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. Dans: Tunnelling and Underground Space Technology, v. 135 (mai 2023).
- (2023): Building information modelling based ground modelling for tunnel projects – Tunnel Angath/Austria. Dans: Tunnelling and Underground Space Technology, v. 135 (mai 2023).
- (2022): Practical recommendations for machine learning in underground rock engineering – On algorithm development, data balancing, and input variable selection. Dans: Geomechanics and Tunnelling, v. 15, n. 5 (octobre 2022).
- (2022): Towards optimized TBM cutter changing policies with reinforcement learning. Dans: Geomechanics and Tunnelling, v. 15, n. 5 (octobre 2022).
- (2022): Potential applications of machine learning for BIM in tunnelling. Dans: Geomechanics and Tunnelling, v. 15, n. 2 (avril 2022).
- (2022): Improving face decisions in tunnelling by machine learning‐based MWD analysis. Dans: Geomechanics and Tunnelling, v. 15, n. 2 (avril 2022).
- (2021): Reinforcement learning based process optimization and strategy development in conventional tunneling. Dans: Automation in Construction, v. 127 (juillet 2021).
- (2021): Learning decision boundaries for cone penetration test classification. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 36, n. 4 (février 2021).
- (2021): On the pointlessness of machine learning based time delayed prediction of TBM operational data. Dans: Automation in Construction, v. 121 (janvier 2021).
- (2020): MSAC: Towards data driven system behavior classification for TBM tunneling. Dans: Tunnelling and Underground Space Technology, v. 103 (septembre 2020).
- (2020): Machine Learning in tunnelling – Capabilities and challenges. Dans: Geomechanics and Tunnelling, v. 13, n. 2 (avril 2020).
- (2019): Geotechnical characteristics of soft rocks of the Inneralpine Molasse – Brenner Base Tunnel access route, Unterangerberg, Tyrol, Austria. Dans: Geomechanics and Tunnelling, v. 12, n. 6 (décembre 2019).
- (2019): Application of artificial neural networks for Underground construction – Chances and challenges – Insights from the BBT exploratory tunnel Ahrental Pfons. Dans: Geomechanics and Tunnelling, v. 12, n. 5 (octobre 2019).
Structurae coopère avec
