Application of artificial neural networks for Underground construction – Chances and challenges – Insights from the BBT exploratory tunnel Ahrental Pfons

Erharter, Georg H.; Marcher, Thomas; Reinhold, Chris

doi:10.1002/geot.201900027

Cited by 26 publications

(14 citation statements)

References 10 publications

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“…learned from it. ML techniques can be used to improve efficiency and self consistency in daily tunnel design and construction work [2].…”

Section: Topics Topicsmentioning

confidence: 99%

“…[3]). -Ethical use by all involved parties is imperative to build the necessary confidence that is required to make the most out of this technology [2].…”

Section: Topics Topicsmentioning

confidence: 99%

“…In [18], the applicability of a special type of artificial neural network (ANN) for the automatic online classification of rock mass behaviour solely based on TBM data has been explored. In [2], it is shown how an AI system can be trained to achieve the best possible rock mass behaviour classification (in the sense of "as close to the human classification as possible"), or how such a system can be misused to yield a more optimistic or pessimistic classification to further the interests of one party.…”

Section: Unsupervised Learningmentioning

confidence: 99%

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Machine Learning in tunnelling – Capabilities and challenges

Marcher

Erharter

Winkler

2020

Geomechanics and Tunnelling

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Digitalization will change the way of gathering geological data, methods of rock classification, application of design analyses in the field of tunnelling as well as tunnel construction and maintenance processes. In recent years, a rapid increase in the successful application of digital techniques (Building Information Modelling and Machine Learning (ML)) for a variety of challenging tasks has been observed. Driven by the increasing overall amount of data combined with the easy availability of more computing power, a sharp increase in the successful deployment of techniques of ML has been seen for different tasks. ML has been introduced in many sciences and technologies and it has finally arrived in the fields of geotechnical engineering, tunnelling and engineering geology, although still not as far developed as in other disciplines. This paper focuses on the potential of ML methods for geotechnical purposes in general and tunnelling in particular. Applications such as automatic rock mass behaviour classification using data from tunnel boring machines (TBM), updating of the geological prognosis ahead of the tunnel face, data driven interpretation of 3D displacement data or fully automatic tunnel inspection will be discussed.

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“…learned from it. ML techniques can be used to improve efficiency and self consistency in daily tunnel design and construction work [2].…”

Section: Topics Topicsmentioning

confidence: 99%

“…[3]). -Ethical use by all involved parties is imperative to build the necessary confidence that is required to make the most out of this technology [2].…”

Section: Topics Topicsmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

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Machine Learning in tunnelling – Capabilities and challenges

Marcher

Erharter

Winkler

2020

Geomechanics and Tunnelling

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“…encountered distribution of lithologies, ground types or ground sections. This also applies for laboratory and in-situ tests as well as for indirect methods like the evaluation of TBM or drilling data, which have to be related to the ground model [22].…”

Section: Construction: Prediction Model Vs Documentationmentioning

confidence: 99%

Digitale Baugrundmodelle im Tunnelbau – Status, Chancen und Risiken

Weil

2020

Geomechanics and Tunnelling

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Digital data acquisition and processing in geology and geotechnics are essential in all phases of a tunnelling project. This article describes typical work flows of engineering geology and the digital tools used, and discusses the requirements, chances and risks associated with the integration of ground models into BIM projects. The handling of factual data is depicted, but the main topic is the development and application of interpreted geological models for tunnelling. Aspects like the transferred information and its localisation, uncertainty of a prediction and requirements for formats, data structures and software environment are discussed. Efficient exchange and long‐term usability of information demands clear definitions for the specialist models and use cases to be covered.

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“…While ML techniques have been used in other disciplines for some time, the demand for ML applications in geotechnics and tunnelling is growing more slowly. Many of the publications using ML for problem solving in geotechnical engineering or tunnelling rely on supervised ML; with [1][2][3] three papers are given that use artificial neural networks (ANN) to classify rock mass behaviour using tunnel boring machine (TBM) operational data.…”

Section: Introductionmentioning

confidence: 99%

Capabilities and Challenges Using Machine Learning in Tunnelling

Marcher¹,

Erharter²,

Unterlaß³

2022

Theory and Practice of Tunnel Engineering

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Digitalization changes the design and operational processes in tunnelling. The way of gathering geological data in the field of tunnelling, the methods of rock mass classification as well as the application of tunnel design analyses, tunnel construction processes and tunnel maintenance will be influenced by this digital transformation. The ongoing digitalization in tunnelling through applications like building information modelling and artificial intelligence, addressing a variety of difficult tasks, is moving forward. Increasing overall amounts of data (big data), combined with the ease to access strong computing powers, are leading to a sharp increase in the successful application of data analytics and techniques of artificial intelligence. Artificial Intelligence now arrives also in the fields of geotechnical engineering, tunnelling and engineering geology. The chapter focuses on the potential for machine learning methods – a branch of Artificial Intelligence - in tunnelling. Examples will show that training artificial neural networks in a supervised manner works and yields valuable information. Unsupervised machine learning approaches will be also discussed, where the final classification is not imposed upon the data, but learned from it. Finally, reinforcement learning seems to be trendsetting but not being in use for specific tunnel applications yet.

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Application of artificial neural networks for Underground construction – Chances and challenges – Insights from the BBT exploratory tunnel Ahrental Pfons

Cited by 26 publications

References 10 publications

Machine Learning in tunnelling – Capabilities and challenges

Machine Learning in tunnelling – Capabilities and challenges

Digitale Baugrundmodelle im Tunnelbau – Status, Chancen und Risiken

Capabilities and Challenges Using Machine Learning in Tunnelling

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