Estimating the penetration rate of tunnel boring machines via gradient boosting algorithms-Reference-Cited by-同舟云学术

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Estimating the penetration rate of tunnel boring machines via gradient boosting algorithms

Published:2024-10 Issue: Volume:136 Page:108985
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Ghorbani Ebrahim^ORCID,Yagiz Saffet

Funder

Nazarbayev University

Publisher

Elsevier BV

Reference55 articles.

1. Bayesian prediction of TBM penetration rate in rock mass;Adoko;Eng. Geol.,2017

2. Prediction of the penetration rate and number of consumed disc cutters of tunnel boring machines (TBMs) using artificial neural network (ANN) and support vector machine (SVM)—case study: Beheshtabad water conveyance tunnel in Iran;Afradi;Asian J. Water Environ. Pollut.,2019

3. Developing the rule of thumb for evaluating penetration rate of TBM, using binary classification;Akbarzadeh;Geotech. Geol. Eng.,2022

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

5. Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment;Bardhan;J. Rock Mech. Geotech. Eng.,2021

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