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Summary: Structures with complex geometries, material properties, and boundary conditions exhibit spatially local dynamic behaviors. A high‐spatial‐resolution model of the structure is thus required for high‐fidelity analysis, assessment, and prediction of the dynamic phenomena of the structure. The traditional approach is to build a highly refined finite element computer model for simulating and analyzing the structural dynamic phenomena based on detailed knowledge and explicit modeling of the structural physics such as geometries, materials properties, and boundary conditions. These physics information of the structure may not be available or accurately modeled in many cases, however. In addition, the simulation on the high‐spatial‐resolution structural model, with a massive number of degrees of freedom and system parameters, is computationally demanding. This study, on a proof‐of‐principle basis, proposes a novel alternative approach for spatiotemporal video‐domain high‐fidelity simulation and realistic visualization of full‐field structural dynamics by an innovative combination of the fundamentals of structural dynamic modeling and the advanced video motion manipulation techniques. Specifically, a low‐modal‐dimensional yet high‐spatial (pixel)‐resolution (as many spatial points as the pixel number on the structure in the video frame) modal model is established in the spatiotemporal video domain with full‐field modal parameters first estimated from line‐of‐sight video measurements of the operating structure. Then in order to simulate new dynamic response of the structure subject to a new force, the force is projected onto each modal domain, and the modal response is computed by solving each individual single‐degree‐of‐freedom system in the modal domain. The simulated modal responses are then synthesized by the full‐field mode shapes using modal superposition to obtain the simulated full‐field structural dynamic response. Finally, the simulated structural dynamic response is embedded into the original video, replacing the original motion of the video, thus generating a new photo‐realistic, physically accurate video that enables a realistic, high‐fidelity visualization/animation of the simulated full‐field vibration of the structure. Laboratory experiments are conducted to validate the proposed method, and the error sources and limitations in practical implementations are also discussed. Compared with high‐fidelity finite element computer model simulations of structural dynamics, the video‐based simulation method removes the need to explicitly model the structure's physics. In addition, the photo‐realistic, physically accurate simulated video provides a realistic visualization/animation of the full‐field structural dynamic response, which was not traditionally available. These features of the proposed method should enable a new alternative to the traditional computer‐aided finite element model simulation for high‐fidelity simulating and realistically visualizing full‐field structural dynamics in a relatively efficient and user‐friendly manner. [ABSTRACT FROM AUTHOR]

Traditional vibration measurement typically requires physically attached sensors, such as accelerometers and strain gauges. However, these discrete-point sensors provide only low spatial resolution vibration measurements, potentially foregoing valuable structural information such as localized damage. Noncontact optical measurement methods such as laser vibrometers can achieve high spatial resolution vibration measurements but only through time-consuming sequential measurements and are not cost-effective. As an alternative to traditional vibration measurement methods, digital video cameras are relatively low-cost, agile, and offer noncontact, simultaneous high spatial resolution measurements where every pixel on the structure becomes a measurement point. However, regular digital video cameras are frame-based where each pixel simultaneously performs temporally uniform (synchronous) measurements containing large amounts of redundant (background) data, which consumes considerable resources for video data measurement, management, and processing. To alleviate such a challenge, this work explores the use of event-based neuromorphic imagers, specifically silicon retinas, an efficient alternative to traditional frame-based video cameras, to perform full-field vibration measurements and operational modal analysis. By imitating biological vision, each silicon retina pixel independently and asynchronously records only intensity change events that contain structural motion information while excluding redundant (background) information. Such an asynchronous event-based data measurement mechanism allows for structural motion to be captured on the microsecond scale in an extremely data-efficient manner, which could benefit real-time vibration measurement and control applications. This study takes the first step toward these applications by formulating an existing video frame-based full-field operational modal analysis technique in the event-based, asynchronous silicon retina measurement framework. Specifically, local phase-based motion extraction and blind source separation are used to automatically and efficiently extract full-field vibration and dynamics parameters from silicon retina measurements. The developed method is validated by laboratory experiments on a bench-scale cantilever beam. [ABSTRACT FROM AUTHOR]

