Structural Displacement Measurement Using an Unmanned Aerial System

Abstract

Vibration‐based Structural Health Monitoring (SHM) is one of the most popular solutions to assess the safety of civil infrastructure. SHM applications all begin with measuring the dynamic response of structures, but displacement measurement has been limited by the difficulty in requiring a fixed reference point, high cost, and/or low accuracy. Recently, researchers have conducted studies on vision‐based structural health monitoring, which provides noncontact and efficient measurement. However, these approaches have been limited to stationary cameras, which have the challenge of finding a location to deploy the cameras with appropriate line‐of‐sight, especially to monitor critical civil infrastructures such as bridges. The Unmanned Aerial System (UAS) can potentially overcome the limitation of finding optimal locations to deploy the camera, but existing vision‐based displacement measurement methods rely on the assumption that the camera is stationary. The displacements obtained by such methods will be a relative displacement of a structure to the camera motion, not an absolute displacement. Therefore, this article presents a framework to achieve absolute displacement of a structure from a video taken from an UAS using the following phased approach. First, a target‐free method is implemented to extract the relative structural displacement from the video. Next, the 6 degree‐of‐freedom camera motion (three translations and three rotations) is estimated by tracking the background feature points. Finally, the absolute structural displacement is recovered by combining the relative structural displacement and the camera motion. The performance of the proposed system has been validated in the laboratory using a commercial UAS. Displacement of a pinned‐connected railroad truss bridge in Rockford, IL subjected to revenue‐service traffic loading was reproduced on a hydraulic simulator, while the UAS was flown from a distance of 4.6 m (simulating the track clearance required by the Federal Railroad Administration), resulting in estimated displacements with an RMS error of 2.14 mm.

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

“…The study used a remotely operated aerial vehicle for displacement measurement of a massive floating membrane spanning over 170 × 420 m in a wastewater treatment plant. Although the UAV system only provided structural displacement relative to the position of the camera, Yoon and Spencer provided a framework to obtain the absolute displacement of the structure by combining the relative structural displacement and camera motion.…”

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.…”

“…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, 2017a;Park et al, 2018;Yoon et al, 2018), three-axes (i.e., X-axis, Y-axis, and * To whom correspondence should be addressed. E-mail: mfeng@ columbia.edu.…”

Pixel-level crack delineation in images with convolutional feature fusion

“…With the rapid adoption of low‐cost unmanned aerial vehicles (UAVs or commonly called drones) in many industrial sectors, Yoon, Hoskere, Park, and Jr proposed a method for structural displacement measurement using UAV video data, which provides the estimates of structural displacement subject to an appropriate scale factor. Yoon, Shin, and Jr further presented a framework that estimates the absolute displacement of a structure from a UAV video and validated the framework in the laboratory. One direct outcome of such low‐cost deployment of UAVs or other robotic vehicles for non‐contact SHM is the proliferation of large‐scale visual data, which demands nontraditional data interpretation methods beyond the domain of traditional system identification and damage detection methods.…”