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

Data in Brief (Dec 2020)

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

Affiliations

Kun Zhang: Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Hai-Min Lyu: MOE Key Laboratory of Intelligent Manufacturing Technology, College of Engineering, Shantou University, Shantou, Guangdong 515063, China
Shui-Long Shen: MOE Key Laboratory of Intelligent Manufacturing Technology, College of Engineering, Shantou University, Shantou, Guangdong 515063, China; Civil and Infrastructure Discipline, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria 3001, Australia; Corresponding author at: College of Engineering, Shantou University, Shantou, Guangdong 515063, China.
Annan Zhou: Civil and Infrastructure Discipline, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria 3001, Australia
Zhen-Yu Yin: Department of Civil and Environmental Engineering, the Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

Journal volume & issue: Vol. 33
p. 106432

Abstract

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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).

Published in Data in Brief

ISSN: 2352-3409 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics; Science: Science (General)
Website: http://www.journals.elsevier.com/data-in-brief/

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