Computer vision-based technique to measure displacement in selected soil tests

被引:0
作者
Obaidat, MT [1 ]
Attom, MF [1 ]
机构
[1] Jordan Univ Sci & Technol, Dept Civil Engn, Irbid, Jordan
来源
GEOTECHNICAL TESTING JOURNAL | 1998年 / 21卷 / 01期
关键词
computer vision; normal case photography; soil properties; displacement; strain;
D O I
暂无
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
The potential of normal case photography using charge-coupled-device (CCD) cameras to extract deformation (strain) in soil specimens of two soil tests, i.e., the unconfined compression test and the direct shear test, was investigated. A PC-based digital vision system was used to obtain accurately measured linear displacement. Using remolded soil specimens, comparisons between displacement measurements using ASTM conventional methods and the normal case photography method showed that use of the latter method is promising and could be used as a substitute for strain gages. Experimental investigation showed that differences between displacement measurements using conventional ASTM procedures and computer vision technique were consistently within 0.04 +/- 0.15 to 0.3 +/- 0.23 mm for unconfined compression tests and direct shear tests, respectively. This was compatible with the image scale where one pixel on the image domain was equivalent to about 0.4 mm on object space coordinates. Statistical correlations between strains by the two methods supported this result. Image scale and resolution were found to be the two major factors affecting the accuracy of the measurements. The results of this work are expected to open the door for geotechnical engineers and agencies responsible for soil testing standards to incorporate image-based analysis in soil testing. This will indeed bridge the gap between manual and fully automated soil testing measurements.
引用
收藏
页码:31 / 37
页数:7
相关论文
共 50 条
[41]   COMPUTER VISION-BASED APPROACH TO END MILL TOOL MONITORING [J].
Klancnik, S. ;
Ficko, M. ;
Balic, J. ;
Pahole, I .
INTERNATIONAL JOURNAL OF SIMULATION MODELLING, 2015, 14 (04) :571-583
[42]   Computer vision-based statistical crack quantification for concrete structures [J].
Bae, Hyunjin ;
An, Yun-Kyu .
MEASUREMENT, 2023, 211
[43]   Computer Vision-Based Architecture for IoMT Using Deep Learning [J].
Al-qudah, Rabiah ;
Aloqaily, Moayad ;
Karray, Fakhri .
2022 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, IWCMC, 2022, :931-936
[44]   A review of computer vision-based approaches for physical rehabilitation and assessment [J].
Bappaditya Debnath ;
Mary O’Brien ;
Motonori Yamaguchi ;
Ardhendu Behera .
Multimedia Systems, 2022, 28 :209-239
[45]   A computer vision-based method for measuring shape of tobacco strips [J].
Xu, Dayong ;
Wang, Shu ;
Zhang, Long ;
Li, Xinfeng ;
Fan, Mingdeng ;
Zhang, Wen ;
Xia, Yingwei ;
Gao, Zhenyu ;
Du, Jinsong .
Tobacco Science and Technology, 2015, 48 (02) :91-95
[46]   Computer vision-based gesture recognition for an augmented reality interface [J].
Störring, M ;
Moeslund, TB ;
Liu, Y ;
Granum, E .
PROCEEDINGS OF THE FOURTH IASTED INTERNATIONAL CONFERENCE ON VISUALIZATION, IMAGING, AND IMAGE PROCESSING, 2004, :766-771
[47]   Visual-inertial displacement sensing using data fusion of vision-based displacement with acceleration [J].
Park, Jong-Woong ;
Moon, Do-Soo ;
Yoon, Hyungchul ;
Gomez, Fernando ;
Spencer, Billie F., Jr. ;
Kim, Jong R. .
STRUCTURAL CONTROL & HEALTH MONITORING, 2018, 25 (03)
[48]   Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring [J].
Spencer, Billie F., Jr. ;
Hoskere, Vedhus ;
Narazaki, Yasutaka .
ENGINEERING, 2019, 5 (02) :199-222
[49]   Computer Vision-Based Classification of Hand Grip Variations in Neurorehabilitation [J].
Zariffa, Jose ;
Steeves, John D. .
2011 IEEE INTERNATIONAL CONFERENCE ON REHABILITATION ROBOTICS (ICORR), 2011,
[50]   A Computer Vision-Based System for Metal Sheet Pick Counting [J].
Ji, Jirasak ;
Pannakkong, Warut ;
Buddhakulsomsiri, Jirachai .
CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 75 (02) :3643-3656