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Depth learning model for prediction of shield tunneling speed in composite strata considering historical effects
  • 2023/04/27 10:34
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English name
Depth learning model for prediction of shield tunneling speed in composite strata considering historical effects
CSTR
CSTR:11738.11.NCDC.NIEER.DB2777.2023
DOI
10.12072/ncdc.nieer.db2777.2023
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Category
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contributors
ZHOU Biao
Datasets description 

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  This data set includes physical property parameters of materials used in the test, including weight, Poisson's ratio, cohesion, friction angle, deformation modulus and other parameters. The physical property parameters of the substances used in the test included in this data set are measured by the test equipment. The folder in the dataset contains an excel table. The tabular data includes physical property parameters of sand, conglomerate and mudstone, gravity, Poisson's ratio, cohesion, friction angle, deformation modulus, etc. The data volume is 10KB.

Base information
collect time 2020/02/01  -  2020/02/28
collect place Shanghai
data size 17.2 KiB
Data thumbnail
Data source description 

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  It is obtained through observation and monitoring.

Data processing method 

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  It is obtained through observation and monitoring.

Data quality description 

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  The data quality is good.

Project information
# number name type
1 2018YFC0809600 National key R & D plan
Citations and annotations
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For the achievements published in English, please refer to the following specifications： The dataset is provided by National Cryosphere Desert Data Center. (http://www.ncdc.ac.cn).

Data reference
  1. ZHOU Biao. Depth learning model for prediction of shield tunneling speed in composite strata considering historical effects. National Cryosphere Desert Data Center(http://www.ncdc.ac.cn), 2023. https://cstr.cn/CSTR:11738.11.NCDC.NIEER.DB2777.2023.
  2. ZHOU Biao. Depth learning model for prediction of shield tunneling speed in composite strata considering historical effects. National Cryosphere Desert Data Center(http://www.ncdc.ac.cn), 2023. https://www.doi.org/10.12072/ncdc.nieer.db2777.2023.
license agreement
# title file size
1 Deep learning model for shield tunneling advance rate prediction in mixed ground condition considering past operations.xlsx 0 Bytes
2 数据集说明文件.docx 17.2 KiB
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Contact information
contributors:
ZHOU Biao
author:
ZHOU Biao
manager:
ZHOU Biao
Platform Service information
contacts:
Li Hongxing
phone:
0931-4967592
mailbox:
ncdc@lzb.ac.cn

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