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Tunnel Settlement Prediction by Transfer Learning

Journal of ICT Research and Applications (JICTRA) · Vol. 13 No. 2 (2019) · pp. 118–132

DOI
10.5614/itbj.ict.res.appl.2019.13.2.3
ISSN
2338-5499, 2337-5787
Published
2019-09-30
Publisher
DRI - ITB
Language
eng

Abstract

Tunnel settlement has a significant impact on property security and personal safety. Accurate tunnel-settlement predictions can quickly reveal problems that may be addressed to prevent accidents. However, each acquisition point in the tunnel is only monitored once daily for around two months. This paper presents a new method for predicting tunnel settlement via transfer learning. First, a source model is constructed and trained by deep learning, then parameter transfer is used to transfer the knowledge gained from the source model to the target model, which has a small dataset. Based on this, the training complexity and training time of the target model can be reduced. The proposed method was tested to predict tunnel settlement in the tunnel of Shanghai metro line 13 at Jinshajiang Road and proven to be effective. Artificial neural network and support vector machines were also tested for comparison. The results showed that the transfer-learning method provided the most accurate tunnel-settlement prediction.

Keywords

Collaboration network of the authors

4 authors · 6 co-authorship links · 3 across different organisations

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  • Tongji University
  • Tongji Zhejiang College
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Tunnel Settlement Prediction by Transfer Learning</dc:title>
	<dc:creator>Zhou, Qicai</dc:creator>
	<dc:creator>Shen, Hehong</dc:creator>
	<dc:creator>Zhao, Jiong</dc:creator>
	<dc:creator>Xiong, Xiaolei</dc:creator>
	<dc:subject xml:lang="en-US">deep neural network</dc:subject>
	<dc:subject xml:lang="en-US">gated recurrent unit</dc:subject>
	<dc:subject xml:lang="en-US">settlement prediction</dc:subject>
	<dc:subject xml:lang="en-US">tunneling</dc:subject>
	<dc:subject xml:lang="en-US">transfer learning</dc:subject>
	<dc:description xml:lang="en-US">Tunnel settlement has a significant impact on property security and personal safety. Accurate tunnel-settlement predictions can quickly reveal problems that may be addressed to prevent accidents. However, each acquisition point in the tunnel is only monitored once daily for around two months. This paper presents a new method for predicting tunnel settlement via transfer learning. First, a source model is constructed and trained by deep learning, then parameter transfer is used to transfer the knowledge gained from the source model to the target model, which has a small dataset. Based on this, the training complexity and training time of the target model can be reduced. The proposed method was tested to predict tunnel settlement in the tunnel of Shanghai metro line 13 at Jinshajiang Road and proven to be effective. Artificial neural network and support vector machines were also tested for comparison. The results showed that the transfer-learning method provided the most accurate tunnel-settlement prediction.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2019-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/10375</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2019.13.2.3</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 13 No. 2 (2019); 118-132</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2019.13.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/10375/4336</dc:relation>
</oai_dc:dc>