Experimental or operational modal analysis traditionally requires physically-attached wired or wireless sensors for vibration measurement of structures. This instrumentation can result in mass-loading on lightweight structures, and is costly and time-consuming to install and maintain on large civil structures, especially for long-term applications (e.g., structural health monitoring) that require significant maintenance for cabling (wired sensors) or periodic replacement of the energy supply (wireless sensors). Moreover, these sensors are typically placed at a limited number of discrete locations, providing low spatial sensing resolution that is hardly sufficient for modal-based damage localization, or model correlation and updating for larger-scale structures. Non-contact measurement methods such as scanning laser vibrometers provide high-resolution sensing capacity without the mass-loading effect; however, they make sequential measurements that require considerable acquisition time. As an alternative non-contact method, digital video cameras are relatively low-cost, agile, and provide high spatial resolution, simultaneous, measurements. Combined with vision based algorithms (e.g., image correlation, optical flow), video camera based measurements have been successfully used for vibration measurements and subsequent modal analysis, based on techniques such as the digital image correlation (DIC) and the point-tracking. However, they typically require speckle pattern or high-contrast markers to be placed on the surface of structures, which poses challenges when the measurement area is large or inaccessible. This work explores advanced computer vision and video processing algorithms to develop a novel video measurement and vision-based operational (output-only) modal analysis method that alleviate the need of structural surface preparation associated with existing vision-based methods and can be implemented in a relatively efficient and autonomous manner with little user supervision and calibration. First a multi-scale image processing method is applied on the frames of the video of a vibrating structure to extract the local pixel phases that encode local structural vibration, establishing a full-field spatiotemporal motion matrix. Then a high-spatial dimensional, yet low-modal-dimensional, over-complete model is used to represent the extracted full-field motion matrix using modal superposition, which is physically connected and manipulated by a family of unsupervised learning models and techniques, respectively. Thus, the proposed method is able to blindly extract modal frequencies, damping ratios, and full-field (as many points as the pixel number of the video frame) mode shapes from line of sight video measurements of the structure. The method is validated by laboratory experiments on a bench-scale building structure and a cantilever beam. Its ability for output (video measurements)-only identification and visualization of the weakly-excited mode is demonstrated and several issues with its implementation are discussed. [ABSTRACT FROM AUTHOR]

Identifying modal parameters from vibration measurements is an essential step for modal analysis and modeling of structural dynamics. A critical challenge in modal parameter identification is the determination of the physical modes from spurious modes, especially with noisy measurement data. In this study, an approach is presented to enable automated identification of modal parameters by quantifying the spatial features of full-field, high-spatial-resolution response measurements. Specifically, it is derived that the local variances of the physical and spurious mode shapes are drastically distinguishing, especially when the spatial resolution of the response measurement is high (i.e., full-field with dense spatial measurement points). This allows an effective identification of the physical modes from spurious. Experimental studies are conducted on a few structural models and detailed comparisons are performed and discussed between the presented method and existing methods, including parametric and non-parametric. • A new approach for automatically distinguishing spurious and physical modes. • Full-field measurements allow spatial and statistical analysis of mode shapes. • Local variances of physical and spurious mode shapes form well-separated clusters. • Performance is compared to existing methods both numerically and experimentally. [ABSTRACT FROM AUTHOR]

