A review and case study of Artificial intelligence and Machine learning methods used for ground condition prediction ahead of tunnel boring Machines-Reference-Cited by-同舟云学术

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A review and case study of Artificial intelligence and Machine learning methods used for ground condition prediction ahead of tunnel boring Machines

Published:2022-07 Issue: Volume:125 Page:104497
ISSN:0886-7798
Container-title:Tunnelling and Underground Space Technology
language:en
Short-container-title:Tunnelling and Underground Space Technology

Author:

Ayawah Prosper E.A.,Sebbeh-Newton Sylvanus,Azure Jessica W.A.,Kaba Azupuri G.A.,Anani Angelina,Bansah Samuel,Zabidi Hareyani

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Building and Construction

Reference53 articles.

1. Prediction of geological hazardous zones in front of a tunnel face using TSP-203 and artificial neural networks;Alimoradi;Tunn. Undergr. Sp. Technol.,2008

2. Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain;Althnian;Applied Sciences,2021

3. ARTIFICIAL NEURAL NETWORKS TECHNOLOGY;Anderson,1992

4. A Review of Hot Deck Imputation for Survey Non-response;Andridge;Int. Stat. Rev.,2010

5. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition;Armaghani;Tunn. Undergr. Sp. Technol.,2017

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2. Physics-informed and data-driven machine learning of rock mass classification using prior geological knowledge and TBM operational data;Tunnelling and Underground Space Technology;2024-10

3. Real-time unsupervised monitoring of earth pressure balance shield-induced sinkholes in mixed-face ground conditions via convolutional variational autoencoders;Tunnelling and Underground Space Technology;2024-10

4. Collapse mechanism and treatments of a deep tunnel in the weathered granite fault zone;Tunnelling and Underground Space Technology;2024-10

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