Safety prediction of shield tunnel construction using deep belief network and whale optimization algorithm
Due to ground loss and shallowly buried tunnels, there are serious safety problems in shield tunnel construction. To comprehensively describe the safety of shield tunnel construction, two safety control indices (ground settlement and segment floating) were applied to represent the two main aspects of construction safety (surrounding environment and tunnel structure). Here, a deep-learning method involving a deep belief network (DBN) optimized by a whale optimization algorithm (WOA) called WO-DBN is proposed to predict ground settlement and segment floating. Based on 370,404 engineering data of shield tunnel construction for Guangzhou subway Line 18 in China, the mean absolute errors of the WO-DBN method for the two indices were only 2.255 and 0.954, respectively. The results show that the WO-DBN achieves a high prediction accuracy, and that it can be effectively used for safety prediction of real shield tunnel construction. The improvement of the WO-DBN, such as through using the newly developed activation functions, should be a future research direction.
- Record URL:
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09265805
-
Supplemental Notes:
- © 2022 Elsevier B.V. All rights reserved. Abstract reprinted with permission of Elsevier.
-
Authors:
- Ge, Shuangshuang
- Gao, Wei
- Cui, Shuang
- Chen, Xin
- Wang, Sen
- Publication Date: 2022-10
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 104488
-
Serial:
- Automation in Construction
- Volume: 142
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0926-5805
- Serial URL: http://www.sciencedirect.com/science/journal/09265805
Subject/Index Terms
- TRT Terms: Construction safety; Mathematical prediction; Optimization; Shielding (Tunneling); Subways
- Geographic Terms: Guangzhou (China)
- Subject Areas: Bridges and other structures; Construction; Railroads;
Filing Info
- Accession Number: 01855251
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 22 2022 4:14PM