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

doi:10.1016/j.dib.2020.106432

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

Data Brief. 2020 Oct 21:33:106432. doi: 10.1016/j.dib.2020.106432. eCollection 2020 Dec.

Authors

Kun Zhang¹, Hai-Min Lyu², Shui-Long Shen^{2

3}, Annan Zhou³, Zhen-Yu Yin⁴

Affiliations

¹ Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
² MOE Key Laboratory of Intelligent Manufacturing Technology, College of Engineering, Shantou University, Shantou, Guangdong 515063, China.
³ Civil and Infrastructure Discipline, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria 3001, Australia.
⁴ Department of Civil and Environmental Engineering, the Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.

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).

Keywords: Artificial intelligence-based analysis; Dataset; Settlement prediction; Tunneling.