Nontarget-Based Displacement Measurement Using Computer Vision and 3-D Point Cloud

Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)The computer vision-based displacement measurement method, which is a noncontact method, has recently drawn significant attention because of its measurement convenience and great performance. However, computer vision-based methods generally require the installation of an artificial target on the structural surface to measure the displacement. Although the artificial target provides clear traceable patterns for robust pattern tracking and metric information for coordinate transformation, target-based methods have practical limitations in field owing to the need for target installation and restrictions on camera positioning. To overcome these limitations, a nontarget-based approach, which uses structural features instead of artificial targets, has been developed. Existing nontarget-based methods still require calibration using artificial targets or the known dimensions of the structure. Moreover, most existing methods are limited to one- or two-degrees of freedom (DOF) in displacement measurement, which limits the reliability of structural health monitoring (SHM) for structures requiring out-of-plane displacement measurement. This paper proposes nontarget-based 6-DOF and in-plane structural displacement measurement approaches using a three-dimensional (3-D) point cloud. The 6-DOF displacement measurement method utilizes the combined red, green, and blue (RGB) and depth (D) information obtained using an RGB-D camera. This method provides 3-D information of the structure and suggests a coordinate transform scheme to estimate the physical 6-DOF displacement without using a target. The proposed method is validated in two laboratory experiments using a shear building model and a pan-tilt with a predefined measurement plate, in which both translational and rotational displacements are measured. In the proposed nontarget-based, in-plane displacement measurement method, the laser scanning 3-D point cloud combined with high-resolution RGB information of structure is used to determine the precise physical displacement tailored to the field measurement. The proposed method also utilizes light detection and ranging (LiDAR) and a camera that provides the combined 3-D point cloud and RGB information, presenting the relationship between the image and physical displacement for the coordinate transform. The proposed method is validated in a laboratory-scale experiment using a concrete specimen and a field test with a full-scale bridge.clos

Nontarget-Based Measurement of 6-DOF Structural Displacement Using Combined RGB Color and Depth Information

Structural displacement is an important physical quantity that provides essential information regarding the structural conditions and the safety of a structure. To date, a wide variety of displacement measurement methods have been developed. As a noncontact type of measurement, the computer-vision-based approach receives significant attention for its cost effectiveness, measurement convenience, and great performance. However, most existing methods are limited to one- or two-degrees-of-freedom (DOF) displacement measurement and require known dimensions of structural members or predefined target markers attached to the target structure, which limits real-world application. This article proposes a nontarget-based 6-DOF displacement measurement method using the combined RGB and depth information. The proposed method utilizes three-dimensional information of measurement points on the target structure obtained using the RGB-D camera, and suggests a coordinate transform scheme to determine the 6-DOF displacement in the physical domain. The proposed method is validated in two laboratory experiments using a shear building model and a pan-tilt with a measurement plate, in which both translational and rotational displacements are measured

Long-term displacement measurement of bridges using a LiDAR system

Long-term displacement measurements provide important information about the structural safety of civil engineering structures and the associated maintenance necessary. Most approaches developed for measuring displacement, such as direct measurements using the linear variable differential transformer and computer vision-based sensing, relate to campaign-type sensor deployment. However, these methods have critical limitations when used to accurately measure long-term displacement, because it is difficult in practice to appropriately handle unavoidable sensor movement. This study therefore proposes a long-term displacement measurement method using light detection and ranging (LiDAR) that is designed for use with short- and medium-span bridge structures. Reflectors that have higher reflectance than common construction materials (such as concrete and steel) are used to enhance measurement accuracy and reduce scanning time. Strategically deployed reflectors provide a reference point, which is independent of the LiDAR position. Thus, LiDAR can be temporarily installed in the field only when measurements are necessary. Expensive LiDAR systems can be employed to measure the long-term displacement of multiple bridges cost-effectively because permanent installation is not required. The proposed method is validated within the laboratory by focusing on LiDAR position independence and is then used during the early erection stages of a railroad bridge to measure long-term displacement

Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion

Cracks on concrete structures are an important indicator for assessing concrete durability and structural safety. Although such cracks are typically monitored by manual visual inspection, this method has drawbacks in terms of inspection time, safety, cost-effectiveness, and measurement accuracy. An innovative alternative is digital image processing, which can be used to obtain crack information from images captured using a digital camera. However, in image-based crack detection, the crack width may vary depending on the angle of the camera with respect to the concrete surface. A skewed angle of view is often encountered, particularly when capturing images from unmanned aerial vehicles or from higher locations. This study proposes a crack identification strategy using a combination of RGB-D and high-resolution digital cameras to accurately measure cracks regardless of the angle of view. The camera system is equipped with a tailored sensor fusion algorithm for crack identification, enabling a high measurement resolution and a robust depth estimation considering the skewed angle problem. An approximate plane corresponding to the concrete surface is introduced to effectively handle the high noise in the depth measurement data of the RGB-D camera. Subsequently, the crack image captured using the high-resolution digital camera is mapped onto the obtained plane model, allowing the crack width to be determined using the three-dimensional coordinates of each crack pixel. The measurement accuracy of the proposed approach is experimentally validated on an actual concrete structure

Effect of using polymer on the properties of concrete with recycled ballast aggregate

The bond strength between ballast aggregates and mortar is a critical factor to achieving the required performance in rapid-hardening track technology. To enhance this strength, the use of polymer is suggested in several studies. In this study, the effect on the properties of preplaced concrete using polymer is evaluated. For this purpose, polymer in different quantities is used to find out the optimal polymer content in concrete with different dry-washing levels of ballast aggregate. Moreover, compressive strength and flexural tests, and freezing-thawing tests are conducted to measure the effect of these variables on the properties of concrete