Showing papers by "Charles R. Farrar published in 2019"

Robust structural health monitoring under environmental and operational uncertainty with switching state-space autoregressive models:

Abstract: Existing methods for structural health monitoring are limited due to their sensitivity to changes in environmental and operational conditions, which can obscure the indications of damage by introdu...

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

Abstract: 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.

Applying Concepts of Complexity to Structural Health Monitoring

Abstract: The process of implementing a damage detection strategy for aerospace, civil, and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). The SHM method complements traditional nondestructive evaluation by extending these concepts to online, in situ, system monitoring on a more global scale. For long term SHM, the output is periodically updated information that provides details on the continual deterioration of a system. After severe events, SHM is used for short term rapid condition screening and aims to provide reliable, near real-time information on structural integrity. The hypothesis of this paper is that structural degradation increases the complexity of a system, and that SHM can be used to detect this change over both long and short-term periods. Various measures of complexity were investigated, including Shannon and spectral entropies of accelerometer readings for real time damage detection and gradient measures for image-based corrosion detection. It was concluded that different measures of complexity were more appropriate for varying types of damage, i.e. spectral entropy was more appropriate for identifying cracks in a structure, while Shannon entropy was more appropriate for identifying corrosion on a plate.

YAP/TAZ Related BioMechano Signal Transduction and Cancer Metastasis.

Abstract: Mechanoreciprocity refers to a cell's ability to maintain tensional homeostasis in response to various types of forces. Physical forces are continually being exerted upon cells of various tissue types, even those considered static, such as the brain. Through mechanoreceptors, cells sense and subsequently respond to these stimuli. These forces and their respective cellular responses are prevalent in regulating everything from embryogenic tissue-specific differentiation, programmed cell death, and disease progression, the last of which being the subject of extensive attention. Abnormal mechanical remodeling of cells can provide clues as to the pathological status of tissues. This becomes particularly important in cancer cells, where cellular stiffness has been recently accepted as a novel biomarker for cancer metastasis. Several studies have also elucidated the importance of cell stiffness in cancer metastasis, with data highlighting that a reversal of tumor stiffness has the capacity to revert the metastatic properties of cancer. In this review, we summarize our current understanding of extracellular matrix (ECM) homeostasis, which plays a prominent role in tissue mechanics. We also describe pathological disruption of the ECM, and the subsequent implications toward cancer and cancer metastasis. In addition, we highlight the most novel approaches toward understanding the mechanisms which generate pathogenic cell stiffness and provide potential new strategies which have the capacity to advance our understanding of one of human-kinds' most clinically significant medical pathologies. These new strategies include video-based techniques for structural dynamics, which have shown great potential for identifying full-field, high-resolution modal properties, in this case, as a novel application.

Light Field Imaging of Three-Dimensional Structural Dynamics

Abstract: Real world structures, such as bridges and skyscrapers, are often subjected to dynamic loading and changing environments. It seems prudent to measure high resolution vibration data, in order to perform accurate damage detection and to validate and update the models and knowledge about the operating structure (aka finite element models). Many existing vibration measurement methods could be either low resolution (e.g., accelerometers or strain gauges), and time and labor consuming to deploy in field (e.g., laser interferometry). Previous work by Yang et al. has shown that low-cost regular digital video cameras enhanced by advanced computer vision and machine learning algorithms can extract very high resolution dynamic information about the structure and perform damage detection at novel scales in an relatively efficient and unsupervised manner. More interestingly this work used a machine learning pipeline that made minimal assumptions about lighting conditions or the nature of the structure in order to perform modal decomposition. The technique is currently limited to two dimensions if only one digital video camera is used. This paper uses light field imagers - a new camera system that captures the direction light entered the camera - to make depth measurements of scenes and extend the modal analysis technique proposed in Yang et al. to three dimensions. The new method is verified experimentally on vibrating cantilever beams with out of plane vibration, whose full-field modal parameters are extracted from the light field measurements. The experimental results are discussed and some limitations are pointed out for future work.

Experimental Modal Analysis of Tumorigenesis and Cancer Metastasis

Abstract: Traditionally, performing an experimental modal analysis of a building/structure required instrumenting the structure with a spatially distributed array of accelerometers or strain gages. Alternatively, a laser doppler vibrometer would have to be scanned across the structure of interest in a sequential manner to measure structural response. Recently, researchers at LANL developed a technology that combines the theory of structural dynamics with computer vision that provides the capability to characterize structural dynamics at very high spatial density using only an imager. With this newfound success at the macro-scale, we have exploited this novel technology to a whole new scale- to studying the basic structure of life itself, the human cell. We hypothesize that this new technology and novel application will provide a significantly better understanding of how stiffness and mass distribution changes in a cell as it undergoes epithelial-mesenchymal transition, and in identifying its associated EMC biochemical cues, highlight potential therapeutic targets. For the first time it should be possible to measure the high-resolution mode shapes of cells; given that all cells undergoing cancer metastasis experience a breakdown in the cytoskeleton, this work will enable groundbreaking advances in various fields including medicine and structural dynamics. It is imperative to highlight, that we are only beginning to understand the relationship between biophysical properties of cells and their potential to regulate tumorigenesis and motility, which is commonly known as metastasis. This knowledge could be used to provide verification and validation of finite element models of cellular structure. This work will represent the first time that expertise in experimental structural dynamics will be brought to bear on the problem of characterizing the structural dynamics of cells at high spatial resolution, which is novel and unique on its own. When successful, this new technology could be used to couple the biophysical cues associated with other detrimental human pathologies.

Full-Field Mode Shape Identification of Vibrating Structures from Compressively Sampled Video

Abstract: Video-based techniques for structural dynamics have shown great potential for identifying full-field, high-resolution modal properties. One significant advantage of these techniques is that they lend themselves to being applied to structures at very small length scales such as MEMS devices and living cells. These small structures typically will have resonant frequencies greater than 1 Khz, thus requiring the use of high-speed photography to capture their dynamics without aliasing. High speed photography generally requires the structure-under-test (e.g. living cell) to be exposed to high levels of illumination. It is well-known that exposing delicate structures such as living cells to these high levels of light energy can result in damage to their structural integrity. It is therefore desirable to develop techniques to minimize the amount of illumination that is required to capture the modal properties of interest. This is particularly important given that the mechanical properties of living cells have recently been found to be of interest to the biomedical community. For example, it is known that changes in cell stiffness are correlated with grade of metastasis in cancer cells. Compressive sensing techniques could help mitigate this problem, particularly in fluorescence microscopy applications where cells are illuminated using a laser light source. Compressive sampling would allow for the cells to be exposed to the laser light with a significantly lower duty cycle, thus resulting in less damage to the cells. As a result the structural dynamics of the cells can be measured at increasingly high frequencies yielding new information about cellular material properties that can be coupled with biochemical cues to yield new therapeutic strategies. Furthermore, video-based techniques would benefit from the reductions in memory, bandwidth and computational requirements normally associated with compressive sampling. In this work we present a technique that intimately combines solutions to the blind-source separation problem for video-based, high-resolution operational modal analysis with compressive sampling.