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Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements
The dataset presented in this article pertains to records of shield tunneling-induced ground settlements in Guangzhou Metro Line No. 9. Field monitoring results obtained from both the two tunnel lines are put on display. In total, 17 principal variables affecting ground settlements are tabulated, wh...
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/PMC7649604/ https://www.ncbi.nlm.nih.gov/pubmed/33204775 http://dx.doi.org/10.1016/j.dib.2020.106432 |
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author | Zhang, Kun Lyu, Hai-Min Shen, Shui-Long Zhou, Annan Yin, Zhen-Yu |
author_facet | Zhang, Kun Lyu, Hai-Min Shen, Shui-Long Zhou, Annan Yin, Zhen-Yu |
author_sort | Zhang, Kun |
collection | PubMed |
description | The dataset presented in this article pertains to records of shield tunneling-induced ground settlements in Guangzhou Metro Line No. 9. Field monitoring results obtained from both the two tunnel lines are put on display. In total, 17 principal variables affecting ground settlements are tabulated, which can be divided into two categories: geological condition parameters and shield operation parameters. Shield operation parameters are specifically provided in time series. Another value of the dataset is the consideration of karst encountered in the shield tunnel area including the karst cave height, the distance between karst cave and tunnel invert, and the karst cave treatment scheme. The dataset can be used to enrich the database of settlement caused by shield tunneling as well as to train artificial intelligence-based ground settlement prediction models. The dataset presented herein were used for the article titled “Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements” (Zhang et al., 2020). |
format | Online Article Text |
id | pubmed-7649604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-76496042020-11-16 Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements Zhang, Kun Lyu, Hai-Min Shen, Shui-Long Zhou, Annan Yin, Zhen-Yu Data Brief Data Article The dataset presented in this article pertains to records of shield tunneling-induced ground settlements in Guangzhou Metro Line No. 9. Field monitoring results obtained from both the two tunnel lines are put on display. In total, 17 principal variables affecting ground settlements are tabulated, which can be divided into two categories: geological condition parameters and shield operation parameters. Shield operation parameters are specifically provided in time series. Another value of the dataset is the consideration of karst encountered in the shield tunnel area including the karst cave height, the distance between karst cave and tunnel invert, and the karst cave treatment scheme. The dataset can be used to enrich the database of settlement caused by shield tunneling as well as to train artificial intelligence-based ground settlement prediction models. The dataset presented herein were used for the article titled “Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements” (Zhang et al., 2020). Elsevier 2020-10-21 /pmc/articles/PMC7649604/ /pubmed/33204775 http://dx.doi.org/10.1016/j.dib.2020.106432 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 Zhang, Kun Lyu, Hai-Min Shen, Shui-Long Zhou, Annan Yin, Zhen-Yu Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements |
title | Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements |
title_full | Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements |
title_fullStr | Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements |
title_full_unstemmed | Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements |
title_short | Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements |
title_sort | data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649604/ https://www.ncbi.nlm.nih.gov/pubmed/33204775 http://dx.doi.org/10.1016/j.dib.2020.106432 |
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