A short overview of soft computing techniques in tunnel construction
Tunnel construction is a complex technology, with a huge number of effective parameters, which cannot be accurately analyzed/designed using empirical or theoretical methods. With the rapid development of computer technologies, Soft Computing (SC) approaches have been widely used in tunnel constructi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Bentham Science Publishers
2022
|
| Subjects: | |
| Online Access: | http://eprints.um.edu.my/43469/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128963318&doi=10.2174%2f18748368-v16-e2201120&partnerID=40&md5=7be0b6559bbeae0ffcf4cd63da08ac57 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Tunnel construction is a complex technology, with a huge number of effective parameters, which cannot be accurately analyzed/designed using empirical or theoretical methods. With the rapid development of computer technologies, Soft Computing (SC) approaches have been widely used in tunnel construction. Typically, the two common tunneling methods, blasting and mechanical excavation (e.g., tunnel boring machine, shield, pipe jacking method), have been used in conjunction with some SC techniques to solve specific problems and have shown a good fit. On this basis, this paper first summarizes the current research on the application of SC techniques in the field of tunnel construction methods. For example, in the case of blasting, the application of SC techniques is focusing on the environmental problems induced by blasting, such as the prediction of peak particle velocity and over-break. As for mechanical tunnel construction, the SC techniques were used to analyze the boring characteristics of the machine, such as the estimation of penetration rate and advance rate. Additionally, an important aspect for the application of SC techniques is the identification of the influencing factors for each of the study subjects, i.e. the necessary input parameters for the SC. Finally, this paper elaborates on the working process of the supervised learning models, highlights the points that need to be taken care of in each step, and points out that the SC technique, which is synergistic with the physical process, is more useful to explain the actual phenomenon. © 2022 He et al. |
|---|