Modal identification or testing of structures consists of two phases, namely, data acquisition and data analysis. Some structures, such as aircrafts, high-speed machines, and plate-like civil structures, have active modes in the high-frequency range when subjected to high-speed or broadband excitation in their operational conditions. In the data acquisition stage, the Shannon–Nyquist sampling theorem indicates that capturing the high-frequency modes (signals) requires uniform high-rate sampling, resulting in sensing too many samples, which potentially impose burdens on the data transfer (especially in wireless platform) and data analysis stage. This paper explores a new-emerging, alternative, signal sampling and analysis technique, compressed sensing, and investigates the feasibility of a new method for output-only modal identification of structures in a non-uniform low-rate random sensing framework based on a combination of compressed sensing (CS) and blind source separation (BSS). Specifically, in the data acquisition stage, CS sensors sample few non-uniform low-rate random measurements of the structural responses signals, which turn out to be sufficient to capture the underlying mode information. Then in the data analysis stage, the proposed method uses the BSS technique, complexity pursuit (CP) recently explored by the authors, to directly decouple the non-uniform low-rate random samples of the structural responses, simultaneously yielding the mode shape matrix as well as the non-uniform low-rate random samples of the modal responses. Finally, CS with ℓ 1 -minimization recovers the uniform high-rate modal response from the CP-decoupled non-uniform low-rate random samples of the modal response, thereby enabling estimation of the frequency and damping ratio. Because CS sensors are currently in laboratory prototypes and not yet commercially available, their functionality—randomly sensing few non-uniform samples—is simulated in this study, which is performed on the examples of a numerical structural model, an experimental bench-scale structural model, and a real-world seismic-excited base-isolated hospital buildings. Results show that the proposed method in the CS framework can identify the modes using non-uniform low-rate random sensing, which is far below what is required by the Nyquist sampling theorem. [ABSTRACT FROM AUTHOR]

Summary: Cables are critical components for a variety of structures such as stay cables and suspenders of cable‐stayed bridges and suspension bridges. When in operational service, they are vulnerable to cumulative fatigue damage induced by dynamic loads (e.g., the cyclic vehicle loads and wind excitation). To accurately analyze and predict their dynamics behaviors and performance that could be spatially local and temporal transient, it is essential to perform high‐resolution vibration measurements, from which their dynamics properties are identified and, subsequently, a high spatial resolution, full‐modal‐order dynamics model of cable vibration can be established. This study develops a physics‐guided, unsupervised machine learning‐based video processing approach that can blindly and efficiently extract the full‐field (as many points as the pixel number of the video frame) modal parameters of cable vibration using only the video of an operating (output‐only) cable. In particular, by incorporating the physics of cable vibration (taut string model), a novel automated modal motion filtering method is proposed to enable autonomous identification of full‐order (as many modes as possible) dynamic parameters, including those weakly excited modes that used to be challenging to identify in operational modal analysis. Therefore, a full‐field, full‐order modal model of cable vibration is established by the proposed method. Furthermore, this new approach provides a low‐cost and noncontact technique to estimate the cable tension using only the video of the vibrating cable where the fundamental frequency is automatically and efficiently estimated to compute the cable tension according to the taut string equation. Laboratory experiments on a bench‐scale cable are conducted to validate the developed approach. [ABSTRACT FROM AUTHOR]

Video-based measurement has received increased attention for modal analysis and nondestructive evaluation, playing an important role in the development of the next-generation structural sensing technologies. As these techniques have evolved, more quantitative approaches based on computer vision techniques have emerged on full-field unsupervised structural identification, exploiting the benefits provided by the use of video cameras such as high spatial sensor density and low installation costs. More recent work has started to explore the use of laser point cloud data for 3D mapping of scenes and structures. Sensors such as LIDAR provide huge amounts of measurements at high spatial resolution from which it is possible to estimate accurate structural geometry for applications such as the generation of CAD models. Unfortunately to-date, the frame rate and depth resolution of LIDAR and other sensors capable of 3D geometry measurements has not been sufficient for measuring structural dynamics. In this paper, we introduce an approach for efficient and extremely high resolution 3D structural dynamic identification/modal analysis from point cloud data acquired using a commercial, low-cost, time-of-flight imager. Vibration mode shapes and modal coordinates are extracted from this data by creating virtual Lagrangian sensors based on the point clouds parameters. First, time-varying point cloud data are collected from a vibrating structure. Then, a mesh of virtual sensors is created based on the dynamic point cloud data for tracking the structure's displacement over time. Next solutions to the blind source separation problem are employed to estimate high resolution 3D mode shapes, modal coordinates, and resonant frequencies. We demonstrate the potential of our proposed approach on laboratory tests and compare the results to the data collected from conventional laser displacement sensors. This technique represents an advance towards efficiently exploring the full advantages of using dynamic point cloud data for practical monitoring applications and has the potential to be extended for a wide range of 3D motion decomposition problems. • Computer vision technique for estimating 3D vibration modes. • This is the first attempt to exploit dynamic point clouds for structural dynamics. • A time-of-flight imager is employed to obtain dynamic measurements. • Vibration modes are estimated from the dynamic data by forming virtual sensors. • Modal estimation is blindly achieved by dimension reduction and BSS algorithms. • The technique is experimentally compared to results obtained from laser data. [ABSTRACT FROM AUTHOR]

