Future Challenges in Tunnel Stability Analysis Using Artificial Intelligence and Machine Learning

DOI

10.2991/978-94-6463-258-3_33How to use a DOI?

Keywords

Machine Learning; Tunnel Stability Analysis; Geotechnics

Abstract

Entering the information age, we encounter a huge amount of data, and this big data requires automatic data analysis methods that provide machine learning and deep learning. Machine learning (ML) is a branch of artificial intelligence that is currently developing and evolving and is a very active field in computer science. In general, machine learning is defined as a set of methods that can automatically identify patterns from data and use undiscovered patterns to predict future data or make other types of decisions under uncertainty. This science is actually an interdisciplinary process that uses various sciences to advance its goals, including artificial intelligence, psychology, philosophy, information theory, statistics and probabilities, control theory, etc. The probabilistic approach to machine learning is closely related to the field of data mining but slightly different in focus and terminology. The importance of machine learning becomes valuable when we know where this learning can help people. On the other hand, the environment can change over time, the machine can adapt to it by learning these changes. The tunneling projects generate vast amounts of data on a daily basis, which includes the instrumentation and behavioral measurements of the tunnels, as well as geological data captured during the construction and execution of underground spaces. This data represents a valuable asset in the field of data science, as it can be leveraged for machine learning and predictive modeling purposes. According to available research, the application of machine learning in the field of important geological and geotechnical questions, which is often a very complex and unknown world full of information, can be used to improve the performance of optimal decision making in the future and plans. The main conclusion is that the number of research in this field increases almost exponentially and the most used (AI) technique is the Artificial Neural Network, and therefore lack of centralized Geotechnical data and datasets is one of the important challenges in Machine Learning and Deep Learning (DL).

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Saadati Ghader
AU  - Schneider-Muntau Barbara
AU  - Sina Javankhoshdel
AU  - Mett Michael
AU  - Kontrus Heiner
PY  - 2023
DA  - 2023/11/08
TI  - Future Challenges in Tunnel Stability Analysis Using Artificial Intelligence and Machine Learning
BT  - Proceedings of the Rocscience International Conference  (RIC 2023)
PB  - Atlantis Press
SP  - 317
EP  - 324
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-258-3_33
DO  - 10.2991/978-94-6463-258-3_33
ID  - Ghader2023
ER  -