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

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The interaction of tunnel boring machines with the rock mass is highly influenced by human, technical and geological factors. Interpretation of geological observations and TBM data is currently done on a subjective basis. Technologies based on Artificial Intelligence research, can be used to automatically classify TBM data into rock mass behaviour types. Albeit first results look promising, any technology poses the threat of malicious use that deliberately harms / benefits one or another party. This paper shows how an Artificial Neural Network (ANN) can be trained to achieve the best possible rock mass behaviour classification, or how such a system can be misused to yield a more optimistic, respectively pessimistic classification to fortify the interests of one party. However, ANN also pose the chance to serve as an independent objective opinion and to improve the self‐consistency of geological classifications.
Original languageEnglish
Pages (from-to)472-477
Number of pages6
JournalGeomechanics and Tunnelling
Issue number5
Publication statusPublished - Oct 2019


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