An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

Qiu, Y; Huang, S; Armaghani, DJ; Pradhan, B; Zhou, A; Zhou, J

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

Qiu, Y Huang, S Armaghani, DJ Pradhan, B

Zhou, A Zhou, J

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Publisher:: TECH SCIENCE PRESS
Publication Type:: Journal Article
Citation:: CMES - Computer Modeling in Engineering and Sciences, 2024, 138, (3), pp. 2873-2897
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Qiu, Y
dc.contributor.author	Huang, S
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Zhou, A
dc.contributor.author	Zhou, J
dc.date.accessioned	2025-01-23T04:36:51Z
dc.date.available	2025-01-23T04:36:51Z
dc.date.issued	2024-01-01
dc.identifier.citation	CMES - Computer Modeling in Engineering and Sciences, 2024, 138, (3), pp. 2873-2897
dc.identifier.issn	1526-1492
dc.identifier.issn	1526-1506
dc.identifier.uri	http://hdl.handle.net/10453/184070
dc.description.abstract	As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy. Hence, this study aims to introduce a hybrid random forest (RF) technique optimized by global harmony search with generalized opposition-based learning (GOGHS) for forecasting TBM advance rate (AR). Optimizing the RF hyper-parameters in terms of, e.g., tree number and maximum tree depth is the main objective of using the GOGHS-RF model. In the modelling of this study, a comprehensive database with the most influential parameters on TBM together with TBM AR were used as input and output variables, respectively. To examine the capability and power of the GOGHS-RF model, three more hybrid models of particle swarm optimization-RF, genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR. Evaluation of the developed models was performed by calculating several performance indices, including determination coefficient (R2), root-mean-square-error (RMSE), and mean-absolute-percentage-error (MAPE). The results showed that the GOGHS-RF is a more accurate technique for estimating TBM AR compared to the other applied models. The newly-developed GOGHS-RF model enjoyed R2 = 0.9937 and 0.9844, respectively, for train and test stages, which are higher than a pre-developed RF. Also, the importance of the input parameters was interpreted through the SHapley Additive exPlanations (SHAP) method, and it was found that thrust force per cutter is the most important variable on TBM AR. The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.
dc.language	English
dc.publisher	TECH SCIENCE PRESS
dc.relation.ispartof	CMES - Computer Modeling in Engineering and Sciences
dc.relation.isbasedon	10.32604/cmes.2023.029938
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0102 Applied Mathematics, 0103 Numerical and Computational Mathematics, 0915 Interdisciplinary Engineering
dc.subject.classification	Applied Mathematics
dc.subject.classification	4901 Applied mathematics
dc.subject.classification	4903 Numerical and computational mathematics
dc.title	An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate
dc.type	Journal Article
utslib.citation.volume	138
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)/Transport Research Centre (TRC) Associate Members
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-01-23T04:36:49Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	138
utslib.citation.issue	3

Abstract:

As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy. Hence, this study aims to introduce a hybrid random forest (RF) technique optimized by global harmony search with generalized opposition-based learning (GOGHS) for forecasting TBM advance rate (AR). Optimizing the RF hyper-parameters in terms of, e.g., tree number and maximum tree depth is the main objective of using the GOGHS-RF model. In the modelling of this study, a comprehensive database with the most influential parameters on TBM together with TBM AR were used as input and output variables, respectively. To examine the capability and power of the GOGHS-RF model, three more hybrid models of particle swarm optimization-RF, genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR. Evaluation of the developed models was performed by calculating several performance indices, including determination coefficient (R2), root-mean-square-error (RMSE), and mean-absolute-percentage-error (MAPE). The results showed that the GOGHS-RF is a more accurate technique for estimating TBM AR compared to the other applied models. The newly-developed GOGHS-RF model enjoyed R2 = 0.9937 and 0.9844, respectively, for train and test stages, which are higher than a pre-developed RF. Also, the importance of the input parameters was interpreted through the SHapley Additive exPlanations (SHAP) method, and it was found that thrust force per cutter is the most important variable on TBM AR. The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

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http://hdl.handle.net/10453/184070