Traditional modal analysis requires physically attached sensors for data acquisition and vibration-based monitoring. Although traditional modal analysis presents well-established techniques for dynamics analysis, they can impose mass-loading effects on lightweight structures and increase budgetary demands on the maintenance of such data acquisition systems. Recently video-based techniques have become of increasing interest in the identification of the dynamic properties of infrastructures with arbitrary complexity. However, most applications rely on frame by frame tracking of fixed speckle targets to derive time-varying physical parameters. This imposes serious limitations for real-world applications, especially in scenarios where the structure is out of reach. Therefore, to address these issues, we propose a novel output-only operational modal analysis method based on vision-based blind source separation scheme. The proposed algorithm makes use of each pixel as a potential measurement point. This enables an increase in the spatial density of sensors conventionally used on a structure by orders of magnitude. This simultaneous processing of all pixel time-series derives full-field high-resolution mode shapes instead of low spatial resolution mode shapes achieved when measuring a limited number of discrete locations with typical sensors. Compared to other approaches, we propose a blind source separation scheme simpler than the ones based on phase extraction and complex steerable pyramids that still capable of disentangling local structural vibration from video measurement only. Moreover, a simple method to magnify and visualize independent vibration modes is introduced using the extracted modal information only. We validate our method by laboratory experiments on a bench-scale building structure and a cantilever beam. The results demonstrate that the proposed technique can decompose high-resolution modal parameters, visualize and reconstruct even those weakly-excited vibration modes. [ABSTRACT FROM AUTHOR]

• Computer vision for modal analysis. • Architecture independent of image pre-processing. • Results superior to conventional vibration measurement techniques. The conventional modal analysis involves physically-attached wired or wireless sensors for vibration measurement of structures. However, this method has certain disadvantages, owing to the sensor's weight and its low spatial resolution, which limits the analysis precision or the high cost of optical vibration sensors. Besides, the sensor installation and calibration in itself is a time consuming and labor-intensive process. Non-contact computer vision-based vibration measurement techniques can address the shortcomings mentioned above. In this paper, we introduce CNN-LSTM (Convolutional Neural Network, Long Short-Term Memory) deep learning based approach that can serve as a backbone for computer vision-based vibration measurement techniques. The key idea is to use each pixel of an image taken from an off the shelf camera, encapsulating the Spatio-temporal information, like a sensor to capture the modal frequencies of a vibrating structure. Non-contact "pixel-sensor" does not alter the system's dynamics and is relatively low-cost, agile, and provides measurements with very high spatial resolution. Our computer vision-based deep learning model takes the video of a vibrating structure as input and outputs the fundamental modal frequencies. We demonstrate, using reliable empirical results, that "pixel-sensor" is more efficient, autonomous, and accurate. Robustness of the deep learning model has been put to the test by using specimens of a variety of materials, and varying dimensions and results have shown high levels of sensing accuracy. [ABSTRACT FROM AUTHOR]