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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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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 |
_version_ | 1783611098409205760 |
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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. |
format | Online Article Text |
id | pubmed-7672276 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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