Intelligent identification of soil and operation parameters in mechanised tunnelling by a hybrid model of artificial neural network-genetic algorithm (case study: Tabriz Metro Line 2)-Reference-Cited by-同舟云学术

Intelligent identification of soil and operation parameters in mechanised tunnelling by a hybrid model of artificial neural network-genetic algorithm (case study: Tabriz Metro Line 2)

Published:2022-06-30 Issue:4 Volume:39 Page:287-308
ISSN:1028-6608
Container-title:Civil Engineering and Environmental Systems
language:en
Short-container-title:Civil Engineering and Environmental Systems

Author:

Nikakhtar Leila¹^ORCID,Zare Shokrollah¹^ORCID,Mirzaei Nasirabad Hossein²^ORCID

Affiliation:

1. Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

2. Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran

Publisher

Informa UK Limited

Subject

Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10286608.2022.2075857

Reference50 articles.

1. Genetic Algorithm: Reviews, Implementations, and Applications

2. Predicting ground vibration induced by rock blasting using a novel hybrid of neural network and itemset mining

3. Airblast prediction through a hybrid genetic algorithm-ANN model

4. Behnia, D., and K. Shahriar. 2015. “Prediction of Tunnelling-Induced Settlement Using Gene Expression Programming.” 49th US Rock Mechanics/Geomechanics Symposium, OnePetro.

5. A hybrid RNN-GPOD surrogate model for real-time settlement predictions in mechanised tunnelling

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