Measurement of Three-Dimensional Structural Displacement Using a Hybrid Inertial Vision-Based System

Accurate three-dimensional displacement measurements of bridges and other structures have received significant attention in recent years. The main challenges of such measurements include the cost and the need for a scalable array of instrumentation. This paper presents a novel Hybrid Inertial Vision-Based Displacement Measurement (HIVBDM) system that can measure three-dimensional structural displacements by using a monocular charge-coupled device (CCD) camera, a stationary calibration target, and an attached tilt sensor. The HIVBDM system does not require the camera to be stationary during the measurements, while the camera movements, i.e., rotations and translations, during the measurement process are compensated by using a stationary calibration target in the field of view (FOV) of the camera. An attached tilt sensor is further used to refine the camera movement compensation, and better infers the global three-dimensional structural displacements. This HIVBDM system is evaluated on both short-term and long-term synthetic static structural displacements, which are conducted in an indoor simulated experimental environment. In the experiments, at a 9.75 m operating distance between the monitoring camera and the structure that is being monitored, the proposed HIVBDM system achieves an average of 1.440 mm Root Mean Square Error (RMSE) on the in-plane structural translations and an average of 2.904 mm RMSE on the out-of-plane structural translations.

[1]  Mi Lu,et al.  An Attention-aware Bidirectional Multi-residual Recurrent Neural Network (Abmrnn): A Study about Better Short-term Text Classification , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Jie Tan,et al.  Non-Contact Measurement of the Surface Displacement of a Slope Based on a Smart Binocular Vision System , 2018, Sensors.

[3]  T. C. Chu,et al.  Three-dimensional displacement measurements using digital image correlation and photogrammic analysis , 1990 .

[4]  Feng Xiao,et al.  Monitoring Bridge Dynamic Responses Using Fiber Bragg Grating Tiltmeters , 2017, Sensors.

[5]  Angel Lozano,et al.  Structural Health Monitoring in Composite Structures by Fiber-Optic Sensors † , 2018, Sensors.

[6]  Victor Giurgiutiu,et al.  Recent Advances in Piezoelectric Wafer Active Sensors for Structural Health Monitoring Applications , 2019, Sensors.

[7]  Gotzon Aldabaldetreku,et al.  Optical Fiber Sensors for Aircraft Structural Health Monitoring , 2015, Sensors.

[8]  Maria Q. Feng,et al.  Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review , 2018 .

[9]  Frédo Durand,et al.  Video Camera–Based Vibration Measurement for Civil Infrastructure Applications , 2017 .

[10]  Hao Wu,et al.  Accurate Vehicle Detection Using Multi-camera Data Fusion and Machine Learning , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Billie F. Spencer,et al.  Sensor Attitude Correction of Wireless Sensor Network for Acceleration-Based Monitoring of Civil Structures , 2015, Comput. Aided Civ. Infrastructure Eng..

[12]  James M. W. Brownjohn,et al.  Review of machine-vision based methodologies for displacement measurement in civil structures , 2018 .

[13]  Paolo Bettini,et al.  Development and Experimental Validation of a Numerical Tool for Structural Health and Usage Monitoring Systems Based on Chirped Grating Sensors , 2015, Sensors.

[14]  Stefan Hurlebaus,et al.  Summary Review of GPS Technology for Structural Health Monitoring , 2013 .

[15]  Nasim Uddin,et al.  Drive-by bridge damage monitoring using Bridge Displacement Profile Difference , 2016 .

[16]  Bing Pan,et al.  Single-camera high-speed stereo-digital image correlation for full-field vibration measurement , 2017 .

[17]  Eugene J. O'Brien,et al.  Drive-by scour monitoring of railway bridges using a wavelet-based approach , 2019, Engineering Structures.

[18]  Mi Lu,et al.  Comparisons and Selections of Features and Classifiers for Short Text Classification , 2017 .

[19]  Billie F. Spencer,et al.  Visual–inertial displacement sensing using data fusion of vision‐based displacement with acceleration , 2018 .

[20]  Peter Avitabile,et al.  Photogrammetry and optical methods in structural dynamics – A review , 2017 .

[21]  Uvais Qidwai,et al.  3D dynamic displacement-field measurement for structural health monitoring using inexpensive RGB-D based sensor , 2017 .

[22]  Yamin Li,et al.  Accurate Structural Dynamic Response Monitoring of Multiple Structures using One CCD Camera and a Novel Targets Configuration , 2017 .

[23]  Hui Li,et al.  Computer vision and deep learning–based data anomaly detection method for structural health monitoring , 2019 .

