Performance Evaluation Indicator (PEI): A new paradigm to evaluate the competence of machine learning classifiers in predicting rockmass conditions-Reference-Cited by-同舟云学术

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Performance Evaluation Indicator (PEI): A new paradigm to evaluate the competence of machine learning classifiers in predicting rockmass conditions

Published:2021-01 Issue: Volume:47 Page:101232
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

Zhu Mengqi,Gutierrez Marte,Zhu Hehua,Ju J. Woody,Sarna Sharmin

Funder

China Scholarship Council

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems

Reference42 articles.

1. An ANN to predict ground condition ahead of tunnel face using TBM operational data;Jung;KSCE J. Civ. Eng.,2019

2. Development of a rock mass characteristics model for TBM penetration rate prediction;Gong;Int. J. Rock Mech. Min. Sci.,2009

3. Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass;Yagiz;Int. J. Rock Mech. Min. Sci.,2015

4. Bayesian networks-based shield TBM risk management system: methodology development and application;Chung;KSCE J. Civ. Eng.,2019

5. Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data;Liu;Tunn. Undergr. Space Technol.,2020

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1. Model-based offline reinforcement learning framework for optimizing tunnel boring machine operation;Underground Space;2024-12

2. Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity;Computer Methods in Applied Mechanics and Engineering;2024-11

3. Multi-output prediction for TBM operation parameters based on stacking ensemble algorithm;Tunnelling and Underground Space Technology;2024-10

4. 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

5. Intelligent Classification of Rocks in Mountain Highway Tunnels Using ISSA-ELM Model;Geotechnical and Geological Engineering;2024-09-14

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