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Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm

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 i...

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Autores principales: Elbaz, Khalid, Shen, Shui-Long, Zhou, Annan, Yin, Zhen-Yu, Lyu, Hai-Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672276/
https://www.ncbi.nlm.nih.gov/pubmed/33241094
http://dx.doi.org/10.1016/j.dib.2020.106479
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author Elbaz, Khalid
Shen, Shui-Long
Zhou, Annan
Yin, Zhen-Yu
Lyu, Hai-Min
author_facet Elbaz, Khalid
Shen, Shui-Long
Zhou, Annan
Yin, Zhen-Yu
Lyu, Hai-Min
author_sort Elbaz, Khalid
collection PubMed
description 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.
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spelling pubmed-76722762020-11-24 Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm Elbaz, Khalid Shen, Shui-Long Zhou, Annan Yin, Zhen-Yu Lyu, Hai-Min Data Brief Data Article 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. Elsevier 2020-11-01 /pmc/articles/PMC7672276/ /pubmed/33241094 http://dx.doi.org/10.1016/j.dib.2020.106479 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Elbaz, Khalid
Shen, Shui-Long
Zhou, Annan
Yin, Zhen-Yu
Lyu, Hai-Min
Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm
title Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm
title_full Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm
title_fullStr Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm
title_full_unstemmed Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm
title_short Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm
title_sort data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672276/
https://www.ncbi.nlm.nih.gov/pubmed/33241094
http://dx.doi.org/10.1016/j.dib.2020.106479
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