Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Hangingwall Stability-Reference-Cited by-同舟云学术

Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Hangingwall Stability

Published:2018-03 Issue:2 Volume:32 Page:04017086
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Qi Chongchong¹^ORCID,Fourie Andy²,Ma Guowei²,Tang Xiaolin³,Du Xuhao⁴

Affiliation:

1. Ph.D. Student, School of Civil, Environmental and Mining Engineering, Univ. of Western Australia, Perth 6009, Australia (corresponding author). ORCID: .

2. Professor, School of Civil, Environmental and Mining Engineering, Univ. of Western Australia, Perth 6009, Australia.

3. Ph.D. Student, Planning and Transport Research Centre, Univ. of Western Australia, Perth 6009, Australia.

4. Ph.D. Student, School of Mechanical and Chemical Engineering, Univ. of Western Australia, Perth 6009, Australia.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000737

Reference55 articles.

1. Modeling and Predicting Biological Performance of Contact Stabilization Process Using Artificial Neural Networks

2. Community Detection in Complex Networks: Multi–objective Enhanced Firefly Algorithm

3. Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression

4. Artificial neural network application for the prediction of ground surface movements induced by shield tunnelling

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