Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures

Abstract

Detecting damage in structures based on the change in their dynamics or modal parameters (modal frequencies and mode shapes) has been extensively studied for three decades. The success of such a global, passive, vibration-based method in field applications, however, has long been hindered by the bottleneck of low spatial resolution vibration sensor measurements. The primary reason is that damage typically initiates and develops in local regions that need to be captured and characterized by very high spatial resolution vibration measurements and modal parameters (mode shapes), which are extremely difficult to obtain using traditional vibration measurement techniques. For example, accelerometers and strain-gauge sensors are typically placed at a limited number of discrete locations, providing low spatial resolution vibration measurements. Laser vibrometers provide high-resolution measurements, but are expensive and make sequential measurements that are time- and labor-consuming. Recently, digital video cameras—which are relatively low cost, agile, and able to provide high spatial resolution, simultaneous, pixel measurements—have emerged as a promising tool to achieve full-field, high spatial resolution vibration measurements. Combined with advanced vision processing and unsupervised machine algorithms, a new method has recently been developed to blindly and efficiently extract the full-field, high-resolution, dynamic parameters from the video measurements of an operating, output-only structure. This work studies the feasibility of performing damage detection using the full-field, very high spatial resolution mode shape (of the fundamental mode) blindly extracted from the video of the operating (output-only) structure without any knowledge of reference (healthy) structural information. A spatial fractal dimension analysis is applied on the full-field mode shape of the damaged structure to detect damage-induced irregularity. Additionally, the equivalence between the fractal dimension and the squared curvature (modal strain energy) of the mode shape curve, when of high spatial resolution, is mathematically derived. Laboratory experiments are conducted on bench-scale structures, including a building structure and a cantilever beam, to validate the approach. The results illustrate that using the full-field, very high-resolution mode shape enables detection of minute, non-visible, damage in a global, completely passive sensing manner, which was previously not possible to achieve.

Estimation of full‐field dynamic strains from digital video measurements of output‐only beam structures by video motion processing and modal superposition

“…However, many of these methods typically rely on installing and tracking the optical markers or speckle paints on the structure's surface, which becomes less practical for large measurement area or in harsh environment (high temperature and corrosion). In addition, they are computationally extremely extensive …”

“…[39][40][41] Lately, phase-based video motion processing technique [42][43][44] has been explored to perform structural dynamics response (displacement) measurements and output-only modal identification without the need for installing markers or speckle paints on a structure's surface, [45][46][47][48][49][50][51] as required by traditional photogrammetry techniques. The recently developed technique enables extraction and visualization of full-field structural dynamic response and modal parameters at high spatial (pixel) resolution locations in an automated manner, 41,51 even with temporally aliased (sub-Nyquist) video measurements avoiding the need of using high-speed digital video cameras. 51 Ideally, full-field strains can then be directly computed from numerical differentiation of the estimated full-field displacements.…”

Surface damage quantification of postearthquake building based on terrestrial laser scan data

“…Image‐based evaluation methods, such as photographic and video imaging methods, have also been proposed for surface damage assessment . Most image‐based methods adopt two‐dimensional viewpoint, which is a critical limitation for crack depth quantification and restricts the collection of full spatial data.…”

“…Image-based evaluation methods, such as photographic and video imaging methods, have also been proposed for surface damage assessment. [20][21][22][23][24][25] Most image-based methods adopt two-dimensional viewpoint, which is a critical limitation for crack depth quantification and restricts the collection of full spatial data. Although multidimensional digital imaging systems, such as the stereo camera, can be used to collect crack depth information, they are much more complicated for postprocessing than a single unit sensor, such as LiDAR.…”

Damage localization and quantification in gusset plates: A battery-free sensing approach

“…Dynamic vision sensors were developed to detect damage using fullfield, high spatial resolution model shape extracted from videos of operating structures. [9,10] Yang et al [9] and Roeder et al [10] conducted spatial fractal dimension analysis on the full-field mode shape of damaged structures to detect damage-induced irregularity. Arguably, vison-based sensing technology is essentially based on processing and analyzing huge amount of collected images captured by cameras.…”