Georg H. Erharter 
- (2023): Discussion on the paper “Success and challenges in predicting TBM penetration rate using recurrent neural networks” by Feng Shan, Xuzhen He, Danial Jahed Armaghani, Pin Zhang, Daichao Sheng. In: Tunnelling and Underground Space Technology, v. 135 (May 2023).
- (2023): Building information modelling based ground modelling for tunnel projects – Tunnel Angath/Austria. In: Tunnelling and Underground Space Technology, v. 135 (May 2023).
- (2022): Practical recommendations for machine learning in underground rock engineering – On algorithm development, data balancing, and input variable selection. In: Geomechanics and Tunnelling, v. 15, n. 5 (October 2022).
- (2022): Towards optimized TBM cutter changing policies with reinforcement learning. In: Geomechanics and Tunnelling, v. 15, n. 5 (October 2022).
- (2022): Potential applications of machine learning for BIM in tunnelling. In: Geomechanics and Tunnelling, v. 15, n. 2 (April 2022).
- (2022): Improving face decisions in tunnelling by machine learning‐based MWD analysis. In: Geomechanics and Tunnelling, v. 15, n. 2 (April 2022).
- (2021): Reinforcement learning based process optimization and strategy development in conventional tunneling. In: Automation in Construction, v. 127 (July 2021).
- (2021): Learning decision boundaries for cone penetration test classification. In: Computer-Aided Civil and Infrastructure Engineering, v. 36, n. 4 (February 2021).
- (2021): On the pointlessness of machine learning based time delayed prediction of TBM operational data. In: Automation in Construction, v. 121 (January 2021).
- (2020): MSAC: Towards data driven system behavior classification for TBM tunneling. In: Tunnelling and Underground Space Technology, v. 103 (September 2020).
- (2020): Machine Learning in tunnelling – Capabilities and challenges. In: Geomechanics and Tunnelling, v. 13, n. 2 (April 2020).
- (2019): Geotechnical characteristics of soft rocks of the Inneralpine Molasse – Brenner Base Tunnel access route, Unterangerberg, Tyrol, Austria. In: Geomechanics and Tunnelling, v. 12, n. 6 (December 2019).
- (2019): Application of artificial neural networks for Underground construction – Chances and challenges – Insights from the BBT exploratory tunnel Ahrental Pfons. In: Geomechanics and Tunnelling, v. 12, n. 5 (October 2019).
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