A New Position Measurement System Using a Motion-Capture Camera for Wind Tunnel Tests

Considering the characteristics of wind tunnel tests, a position measurement system that can minimize the effects on the flow of simulated wind must be established. In this study, a motion-capture camera was used to measure the displacement responses of structures in a wind tunnel test, and the applicability of the system was tested. A motion-capture system (MCS) could output 3D coordinates using two-dimensional image coordinates obtained from the camera. Furthermore, this remote sensing system had some flexibility regarding lab installation because of its ability to measure at relatively long distances from the target structures. In this study, we performed wind tunnel tests on a pylon specimen and compared the measured responses of the MCS with the displacements measured with a laser displacement sensor (LDS). The results of the comparison revealed that the time-history displacement measurements from the MCS slightly exceeded those of the LDS. In addition, we confirmed the measuring reliability of the MCS by identifying the dynamic properties (natural frequency, damping ratio, and mode shape) of the test specimen using system identification methods (frequency domain decomposition, FDD). By comparing the mode shape obtained using the aforementioned methods with that obtained using the LDS, we also confirmed that the MCS could construct a more accurate mode shape (bending-deflection mode shape) with the 3D measurements.

[1]  J. Gandemer.  Aerodynamic studies of built-up areas made by C.S.T.B. at Nantes, France , 1978 .

[2]  H. Woltring.  On optimal smoothing and derivative estimation from noisy displacement data in biomechanics , 1985 .

[3]  Daniele Inaudi,et al.  Mathematical model for the determination of the vertical displacement from internal horizontal measurements of a bridge , 1996, Smart Structures.

[4]  Christophe Delacourt,et al.  Application of a Terrestrial Laser Scanner (TLS) to the Study of the Séchilienne Landslide (Isère, France) , 2010, Remote. Sens..

[5]  Mustafa Gul,et al.  Damage assessment using flexibility and flexibility-based curvature for structural health monitoring , 2008 .

[6]  Timothy A. Reinhold,et al.  Wind tunnel modeling for civil engineering applications : proceedings of the International Workshop on Wind Tunnel Modeling Criteria and Techniques in Civil Engineering Applications, Gaithersburg, Maryland, USA, April 1982 , 1982 .

[7]  Hojjat Adeli,et al.  A New Approach for Health Monitoring of Structures: Terrestrial Laser Scanning , 2007, Comput. Aided Civ. Infrastructure Eng..

[8]  C. Fraser,et al.  Monitoring the thermal deformation of steel beams via vision metrology , 2000 .

[9]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[10]  Alan G. Davenport,et al.  The base balance technique for the determination of dynamic wind loads , 1983 .

[11]  Zhengnong Li,et al.  Wind tunnel and full-scale study of wind effects on China’s tallest building , 2006 .

[12]  Peter A. Irwin,et al.  Wind engineering challenges of the new generation of super-tall buildings , 2009 .

[13]  E A Harman,et al.  Three-dimensional cinematography with control object of unknown shape. , 1981, Journal of biomechanics.

[14]  Qiusheng Li,et al.  Full-scale measurements of wind effects on the Jin Mao building , 2007 .

[15]  K. Worden,et al.  Data processing and experiment design for the restoring force surface method, part I: integration and differentiation of measured time data , 1990 .

[16]  Jack E. Cermak,et al.  Wind-tunnel development and trends in applications to civil engineering , 2003 .

[17]  Jörg Müller,et al.  Modelling Forest α-Diversity and Floristic Composition - On the Added Value of LiDAR plus Hyperspectral Remote Sensing , 2012, Remote. Sens..

[18]  Hyo Seon Park,et al.  A wireless vibrating wire sensor node for continuous structural health monitoring , 2010 .

[19]  Jörg M. Hacker,et al.  Demonstration of Two Portable Scanning LiDAR Systems Flown at Low-Altitude for Investigating Coastal Sea Surface Topography , 2011, Remote. Sens..

[20]  N. Isyumov,et al.  Wind tunnel studies of buildings and structures , 1998 .

[21]  Norbert Pfeifer,et al.  Landslide Displacement Monitoring Using 3D Range Flow on Airborne and Terrestrial LiDAR Data , 2013, Remote. Sens..

[22]  Jack E. Cermak.  Wind-Tunnel Testing of Structures , 1977 .

[23]  Jong-Jae Lee,et al.  A vision-based system for remote sensing of bridge displacement , 2006 .

[24]  Palle Andersen,et al.  Modal Identification from Ambient Responses using Frequency Domain Decomposition , 2000 .

[25]  Siu-Kui Au,et al.  Full-Scale Validation of Dynamic Wind Load on a Super-Tall Building under Strong Wind , 2012 .

[26]  N. Null.  Wind Tunnel Studies of Buildings and Structures , 1996 .

[27]  Gianni Bartoli,et al.  Wind action on regular and irregular tall buildings: Higher order moment statistical analysis by HFFB and SMPSS measurements , 2011 .

[28]  S. Fioretti,et al.  Numerical differentiation in movement analysis: how to standardise the evaluation of techniques , 1993, Medical and Biological Engineering and Computing.

[29]  Ki-Tae Park,et al.  The determination of bridge displacement using measured acceleration , 2005 .

[30]  Ahsan Kareem,et al.  Wind load identification using wind tunnel test data by inverse analysis , 2011 .

[31]  D. W. Boggs,et al.  Validation of the aerodynamic model method , 1992 .

[32]  Steven Lang,et al.  LIDAR and SODAR Measurements of Wind Speed and Direction in Upland Terrain for Wind Energy Purposes , 2011, Remote. Sens..

[33]  Juha Hyyppä,et al.  Automatic Detection of Buildings and Changes in Buildings for Updating of Maps , 2010, Remote. Sens..

[34]  Luis Ángel Ruiz Fernández,et al.  Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data , 2011, Remote. Sens..