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Suttisak Soralump. Integrating artificial intelligence for enhanced tunnel convergence monitoring : a paradigm shift from total station to pose estimation algorithm. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2567.
Integrating artificial intelligence for enhanced tunnel convergence monitoring : a paradigm shift from total station to pose estimation algorithm
Name: Anurak Puengrostham
Organization :
Kasetsart University. Faculty of Geotechnical Engineering
Email :
anurak.p@ckplc.com, anurak.pu@live.ku.th
Name: Suttisak Soralump
Organization :
Kasetsart University. Faculty of Geotechnical Engineering
keyword:
Computer vision in engineering
LCSH:
Artificial intelligence
LCSH:
Geotechnical engineering
LCSH:
Convergence
Abstract:
Tunnel collapse is a significant risk in tunnel both during construction and operation. Tunnel deformation monitoring, so called
tunnel convergence monitoring, play an important role in giving early warning signal to engineers to review the safety and making
decision to strengthening of tunnel support. Several methods for monitoring tunnel convergence include Total Station, Prismbased
monitoring, and Wireless Sensors Networks, 3D Laser Scanning and Photogrammetry, the use of total station to measure the
positions of optical targets has been, and still be, the dominant method for tunnel convergence monitoring, because it is already
utilized in typical construction for survey control network and alignment control of the tunnel itself. Nevertheless, there are some
limitations, and interfere with on-going construction activities. The reference points for total stations requires periodic maintenance
due to damages, misplaced by construction activities, or dynamic deformations over time of tunnel itself, which requires tremendous
efforts in interpretation of survey data to ensure reliability. To overcome the problem presented in Total Station method, the new
method with the integration of Artificial Intelligence (AI) using pose estimation algorithm to identify and locate the fiducial markers
for its position in 3 dimensional spaces will be presented. The relative distances between markers are calculated and compared
with the initial reading data to identify the degree of deformation of the tunnel. Both in-lab and in-field experiment were performed
to compare the proper environment for the camera to detect and extract visual data, and sensitivity of the camera to detect small
degree of movement
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2567
Modified:
2024-07-25
Issued:
2024-07-25
บทความ/Article
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BibliograpyCitation :
ใน มหาวิทยาลัยธรรมศาสตร์ ศูนย์รังสิต. คณะวิศวกรรมศาสตร์. วิศวกรรมสถานแห่งประเทศไทย ในพระบรมราชูปถัมภ์. การประชุมวิชาการวิศวกรรมโยธาแห่งชาติ ครั้งที่ 29 : จากภูมิปัญญาที่สืบสานสู่การรังสรรค์โลกที่ยั่งยืน (NCCE 29) (GTE47). ปทุมธานี : มหาวิทยาลัยธรรมศาสตร์ ศูนย์รังสิต
eng
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Anurak Puengrostham
| Title | Contributor | Type |
|---|---|---|
| Integrating artificial intelligence for enhanced tunnel convergence monitoring : a paradigm shift from total station to pose estimation algorithm
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Anurak Puengrostham;Suttisak Soralump | บทความ/Article |
Suttisak Soralump
| Title | Contributor | Type |
|---|---|---|
| Integrating artificial intelligence for enhanced tunnel convergence monitoring : a paradigm shift from total station to pose estimation algorithm
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Anurak Puengrostham;Suttisak Soralump | บทความ/Article |