[24]  Eugene J. O'Brien,et al.  A mode shape‐based damage detection approach using laser measurement from a vehicle crossing a simply supported bridge , 2016 .

[25]  K. J. Vinoy,et al.  Structural Health Monitoring Applications , 2006 .

[26]  Mi Lu,et al.  An optimized system to solve text-based CAPTCHA , 2018, ArXiv.

[27]  Vikram Pakrashi,et al.  Scour Damage Detection and Structural Health Monitoring of a Laboratory-Scaled Bridge Using a Vibration Energy Harvesting Device , 2019, Sensors.

[28]  Ivan Roselli,et al.  Motion Magnification Analysis for structural monitoring of ancient constructions , 2018, Measurement.

[29]  B. F. Spencer,et al.  Structural Displacement Measurement Using an Unmanned Aerial System , 2018, Comput. Aided Civ. Infrastructure Eng..

[30]  F. Necati Catbas,et al.  Completely contactless structural health monitoring of real‐life structures using cameras and computer vision , 2017 .

[31]  X. W. Ye,et al.  Identification of structural dynamic characteristics based on machine vision technology , 2017, Measurement.

[32]  Mi Lu,et al.  A self-adaptive algorithm to defeat text-based CAPTCHA , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

[33]  Andreas Geiger,et al.  Automatic camera and range sensor calibration using a single shot , 2012, 2012 IEEE International Conference on Robotics and Automation.

[34]  S. P. Mudur,et al.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[35]  Maria Q. Feng,et al.  Vision‐based multipoint displacement measurement for structural health monitoring , 2016 .

[36]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

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

[38]  Gang Liu,et al.  A Noncontact FMCW Radar Sensor for Displacement Measurement in Structural Health Monitoring , 2015, Sensors.

[39]  Bernhard Wilhelm Roth,et al.  Structural Health Monitoring Using Textile Reinforcement Structures with Integrated Optical Fiber Sensors , 2017, Sensors.

[40]  Jongbin Won,et al.  Non-Target Structural Displacement Measurement Using Reference Frame-Based Deepflow , 2019, Sensors.

[41]  Dinesh Rajan,et al.  Concrete crack detection using context‐aware deep semantic segmentation network , 2019, Comput. Aided Civ. Infrastructure Eng..

[42]  Jean Michel Franco,et al.  Static and dynamic displacement measurements of structural elements using low cost RGB-D cameras , 2017 .

[43]  William Greenwood,et al.  Applications of UAVs in Civil Infrastructure , 2019, Journal of Infrastructure Systems.

[44]  Sung-Han Sim,et al.  Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges , 2017, Sensors.

[45]  F. Necati Catbas,et al.  Structural Identification Using Computer Vision–Based Bridge Health Monitoring , 2018 .

[46]  Billie F. Spencer,et al.  Vision-Based Modal Survey of Civil Infrastructure Using Unmanned Aerial Vehicles , 2019, Journal of Structural Engineering.

[47]  Maria Q. Feng,et al.  A Vision-Based Sensor for Noncontact Structural Displacement Measurement , 2015, Sensors.

[48]  Yue Zhang,et al.  Effective real-scenario Video Copy Detection , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[49]  F. Necati Catbas,et al.  Computer vision-based displacement and vibration monitoring without using physical target on structures , 2017, Bridge Design, Assessment and Monitoring.

[50]  Mi Lu,et al.  Combining convolutional neural network and self-adaptive algorithm to defeat synthetic multi-digit text-based CAPTCHA , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[51]  Eugene J. O'Brien,et al.  A Review of Indirect Bridge Monitoring Using Passing Vehicles , 2015 .

[52]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[53]  Hong Hao,et al.  Target-free vision-based technique for vibration measurements of structures subjected to out-of-plane movements , 2019, Engineering Structures.

[54]  Jerome P. Lynch,et al.  Structural health monitoring: technological advances to practical implementations [scanning the issue] , 2016, Proc. IEEE.

[55]  B. F. Spencer,et al.  Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles , 2017, Sensors.

[56]  Dongming Feng,et al.  Investigation of dynamic properties of long‐span cable‐stayed bridges based on one‐year monitoring data under normal operating condition , 2018 .

[57]  Janko Slavič,et al.  Frequency domain triangulation for full-field 3D operating-deflection-shape identification , 2019, Mechanical Systems and Signal Processing.

[58]  Nasim Uddin,et al.  Drive-By Bridge Frequency Identification under Operational Roadway Speeds Employing Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR) , 2018, Sensors.