Episodi
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In this episode NGI’s Georg H. Erharter and Tom F. Hansen talk about rock mass classification for tunnel construction and other applications. We start with a look back into early developed rock mass classification systems and then move forward up to state of the art applications of machine learning for rock mass classification and characterization that can be used today and perhaps will shape the future of this field.
SPEAKERS
Georg H. Erharter and Tom F: Hansen are both engineering geologists working in the rock engineering department of NGI. Tom F: Hansen has substantial practical experience ranging from rock engineering in coal mining at Svalbard up to engineering geological work for the Norwegian public railway authority. Today, he is finishing a PhD about the application of machine learning for rock engineering. Georg H. Erharter has brought practical experience from large alpine tunnel projects such as the Brenner- or Semmering Base tunnels to NGI and today is working in both engineering geological consultancy and research. He holds a PhD in Civil Engineering where he dealt with machine learning for geotechnics, and he is generally engaged with using modern technology to solve long standing problems of the field.
Georg H. Erharter
LinkedIn: https://www.linkedin.com/in/georg-h-erharter-b514b4125/
Google Scholar: https://scholar.google.com/citations?user=fb3-U50AAAAJ&hl=en
Tom F. Hansen
LinkedIn: https://www.linkedin.com/in/tom-f-hansen-62ba0878/
Google Scholar: https://scholar.google.com/citations?user=aqo4kowAAAAJ&hl=en
Contact: [email protected]
1st international Rock Mass Classification Conference (RMCC): https://www.rmcc2024.com/
MENTIONED PUBLICATIONS:
Bieniawski, Z. T. (1973): Engineering Classification of Jointed Rock Masses. In Civil Engineer in South Afrika, pp. 335–343.
Erharter, G. H.; Hansen, Tom F.; Qi, Shengwen; Bar, Neil; Marcher, Thomas (2023): A 2023 perspective on Rock Mass Classification Systems. In Wulf Schubert, Alexander Kluckner (Eds.): Proceedings of the 15th ISRM Congress 2023 & 72nd Geomechanics Colloquium. CHALLENGES IN ROCK MECHANICS AND ROCK ENGINEERING. 15th ISRM Congress 2023 & 72nd Geomechanics Colloquium. Salzburg / Austria, 9.-14. October 203. Austrian Society for Geomechanics, pp. 758–763. Available online at https://www.researchgate.net/publication/374554022_A_2023_perspective_on_Rock_Mass_Classification_Systems
Erharter, Georg H.; Bar, Neil; Hansen, Tom F.; Jain, Sumit; Marcher, Thomas (2024): International distribution and development of rock mass classification - a review. (IN REVIEW).
Hansen, T. F.; Aarset, A. (2024): Unsupervised machine learning for data-driven classification of rock mass using drilling data: How can a data-driven system handle limitations in existing rock mass classification systems? (NON PEER REVIEWED PREPRINT); Available online at http://arxiv.org/pdf/2405.02631v1.
Hansen, Tom F.; Liu, Zhongqiang; Torresen, Jim (2024): Predicting rock type from MWD tunnel data using a reproducible ML-modelling process. In Tunnelling and Underground Space Technology 152, p. 105843. Available online at https://www.sciencedirect.com/science/article/pii/S088677982400261X?via%3Dihub.
ISO 14689:2018: Geotechnical investigation and testing Identification, description and classification of rock
NGI (2015): Handbook The Q-system. Rock mass classification and support design. Available online at: https://www.ngi.no/en/research-and-consulting/infrastructure-container/tunnels-and-the-q-system/
ÖGG (2010): Guideline for the Geotechnical Design of Underground Structures with Conventional Excavation. Ground characterization and coherent procedure for the determination of excavation and support during design and construction. Translated from version 2.1. 2.1th ed. Salzburg. Available online at https://s3.nl-ams.scw.cloud/assets.oegg.at/attachments/ckdy7segc00o218lmcntn1266-geotech-rili-10-engl-endg%C3%BClitgeversion.pdf
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