3D dynamic displacement-field measurement for structural health monitoring using inexpensive RGB-D based sensor

Abstract

The advent of inexpensive digital cameras with depth sensing capabilities (RGB-D cameras) has opened the door to numerous useful applications that need quantitative measures of dynamic fields whose simultaneous time history quantification (at many points as dictated by the resolution of the camera) provides capabilities that were previously accessible only through expensive sensors (e.g., laser scanners). This paper presents a comprehensive experimental and computational study to evaluate the performance envelope of a representative RGB-D sensor (the first generation of Kinect sensor) with the aim of assessing its suitability for the class of problems encountered in the structural dynamics field, where reasonably accurate information of evolving displacement fields (as opposed to few discrete locations) that have simultaneous dynamic planar translational motion with significant rotational (torsional) components. This study investigated the influence of key system parameters of concern in selecting an appropriate sensor for such structural dynamic applications, such as amplitude range, spectral content of the dynamic displacements, location and orientation of sensors relative to target structure, fusing of measurements from multiple sensors, sensor noise effects, rolling-shutter effects, etc. The calibration results show that if the observed displacement field generates discrete (pixel) sensor measurements with sufficient resolution (observed displacements more than 10 mm) beyond the sensor noise floor, then the subject sensors can typically provide reasonable accuracy for transnational motion (about 5%) when the frequency range of the evolving field is within about 10 Hz. However, the expected error for torsional measurements is around 6% for static motion and 10% for dynamic rotation for measurements greater than 5°.

A literature review of next-generation smart sensing technology in structural health monitoring

“…In a different study, the effect of body motion of various subjects in footbridges was studied using cameras . In another recent study, Abdelbarr et al investigated the use of RGB‐D sensors for 3D displacement field measurement of flexible structures under dynamic loads. The research studied the advanced use of Kinect sensor for 3D translation motion along with rotational and torsional component.…”

3D reconstruction of existing concrete bridges using optical methods

“…Franco et al (2017) used two RGB-D cameras (Kinect V1 and Kinect V2) to measure displacements during experimental tests of structural elements, achieving mean displacement errors of 3.4% and 7.9% for static and dynamic displacements, respectively. Displacement fields under dynamic loads were measured using an RGB-D camera (Kinect V1) by Abdelbarr et al (2017). The tested sensor was found to be a low-cost solution capable of monitoring multi-component displacement with an error of less than 5% for displacements larger than 10 mm.…”

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

“…Vision sensors, among these new techniques, have been broadly applied for civil engineering problems. Famous applications of vision‐sensing techniques include dynamic displacement monitoring (Cha et al., ; Park et al., ; Yoon et al., ), three‐axes (i.e., X‐axis, Y‐axis, and depth) displacement measurement (Park et al., ; Abdelbarr et al., ), surface displacement/strain measurement (Luo et al., ; Almeida et al., ), vision‐based structural analysis (Chen et al., ; Sharif et al., ; Park et al., ), cable tensile force evaluation (Kim et al., ), bridge‐lining inspection (Zhu et al., ), rocking motion and landslide monitoring (Debella‐Gilo and Kääb, ; Greenbaum et al., ), automatic construction progress assessment (Bügler et al., ), 3D object finding in point cloud (Sharif et al., ), surface crack/defection detection based on texture‐based video processing (Cord and Chambon, ; Chen et al., ) or deep learning (Cha et al., ; Cha et al, ; Zhang et al., ), vehicle classification based on spectrogram features (Yeum et al., ), and intelligent transportation (Chen et al., ; Fernandez‐Llorca et al., ). With advancement in image sensors and computer techniques such as computer vision, cloud computing, and wireless data transfer, vision sensors have become more cost‐effective and computation‐efficient, thus have high potential in field application for SHM problems.…”