Artificial Neural Network Based Online Rockmass Behavior Classification of TBM Data

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

The tunnel boring machine (TBM) which is currently excavating the exploratory tunnel Ahrental-Pfons of the Brenner Base Tunnel records parameters like cutter head torque or advance pressure on a ten second interval. TBM data like this and derived indicators (e.g.: specific penetration, torque ratio…) are often used as additional help for assessing the response of the rockmass towards the excavation. The goal of this paper is to explore the applicability of a special type of artificial neural network (ANN) for an automatic online classification of the rockmass behavior solely based on TBM data. An ensemble of Long Short Term Memory (LSTM) networks with additional one-dimensional convolutional layers on top, is used to classify individual features of TBM data in mini-batches. The 1D convolutional input layers enhance the ANN’s ability to extract significant features of the data. After an experimental phase, the best performance was achieved with an ensemble of eight convolutional LSTM – networks, where four networks each were deployed on the features torque - ratio and torque. Although the final categorical classification of the ensemble only achieved an overall accuracy of 74.4%, the probabilistic, relative output still yields valuable information about the rockmass behavior and could be used to aid geotechnicians in a real-world scenario.
Originalspracheenglisch
TitelInformation Technology in Geo-Engineering
UntertitelProceedings of the 3rd International Conference (ICITG), Guimarães, Portugal
Herausgeber (Verlag)Springer
DOIs
PublikationsstatusVeröffentlicht - 2019

Publikationsreihe

NameGeomechanics and Geoengineering
Herausgeber (Verlag)Springer

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Tunnels
Neural networks
Torque
Excavation
Long short-term memory

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    Erharter, G. H., Marcher, T., & Reinhold, C. (2019). Artificial Neural Network Based Online Rockmass Behavior Classification of TBM Data. in Information Technology in Geo-Engineering: Proceedings of the 3rd International Conference (ICITG), Guimarães, Portugal (Geomechanics and Geoengineering). Springer. https://doi.org/10.1007/978-3-030-32029-4_16

    Artificial Neural Network Based Online Rockmass Behavior Classification of TBM Data. / Erharter, Georg Hermann; Marcher, Thomas; Reinhold, Chris.

    Information Technology in Geo-Engineering: Proceedings of the 3rd International Conference (ICITG), Guimarães, Portugal. Springer, 2019. (Geomechanics and Geoengineering).

    Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

    Erharter, GH, Marcher, T & Reinhold, C 2019, Artificial Neural Network Based Online Rockmass Behavior Classification of TBM Data. in Information Technology in Geo-Engineering: Proceedings of the 3rd International Conference (ICITG), Guimarães, Portugal. Geomechanics and Geoengineering, Springer. https://doi.org/10.1007/978-3-030-32029-4_16
    Erharter GH, Marcher T, Reinhold C. Artificial Neural Network Based Online Rockmass Behavior Classification of TBM Data. in Information Technology in Geo-Engineering: Proceedings of the 3rd International Conference (ICITG), Guimarães, Portugal. Springer. 2019. (Geomechanics and Geoengineering). https://doi.org/10.1007/978-3-030-32029-4_16
    Erharter, Georg Hermann ; Marcher, Thomas ; Reinhold, Chris. / Artificial Neural Network Based Online Rockmass Behavior Classification of TBM Data. Information Technology in Geo-Engineering: Proceedings of the 3rd International Conference (ICITG), Guimarães, Portugal. Springer, 2019. (Geomechanics and Geoengineering).
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    abstract = "The tunnel boring machine (TBM) which is currently excavating the exploratory tunnel Ahrental-Pfons of the Brenner Base Tunnel records parameters like cutter head torque or advance pressure on a ten second interval. TBM data like this and derived indicators (e.g.: specific penetration, torque ratio…) are often used as additional help for assessing the response of the rockmass towards the excavation. The goal of this paper is to explore the applicability of a special type of artificial neural network (ANN) for an automatic online classification of the rockmass behavior solely based on TBM data. An ensemble of Long Short Term Memory (LSTM) networks with additional one-dimensional convolutional layers on top, is used to classify individual features of TBM data in mini-batches. The 1D convolutional input layers enhance the ANN’s ability to extract significant features of the data. After an experimental phase, the best performance was achieved with an ensemble of eight convolutional LSTM – networks, where four networks each were deployed on the features torque - ratio and torque. Although the final categorical classification of the ensemble only achieved an overall accuracy of 74.4{\%}, the probabilistic, relative output still yields valuable information about the rockmass behavior and could be used to aid geotechnicians in a real-world scenario.",
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    AB - The tunnel boring machine (TBM) which is currently excavating the exploratory tunnel Ahrental-Pfons of the Brenner Base Tunnel records parameters like cutter head torque or advance pressure on a ten second interval. TBM data like this and derived indicators (e.g.: specific penetration, torque ratio…) are often used as additional help for assessing the response of the rockmass towards the excavation. The goal of this paper is to explore the applicability of a special type of artificial neural network (ANN) for an automatic online classification of the rockmass behavior solely based on TBM data. An ensemble of Long Short Term Memory (LSTM) networks with additional one-dimensional convolutional layers on top, is used to classify individual features of TBM data in mini-batches. The 1D convolutional input layers enhance the ANN’s ability to extract significant features of the data. After an experimental phase, the best performance was achieved with an ensemble of eight convolutional LSTM – networks, where four networks each were deployed on the features torque - ratio and torque. Although the final categorical classification of the ensemble only achieved an overall accuracy of 74.4%, the probabilistic, relative output still yields valuable information about the rockmass behavior and could be used to aid geotechnicians in a real-world scenario.

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