Experimental Investigation and Prediction of the Permanent Deformation of Crushed Waste Rock Using an Artificial Neural Network Model-Reference-Cited by-同舟云学术

Experimental Investigation and Prediction of the Permanent Deformation of Crushed Waste Rock Using an Artificial Neural Network Model

Published:2022-05 Issue:5 Volume:22 Page:
ISSN:1532-3641
Container-title:International Journal of Geomechanics
language:en
Short-container-title:Int. J. Geomech.

Author:

Hao Shengpeng¹,Pabst Thomas²

Affiliation:

1. Dept. of Civil, Geological, and Mining Engineering, Polytechnique Montréal, Research Institute on Mines and Environment (RIME), C.P. 6079, Station Centre-ville, Montréal, QC H3C 3A7, Canada.

2. Dept. of Civil, Geological, and Mining Engineering, Polytechnique Montréal, Research Institute on Mines and Environment (RIME), C.P. 6079, Station Centre-ville, Montréal, QC H3C 3A7, Canada (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Soil Science

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29GM.1943-5622.0002363

Reference81 articles.

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2. Experimental Investigation and Development of Artificial Neural Network Model for the Properties of Locally Produced Light Weight Aggregate Concrete

3. Effect of loading frequencies on permanent deformation of unbound granular materials

4. ASTM. 2012. Standard test methods for laboratory compaction characteristics of soil using modified effort (56,000 ft-lbf/ft3 (2,700 kN-m/m3)). ASTM D1557-12e1. West Conshohocken, PA: ASTM.

5. ASTM. 2014. Standard test methods for specific gravity of soil solids by water pycnometer. ASTM D854-14. West Conshohocken, PA: ASTM.

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