Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements

Kun Zhang, Hai Min Lyu, Shui Long Shen, Annan Zhou, Zhen Yu Yin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

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).

Original languageEnglish
Article number106432
JournalData in Brief
Volume33
DOIs
Publication statusPublished - Dec 2020

Keywords

  • Artificial intelligence-based analysis
  • Dataset
  • Settlement prediction
  • Tunneling

ASJC Scopus subject areas

  • General

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