Prediction of Tunnelling Parameters for Underwater Shield Tunnels, Based on the GA-BPNN Method

Yu Liang (), Kai Jiang, Shijun Gao and Yihao Yin
Additional contact information
Yu Liang: School of Civil Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518000, China
Kai Jiang: School of Civil Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518000, China
Shijun Gao: China Railway 14th Bureau Group Mega Shield Construction Engineering Co., Ltd., Nanjing 210000, China
Yihao Yin: China Railway 14th Bureau Group Mega Shield Construction Engineering Co., Ltd., Nanjing 210000, China

Sustainability, 2022, vol. 14, issue 20, 1-15

Abstract: Reasonable tunnelling parameters for underwater shield tunnels play an important role in maintaining driving efficiency and safety. In this paper, a neural network method was developed to predict tunnelling parameters. Soil properties and geometric parameters were taken as inputs for the neural network, which output the tunnelling parameters, such as advancing thrust, rotation, penetration, torque of the cutter head, and support pressure. In order to improve the stability of the neural network, a genetic algorithm (GA) with a global searching ability was used to optimize the initial weight of the neural network (GA-BPNN). The accuracy of the algorithm, based on GA-BPNN, was studied through an underwater shield tunnel project. The results showed that the integration of GA into the neural network significantly improves the prediction ability for shield tunnelling parameters, especially for adjustable parameters. Later, the developed GA-BPNN model was further utilized to predict and set the range of shield tunnelling parameters in fine sand stratum of high risk. Through a comparative analysis of tunnelling parameters, the reasons leading to ground instability have been found out, and the effectiveness of ground pre-reinforcement has been verified.

Keywords: genetic algorithm; back propagation neural networks; shield tunneling; operational parameters prediction; ground reinforcement evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/20/13420/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/20/13420/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:20:p:13420-:d:945922

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().