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Digital cameras are cost-effective vision sensors and able to directly provide two-dimensional information of structural condition in monitoring and assessment applications. For example, digital cameras are essential components of unmanned aerial vehicles (UAVs) and robotic agents for mobile sensing and inspection of pipelines, buildings, transportation infrastructure, etc, especially in post-natural disaster and man-made extreme events assessment. Additionally, while surveillance cameras have been widely used for transportation systems (e.g., traffic monitoring), if appropriately mounted on the large-scale structures such as the bridges, they can continuously monitor the structural condition under operational loads and hazards, complementing the regular visual inspection and assessment conducted by experts. In these or other applications, efficiently and reliably transferring the structural images or videos, which are as such large-scale, are important and challenging, especially in wireless platform that is either required (e.g., UAVs and robotic agents) or more suitable (e.g., camera monitoring networks) with only limited power and communication resources. This paper studies the computational algorithms for efficient and reliable transmission of the structural monitoring images; in particular, the compressed sensing (CS) technique is explored for robust data transmission and recovery. The sparse representation or data structure of the structural images is exploited, leading to the CS based central strategy: on some sparse domain, randomly encode large-scale image data into few relevant coefficients, which are then transferred (robust to random data loss) and recovered (in base station) for subsequent structural health diagnosis. Image data of bench scale pipe structure, concrete structure and full scale stay cable are employed for validation of the CS based method. Its performance is also compared with traditional transform coding and low-dimensional encoding (sampling), and their advantages and drawbacks are discussed. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a new paradigm of explicitly modeling and harnessing the data structure to address the inverse problems in structural dynamics, identification, and data-driven health monitoring. In particular, it is shown that the structural dynamic features and damage information, intrinsic within the structural vibration response measurement data, possesses sparse and low-rank structure, which can be effectively modeled and processed by emerging mathematical tools such as sparse representation and compressed sensing, low-rank matrix decomposition and completion, as well as the unsupervised multivariate blind source separation. It is also discussed that explicitly modeling and harnessing the sparse and low-rank data structure could benefit future work in developing data-driven approaches toward rapid, unsupervised, and effective system identification, damage detection, as well as massive SHM data sensing and management. Copyright © 2016 John Wiley & Sons, Ltd. [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]

• We propose an accelerated noncontact guided wave array imaging method with sparse scanning measurements; • Our method using sparse array data provides defect imaging performance comparable to the dense scanning case; • We demonstrate the proposed approach on laboratory experiments on composite plate specimens with multiple defects with encouraging results. Noncontact guided wave array imaging with a scanning laser Doppler vibrometer (SLDV) is an effective tool to detect and locate defects within a plate-like structure, as it obviates the need for installing, calibrating, and maintaining a transducer array. However, it requires collecting guided wave signals through scanning across dense spatial grid points to avoid non-defect artifacts in the array image, which is time-consuming. In this paper, we present an accelerated noncontact guided wave array imaging method that does not require dense scanning array while providing defect imaging performance comparable to the dense scanning case. In our approach, sparse scanning measurements at only a small number of points are carried out first for fast guide wave data acquisition. Then, dense guided wave array data is reconstructed from these sparse array measurements using a sparsity-promoting optimization technique, followed by delay-and-sum (DAS) beamforming to image defects within a test structure. We validate this method with laboratory experiments on composite plate specimens with multiple defects. The results demonstrate that defects within a composite plate can be successfully detected and located using sparsely sampled guided wave array measurement data. Such a significant reduction in the number of required measurement points enables accelerated noncontact guided wave array imaging. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses two problems in structural damage identification: locating damage and assessing damage severity, which are incorporated into the classification framework based on the theory of sparse representation (SR) and compressed sensing (CS). The sparsity nature implied in the classification problem itself is exploited, establishing a sparse representation framework for damage identification. Specifically, the proposed method consists of two steps: feature extraction and classification. In the feature extraction step, the modal features of both the test structure and the reference structure model are first blindly extracted by the unsupervised complexity pursuit (CP) algorithm. Then in the classification step, expressing the test modal feature as a linear combination of the bases of the over-complete reference feature dictionary—constructed by concatenating all modal features of all candidate damage classes—builds a highly underdetermined linear system of equations with an underlying sparse representation, which can be correctly recovered by -minimization; the non-zero entry in the recovered sparse representation directly assigns the damage class which the test structure (feature) belongs to. The two-step CP–SR damage identification method alleviates the training process required by traditional pattern recognition based methods. In addition, the reference feature dictionary can be of small size by formulating the issues of locating damage and assessing damage extent as a two-stage procedure and by taking advantage of the robustness of the SR framework. Numerical simulations and experimental study are conducted to verify the developed CP–SR method. The problems of identifying multiple damage, using limited sensors and partial features, and the performance under heavy noise and random excitation are investigated, and promising results are obtained. [Copyright &y& Elsevier]