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Cited by 70Tunneling and Rock MechanicsGeotechnical Engineering and AnalysisCivil and Geotechnical Engineering Research

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Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

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Wang, Xiao, Lu, Hongfang, Wei, Xinjiang, Wei, Gang, Behbahani, Seyed Saleh, Iseley, Tom·2020·IEEE Access
DOI 10.1109/access.2020.3004995
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Abstract

Due to the lack of living space and the increase in population, there has been a construction boom in the underground space to improve the quality of human life. Tunnel engineering plays a vital role in the development of underground space. In addition to traditional methods, some intelligent methods such as artificial neural networks (ANNs) have been applied to various problems in the tunnel domain in recent years. This paper systematically reviews the application of ANNs from different aspects of tunnel engineering. It reveals that the backpropagation algorithm (BPA) and Levenberg-Marquardt algorithm (LMA) are the most widely used. Due to the limitations of some original models, some scholars use optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the original ANNs to obtain better prediction results. A comparison between the ANN-based methods and methods like statistical methods is conducted. Finally, the following conclusions can be drawn: (1) The recommended ratio of the training set and test set is 3:1; (2) The advantage of optimized ANNs is not apparent when the optimization algorithm varies. Additionally, the performance of ANNs is always better than that of statistical methods.

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Metadata

Authors
Wang, X., Lu, H., Wei, X., Wei, G., Behbahani, S. S., & Iseley, T.
Affiliations
  • Zhejiang University (CN)
  • Louisiana Tech University (US)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020
Volume
8
Pages
119527-119543
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2020.3004995
OpenAlex
W3037098832
Cited by
70
References
0
Citations by year
2026: 5 · 2025: 16 · 2024: 17 · 2023: 13 · 2022: 10 · 2021: 8 · 2020: 1
FWCI
1.10
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
China Scholarship Council (201806320087)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Tunneling and Rock Mechanics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Geotechnical Engineering and Analysis
  • Physical Sciences › Engineering › General Engineering › Civil and Geotechnical Engineering Research
Keywords
artificial neural networks, tunnel engineering, backpropagation, Levenberg-Marquardt algorithm, particle swarm optimization, genetic algorithm, neural network optimization, underground space development

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CoChat. (2026). Citation check: Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review. https://app.cochat.ai/papers/10.1109/access.2020.3004995
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Wang, X., Lu, H., Wei, X., Wei, G., Behbahani, S. S., & Iseley, T. (2020). Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review. IEEE Access. https://doi.org/10.1109/access.2020.3004995