Investigation of Transfer Learning for Tunnel Support Design

Amichai Mitelman and Alon Urlainis ()
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Amichai Mitelman: Department of Civil Engineering, Ariel University, Ariel 40700, Israel
Alon Urlainis: Department of Civil Engineering, Ariel University, Ariel 40700, Israel

Mathematics, 2023, vol. 11, issue 7, 1-15

Abstract: The potential of machine learning (ML) tools for enhancing geotechnical analysis has been recognized by several researchers. However, obtaining a sufficiently large digital dataset is a major technical challenge. This paper investigates the use of transfer learning, a powerful ML technique, used for overcoming dataset size limitations. The study examines two scenarios where transfer learning is applied to tunnel support analysis. The first scenario investigates transferring knowledge between a ground formation that has been well-studied to a new formation with very limited data. The second scenario is intended to investigate whether transferring knowledge is possible from a dataset that relies on simplified tunnel support analysis to a more complex and realistic analysis. The technical process for transfer learning involves training an Artificial Neural Network (ANN) on a large dataset and adding an extra layer to the model. The added layer is then trained on smaller datasets to fine-tune the model. The study demonstrates the effectiveness of transfer learning for both scenarios. On this basis, it is argued that, with further development and refinement, transfer learning could become a valuable tool for ML-related geotechnical applications.

Keywords: artificial neural networks; geotechnical engineering; machine-learning; transfer learning; tunnel support (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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