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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.

This study investigates experimentally and numerically the response of a magnetorheological elastomer (MRE) layer placed atop an electromagnetic coil. The MRE layer is deflected upon application of a current in the coil, which creates highly non-uniform magnetic fields. Isotropic as well as transversely isotropic layers (i.e., containing chains of magnetic particles) are tested experimentally, and the isotropic layer exhibits the largest deflection. To enhance the energetic efficiency of the model device, an iron core is introduced inside the electromagnetic coil, thereby leading to an increase of the resulting magnetic field near the center of the MRE layer. In parallel, the boundary value problem (BVP)–including the MRE layer, the coil, the core and the surrounding air–is modeled numerically. For this, a magneto-mechanical, vector-potential-based variational formulation is implemented in a standard three-dimensional finite element model at finite strains. For the material description, a recently proposed analytical homogenization-guided model is used to analyze the MRE in the “coil-only” configuration. It is then employed to predict the response of the layer in the “coil plus core” configuration, thus circumventing the need for a separate material characterization procedure. The proposed numerical simulation strategy provides a deeper understanding of the underlying complexity of the magnetic fields and of their interaction with the MRE layer. This study also reveals the importance of modeling the entire setup for predicting the response of MRE materials and, as a result, constitutes a step towards designing more efficient MRE-based devices.

Summary Strain is an essential quantity to characterize local structural behaviors and directly correlates with structural damage initiation and development that is within local regions. Strain measurement at high spatial resolution (density) locations is thus required to characterize local structural behaviors and detect potential local damage. Traditional contact‐type strain gauges are mostly discrete point‐wise sensors that can only be placed in a limited number of positions. Distributed optical fiber sensing techniques can measure strains at spatially dense measurement points, but their instrumentation is a time‐ and labor‐intensive process associated with the issue of the fragility of fibers. Noncontact optical measurement techniques, such as a family of interferometry techniques using laser beams (e.g., laser Doppler vibrometers), can provide vibration measurement at high density spatial points without the need to install sensors on the structure. However, these measurement devices are active sensing methods that are relatively expensive and vulnerable to ambient motion. Photogrammetry is an alternative noncontact optical measurement method using (passive) white‐light imaging of digital video cameras that are relatively low‐cost, agile, and provides simultaneous measurements at high spatial density locations where every pixel becomes a measurement point. Among others, digital image correlation can achieve full‐field deformation measurements and subsequently estimate the full‐field strains. However, it is computationally extensive. This study develops a new efficient approach to estimate the full‐field (as many measurement points as the pixel number of the video frame on the structure) dynamic strains at high‐spatial (pixel)‐resolution/density location points from the digital video measurement of output‐only vibrating structures. The developed approach is based on phase‐based video motion estimation and modal superposition of structural dynamic response. Furthermore, the method is augmented by a high‐fidelity finite element model, which is updated with the full‐field experimental modal parameters “blindly” identified from the video measurement of the output‐only structure. Laboratory experiments are conducted to validate the method on a bench‐scale cantilever beam structure. Results demonstrate that the full‐field dynamic strain estimated by the developed approach from the video measurement of the output‐only vibrating beam match very well those directly measured by the strain gauges (at discrete measurement points). Some factors associated with the effectiveness of the method are experimentally studied and discussed.

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Recent developments in the ability to automatically and efficiently extract natural frequencies, damping ratios, and full-field mode shapes from video of vibrating structures has great potential for reducing the resources and time required for performing experimental and operational modal analysis at very high spatial resolution. Furthermore, these techniques have the added advantage that they can be implemented remotely and in a non-contact fashion. Emerging full-field imaging techniques therefore have potential to allow the identification of the modal properties of structures in regimes that used to be challenging. For instance, these techniques suggest that the high spatial resolution structural identification could be performed on an aircraft during flight using a ground or aircraft-based imager. They also have the potential to identify the dynamics of microscopic systems. In order to realize this capability it will be necessary to develop techniques that can extract full-field structural dynamics in the presence of non-ideal operating conditions. In this work, we develop a framework for the deployment of emerging algorithms that allow the automatic extraction of high-resolution, full-field modal parameters in the presence of non-ideal operating conditions. One of the most notable non-ideal operating conditions is the rigid body motion of both the structure being measured as well as the imager performing the measurement. We demonstrate an instantiation of the framework by showing how it can be used to address, in-plane, translational, rigid body motion. The development of a frame-to-frame keypoint–based technique for identifying full-field structural dynamics in the presence of either rigid body motion is presented and demonstrated in the context of the framework for the deployment of full-field structural identification techniques in the presence of non-ideal operating conditions. It is expected that this framework will ultimately help enable the collection of full-field structural dynamics using measurement platforms including unmanned aerial vehicles, robotic telescopes, satellites, imagers mounted in high-vibration environments (seismic, industrial, harsh weather), characterization of microscopic structures, and human-carried imagers. If imager-based structural identification techniques mature to the point that they can be used in non-ideal field conditions, it could open up the possibility that the structural health monitoring community will be able to think beyond monitoring individual structures, to full-field structural integrity monitoring at the city scale.