Study on the Estimation of Concrete Defects Volume on Dam Body Surface

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Akira Ishii Hiroaki Sugawara Masazumi Amakata

Abstract

To maintain the safety and functionality of dams over the long term, it is necessary to make inspections more labor-saving and efficient using the latest technology and to improve the sophistication of inspections based on data. Although dam inspections cover a wide range of items, this study focuses on the continuous monitoring of popouts, a phenomenon of concrete deterioration occurring on the surface of a dam body. It is difficult to predict whether a popout will occur from information on the body surface of the dam, owing to the generation mechanism of the popout. The number of popouts was monitored over time; however, no examples of shape changes were monitored over time. Advancements in various digital technologies are required to accurately evaluate changes in the dam body's surface over time; therefore, in this study, three-dimensional (3D) point-cloud data is created by the Structure from Motion (SfM) from images captured by a Unmanned Aerial Vehicle (UAV) of the concrete defect area due to the popout in an arch dam in the Tohoku region of Japan. The volume of concrete defects of a popout in each of two different periods was calculated by estimating the plane shape of the surface of the dam body. In addition, the shapes of two popouts were compared to confirm the possibility of predictive signs of change.  

Abstract 64 | PDF Downloads 58

##plugins.themes.bootstrap3.article.details##

Keywords

Dam Inspection, Popout, SfM, Point Cloud Data, Volume Estimation, ICP

References
Takato Yasuno, Junichiro Fujii, & Masazumi Amakata (2019). Pop-outs Segmentation for Concrete Prognosis Indices using UAV Monitoring and Dense Dilated Convolutions. Proceedings of 12th International Workshop on Structure Health Monitering (page 3175), September 10-12, California, USA. doi:10.12783/shm2019/32471

Takato Yasuno, Akira Ishii, Junichiro Fujii, Masazumi Amakata & Yuta Takahashi (2020). Generative Damage Learning for Concrete Aging Detection using Auto-flight Images. 2020 Proceedings of the 37th ISARC, pp. 1211-1218. doi:10.22260/ISARC2020/0166

Akira Ishii, Takato Yasuno, Masazumi Amakata, Hiroaki Sugawara, Junichiro Fujii & Kohei Ozasa (2020). Autonomous UAV flight using the Total Station Navigation System in Non-GNSS Environments. 2020 Proceedings of the 37th ISARC, pp. 685-692. doi:10.22260/ISARC2020/0096

Akira Ishii, Hiroaki Sugawara, Junichiro Fujii & Masazumi Amakata (2023), Study on How to Ensure the Accuracy of 3D Model in Digital Inspection of Dam Body Degradation Survey. Intelligence, Informatics and Infrastructure Conference. May 29, Tokyo, JAPAN. doi: 10.11532/jsceiii.4.2_38

Paul J. Besl, & Neil D. McKay (1992). Method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, No.2., pp. 239-256. doi:10.1109/34.121791
Section
Special Session Papers