Research Article
A Review of Present Status & Future Aspects of Computer Vision and Machine Learning Techniques used in Underground Tunnel Engineering: Digital Revolution
@INPROCEEDINGS{10.4108/eai.24-3-2022.2318770, author={Sumit Kumari and Vikas Siwach and Yudhvir Singh}, title={A Review of Present Status \& Future Aspects of Computer Vision and Machine Learning Techniques used in Underground Tunnel Engineering: Digital Revolution}, proceedings={Proceedings of the 3rd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2022, 24-25 March 2022, New Delhi, India}, publisher={EAI}, proceedings_a={ICIDSSD}, year={2023}, month={5}, keywords={artificial neural networks computer vision digital revolution machine learning deep learning underground mining}, doi={10.4108/eai.24-3-2022.2318770} }- Sumit Kumari
Vikas Siwach
Yudhvir Singh
Year: 2023
A Review of Present Status & Future Aspects of Computer Vision and Machine Learning Techniques used in Underground Tunnel Engineering: Digital Revolution
ICIDSSD
EAI
DOI: 10.4108/eai.24-3-2022.2318770
Abstract
The AEC (Architecture Engineering Construction) industry is going through the phase of a digital revolution driven by bombarding it with digitization and automation. These developments can be possible due to the advancement in research areas of information technologies and computer science which attracted many researchers. Simultaneously, in the population-driven underground work, the technological involvement has also been increased with the help of the digital revolution. Underground tunnels are important assets that constantly demand effective construction, planning, and maintenance, etc. Therefore, applications of computer vision and machine learning techniques in underground tunnel engineering provide different sets of challenges and opportunities by enabling larger clarity and accuracy into the subsystems and process of underground tunnel engineering. The main aim of this study is to examine the current state of computer vision and machine learning, as well as related approaches that aid in the digital growth of underground tunnel engineering. In this paper, the research of the last two decades in the area of underground tunneling by using computer vision and machine learning has been discussed and compared. In addition, this research will help the researchers to explore the digital revolution of underground tunnel engineering.
