80% accuracy and 10% error rate on average). When compared between ground types, the more disc cutter replacement data existed, the better prediction results were obtained. From this results, it is expected that machine learning methods become very popularly used in practice in near future as more data is accumulated and the machine learning models continue to be fine-tuned.
Keywords
Shield TBM; Disc cutter; Machine learning; SVM;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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