Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm

Khalid Elbaz; Shui-Long Shen; Annan Zhou; Zhen-Yu Yin; Hai-Min Lyu

doi:10.1016/j.dib.2020.106479

Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm

Data Brief. 2020 Nov 1:33:106479. doi: 10.1016/j.dib.2020.106479. eCollection 2020 Dec.

Authors

Khalid Elbaz¹, Shui-Long Shen¹, Annan Zhou², Zhen-Yu Yin³, Hai-Min Lyu⁴

Affiliations

¹ MOE Key Laboratory of Intelligence Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Guangdong 515063, China.
² Discipline of Civil and Infrastructure, School of Engineering, Royal Melbourne Institute of Technology, Victoria 3001, Australia.
³ Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
⁴ State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau, China.

Abstract

This data in brief presents the monitoring data measured during shield tunnelling of Guangzhou-Shenzhen intercity railway project. The monitoring data includes shield operational parameters, geological conditions, and geometry at the site. The presented data were arbitrarily split into two subsets including the training and testing datasets. The field observations are compared to the forecasting values of the disc cutter life assessed using a hybrid metaheuristic algorithm proposed for "Prediction of disc cutter life during shield tunnelling with artificial intelligent via incorporation of genetic algorithm into GMDH-type neural network" [1]. The presented data can provide a guidance for cutter exchange in shield tunnelling.

Keywords: Disc cutter; GMDH-type neural network; Genetic algorithm; Tunnel boring machine.