Deep Convolutional Neural Network-Based Method for Strength Parameter Prediction of Jointed Rock Mass Using Drilling Logging Data-Reference-Cited by-同舟云学术

Deep Convolutional Neural Network-Based Method for Strength Parameter Prediction of Jointed Rock Mass Using Drilling Logging Data

Published:2021-07 Issue:7 Volume:21 Page:
ISSN:1532-3641
Container-title:International Journal of Geomechanics
language:en
Short-container-title:Int. J. Geomech.

Author:

He Mingming¹,Zhang Zhiqiang²,Li Ning³

Affiliation:

1. Lecturer, State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi’an Univ. of Technology, Xi’an 710048, China (corresponding author).

2. Associate Professor, Institute of Geotechnical Engineering, Xi’an Univ. of Technology, Xi’an 710048, China.

3. Professor, Institute of Geotechnical Engineering, Xi’an Univ. of Technology, Xi’an 710048, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Soil Science

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29GM.1943-5622.0002074

Reference80 articles.

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3. A new rock mass quality rating system: Rock mass quality rating (RMQR) and its application to the estimation of geomechanical characteristics of rock masses;Aydan Ö;Rock Mech. Rock Eng.,2014

4. Barr M. V. 1984. “Instrumented horizontal drilling for tunneling site investigations.” Ph.D. thesis Faculty of Engineering Imperial College of Science and Technology.

5. Deep learning-based automatic volumetric damage quantification using depth camera

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