Success and challenges in predicting TBM penetration rate using recurrent neural networks

Shan, F; He, X; Jahed Armaghani, D; Zhang, P; Sheng, D

Success and challenges in predicting TBM penetration rate using recurrent neural networks

Shan, F

He, X

Jahed Armaghani, D Zhang, P Sheng, D

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Tunnelling and Underground Space Technology, 2022, 130, pp. 1-13
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Shan, F https://orcid.org/0000-0001-7497-6768
dc.contributor.author	He, X https://orcid.org/0000-0001-5336-3663
dc.contributor.author	Jahed Armaghani, D
dc.contributor.author	Zhang, P
dc.contributor.author	Sheng, D https://orcid.org/0000-0002-1665-6931
dc.date.accessioned	2023-03-23T00:42:34Z
dc.date.available	2023-03-23T00:42:34Z
dc.date.issued	2022-12-01
dc.identifier.citation	Tunnelling and Underground Space Technology, 2022, 130, pp. 1-13
dc.identifier.issn	0886-7798
dc.identifier.issn	1878-4364
dc.identifier.uri	http://hdl.handle.net/10453/168115
dc.description.abstract	Tunnel Boring Machines (TBMs) have been increasingly used in tunnelling projects. Forecasting future TBM performance would be desirable for project time management and cost control. We aim to use recurrent neural networks to predict the near future TBM penetration rate from historical data. Our datasets are composed of Changsha and Zhengzhou metro lines, with totally different geological conditions. In the experiments, the one-step forecast of TBM penetration rate by the traditional recurrent neural network (RNN) or long short-term memory (LSTM) is relatively accurate, irrespective of the different geological conditions used in training and evaluation. Predicting the next Nth step penetration rate proves to be more challenging and depends on the time to the future or the distance ahead of the TBM cutterhead. There are generally time lags between measured and predicted results. The recursive RNN is then developed to address the lag problems, but to no avail. Alternative methods for predicting future penetration rates are studied, including the penetration rate at the Nth step in the future and the average penetration rate of the next N steps, with the latter being trained by long-input or short-input methods. The average N-step forecast using short inputs provides the best results, and its performance over other alternatives becomes more distinct as the number N increases. We also discuss the possibility of the forecast problem as a quasi-random walk, which means that forecasting penetration rate cannot easily be achieved using low-frequency data with RNNs, and that the accuracy depends on the correlation between the last and predicted steps in the data.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Tunnelling and Underground Space Technology
dc.relation.isbasedon	10.1016/j.tust.2022.104728
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0914 Resources Engineering and Extractive Metallurgy
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Success and challenges in predicting TBM penetration rate using recurrent neural networks
dc.type	Journal Article
utslib.citation.volume	130
utslib.for	0905 Civil Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
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/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-23T00:41:32Z
pubs.publication-status	Published
pubs.volume	130

Abstract:

Tunnel Boring Machines (TBMs) have been increasingly used in tunnelling projects. Forecasting future TBM performance would be desirable for project time management and cost control. We aim to use recurrent neural networks to predict the near future TBM penetration rate from historical data. Our datasets are composed of Changsha and Zhengzhou metro lines, with totally different geological conditions. In the experiments, the one-step forecast of TBM penetration rate by the traditional recurrent neural network (RNN) or long short-term memory (LSTM) is relatively accurate, irrespective of the different geological conditions used in training and evaluation. Predicting the next Nth step penetration rate proves to be more challenging and depends on the time to the future or the distance ahead of the TBM cutterhead. There are generally time lags between measured and predicted results. The recursive RNN is then developed to address the lag problems, but to no avail. Alternative methods for predicting future penetration rates are studied, including the penetration rate at the Nth step in the future and the average penetration rate of the next N steps, with the latter being trained by long-input or short-input methods. The average N-step forecast using short inputs provides the best results, and its performance over other alternatives becomes more distinct as the number N increases. We also discuss the possibility of the forecast problem as a quasi-random walk, which means that forecasting penetration rate cannot easily be achieved using low-frequency data with RNNs, and that the accuracy depends on the correlation between the last and predicted steps in the data.

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http://hdl.handle.net/10453/168115