Feature Visualization Using a Deep Learning Technique with Attention-Based Mechanism for Pipe Jacking through “Soft Rocks”-Reference-Cited by-同舟云学术

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Feature Visualization Using a Deep Learning Technique with Attention-Based Mechanism for Pipe Jacking through “Soft Rocks”

Published:2023-07-20 Issue: Volume: Page:
ISSN:
Container-title:Geo-Risk 2023
language:
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Author:

Yen Yeo Lit¹¹¹¹²,Gee Kueh Pei¹¹¹¹²,Siung Choo Chung¹¹¹¹²,Han Lee Sue¹¹¹¹²,Zhang Dongming¹¹¹¹²

Affiliation:

1. Faculty of Engineering, Computing, and Science, Swinburne Univ. of Technology, Sarawak, Malaysia.

2. Key Laboratory of Geotechnical and Underground Engineering of Minister of Education and Dept. of Geotechnical Engineering, Tongji Univ., Shanghai, China.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784484982.005

Reference30 articles.

1. Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital;Chen C. W.;Information,2020

2. Chen H. Xiao C. Yao Z. Jiang H. Zhang T. and Guan Y. (2019). Prediction of TBM Tunneling Parameters through an LSTM Neural Network. IEEE International Conference on Robotics and Biomimetics (ROBIO) pp. 702–707 doi: 10.1109/ROBIO49542.2019.8961809.

3. Deep learning based classification of rock structure of tunnel face

4. Identifying characteristics of pipejacking parameters to assess geological conditions using optimisation algorithm-based support vector machines;Cheng W.-C.;Tunnelling and Underground Space Technology,2020

5. Investigation into factors affecting jacking force: a case study

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