Tunnel Settlement Prediction by Transfer Learning

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of ICT Research and Applications Pub Date : 2019-09-30 DOI:10.5614/itbj.ict.res.appl.2019.13.2.3
Qicai Zhou, Hehong Shen, Jiong Zhao, Xiaolei Xiong
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引用次数: 3

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.
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基于迁移学习的隧道沉降预测
隧道沉降对财产安全和人身安全有重大影响。准确的隧道沉降预测可以迅速揭示可能解决的问题,以防止事故发生。然而,在大约两个月的时间里,隧道中的每个采集点每天只监测一次。本文提出了一种基于迁移学习的隧道沉降预测新方法。首先,通过深度学习构建源模型并对其进行训练,然后利用参数传递方法将源模型中获得的知识传递到数据集较小的目标模型中。在此基础上,可以降低目标模型的训练复杂度和训练时间。以上海地铁13号线金沙江路隧道为例,验证了该方法的有效性。人工神经网络和支持向量机也进行了比较测试。结果表明,迁移学习方法能提供最准确的隧道沉降预测。
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来源期刊
Journal of ICT Research and Applications
Journal of ICT Research and Applications COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
1.60
自引率
0.00%
发文量
13
审稿时长
24 weeks
期刊介绍: Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management. Authors are invited to submit articles that have not been published previously and are not under consideration elsewhere.
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