An Overall Deformation Monitoring Method of Structure Based on Tracking Deformation Contour

Abstract

Featured Application: This study aims at the lack of sufficient data supporting structural damage identification, which is a general issue in traditional single-point measurement method. A novel method has been proposed for structural deformation monitoring based on digitalized photogrammetry, with improved efficiency and reduced cost. The method can be applied in the overall deformation monitoring of engineering structures, such as bridges. Furthermore, the method can provide a solid foundation for the estimation of structural health state.Abstract: In structural deformation monitoring, traditional methods are mainly based on the deformation data measured at several individual points. As a result, only the discrete deformation, not the overall one, can be obtained, which hinders the researcher from a better and all-round understanding on the structural behavior. At the same time, the surrounding area around the measuring structure is usually complicated, which notably escalates the difficulty in accessing the deformation data. In dealing with the said issues, a digital image-based method is proposed for the overall structural deformation monitoring, utilizing the image perspective transformation and edge detection. Due to the limitation on camera sites, the lens is usually not orthogonal to the measuring structure. As a result, the obtained image cannot be used to extract the deformation data directly. Thus, the perspective transformation algorithm is used to obtain the orthogonal projection image of the test beam under the condition of inclined photography, which enables the direct extraction of deformation data from the original image. Meanwhile, edge detection operators are used to detect the edge of structure's orthogonal projection image, to further characterize the key feature of structural deformation. Using the operator, the complete deformation data of structural edge are obtained by locating and calibrating the edge pixels. Based on the above, a series of load tests has been carried out using a steel-concrete composite beam to validate the proposed method, with the implementation of traditional dial deformation gauges. It has been found that the extracted edge lines have an obvious sawtooth effect due to the illumination environment. The sawtooth effect makes the extracted edge lines slightly fluctuate around the actual contour of the structure. On this end, the fitting method is applied to minimize the fluctuation and obtain the linear approximation of the actual deflection curve. The deformation data obtained by the proposed method have been compared with the one measured by the dial meters, indicating that the measurement error of the proposed method is less than 5%. However, since the overall deformation data are continuously measured by the proposed method, it can better reflect the overall deformation of the structure, and moreover the structural health state, when compared with the traditional "point" measurements.

Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor

“…xt field-of-vision edge pixel band point group To quantify the acquired characteristic parameters of holography geometric morphology, a fuzzy probability-based edge detection algorithm [32,33] is adopted to extract probability edges of the structure in a complex illumination condition. Moreover, the probability edge can reflect holography geometric morphology of the structure to a significant degree.…”

“…Notably, the movements obtained by applying this algorithm actually represent pixels. The real displacement can be obtained when the distance a pixel represents is known [33]. As shown in Equations (5), characteristic parameters of the holography geometric morphology are aligned between pixel and world coordinate systems on an image plane, so that degrees of freedom (DoF) of diverse pixels in the structure's holography geometric morphology can be acquired along axes x and y, and accordingly converted into actual displacement of the structure in the world coordinate system.…”

“…As shown in Equations (5), characteristic parameters of the holography geometric morphology are aligned between pixel and world coordinate systems on an image plane, so that degrees of freedom (DoF) of diverse pixels in the structure's holography geometric morphology can be acquired along axes x and y, and accordingly converted into actual displacement of the structure in the world coordinate system. To quantify the acquired characteristic parameters of holography geometric morphology, a fuzzy probability-based edge detection algorithm [32,33] is adopted to extract probability edges of the structure in a complex illumination condition. Moreover, the probability edge can reflect holography geometric morphology of the structure to a significant degree.…”

A multiple camera position approach for accurate displacement measurement using computer vision

“…Multi-point displacement measurements for the desired accuracy have been obtained also using a single camera [3,[18][19][20]. In a laboratory environment, accurate full-field static displacements of beams have been obtained with a single camera using a photogrammetric measurement approach [21] and tracking deformation contour [22], a robotic camera system with a single camera [23], and a holographic visual sensor consisting of two cameras [24]. However, the aforementioned studies focused on short-term measurement campaigns where the camera location is fixed in a single position.…”

An Enhanced Detection System of Drill Rod Bending Degree Based on Two-Dimensional Laser

“…Xiao et al [10] realized the real-time measurement of straightness and roundness of spun workpieces during spinning by obtaining real-time images based on machine vision technology. Chu et al [11] proposed a digital image-based method for the overall structural deformation monitoring, utilizing the image perspective transformation and edge detection. Qiu et al [12] reported a novel method based on laser and machine vision to automatically measure tunnel deformation of multiple interest points in real time and effectively compensate for the environment vibration.…”