Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91052
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Title: Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm
Authors: Elbaz, K
Shen, SL
Zhou, AN
Yin, ZY 
Lyu, HM
Issue Date: Dec-2020
Source: Data in brief, Dec. 2020, v. 33, 106479
Abstract: This data in brief presents the monitoring data measured during shield tunnelling of Guangzhou-Shenzhen intercity railway project. The monitoring data includes shield operational parameters, geological conditions, and geometry at the site. The presented data were arbitrarily split into two subsets including the training and testing datasets. The field observations are compared to the forecasting values of the disc cutter life assessed using a hybrid metaheuristic algorithm proposed for "Prediction of disc cutter life during shield tunnelling with artificial intelligent via incorporation of genetic algorithm into GMDH-type neural network"[1]. The presented data can provide a guidance for cutter exchange in shield tunnelling.
Keywords: Disc cutter
GMDH-type neural network
Tunnel boring machine
Genetic algorithm
Publisher: Elsevier
Journal: Data in brief 
ISSN: 2352-3409
DOI: 10.1016/j.dib.2020.106479
Rights: © 2020 The Author(s). Published by Elsevier Inc.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
The following publication Khalid Elbaz, Shui-Long Shen, Annan Zhou, Zhen-Yu Yin, Hai-Min Lyu, Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm, Data in Brief, Volume 33, 2020, 106479, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2020.106479 is available at (https://www.sciencedirect.com/science/article/pii/S2352340920313615)
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