Browsing by Author "Jana, Debasish"
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Item Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm(MDPI, 2023) Jana, Debasish; Nagarajaiah, SatishIn structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix’s elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.Item Measurement and identification of the nonlinear dynamics of a jointed structure using full-field data, Part I: Measurement of nonlinear dynamics(Elsevier, 2022) Chen, Wei; Jana, Debasish; Singh, Aryan; Jin, Mengshi; Cenedese, Mattia; Kosova, Giancarlo; Brake, Matthew R.W.; Schwingshackl, Christoph W.; Nagarajaiah, Satish; Moore, Keegan J.; Noël, Jean-PhilippeJointed structures are ubiquitous constituents of engineering systems; however, their dynamic properties (e.g., natural frequencies and damping ratios) are challenging to identify correctly due to the complex, nonlinear nature of interfaces. This research seeks to extend the efficacy of traditional experimental methods for linear system identification (such as impact testing, shaker ringdown testing, random excitation, and force or amplitude-control stepped sine testing) on nonlinear jointed systems, e.g., the half Brake–Reuß beam, by augmenting them with full-field data collected by high-speed videography. The full-field response is acquired using high-speed cameras combined with Digital Image Correlation (DIC), which enables studying the spatial–temporal dynamic characteristics of the system. As this is a video-based experiment, additional constraints are attached to the beam at the node points to remove the rigid body motion, which ensures that the beam is in the view of the camera during the entire test. The use of a video-based method introduces new sources of experimental error, such as noise from the high-speed camera’s fan and electrical noise, and so the measurement accuracy of DIC is validated using accelerometer data. After validating the DIC data, the measurements are recorded for several types of excitation, including hammer testing, shaker ringdown testing, fixed sine testing, and stepped sine testing. Using the DIC data to augment standard nonlinear system identification techniques, modal coupling and the mode shapes’ evolution are investigated. The suitability of videography methods for nonlinear system identification of nonlinear beams is explored for the first time in this paper, and recommendations for techniques to facilitate this process are made. This article focuses on developing an accurate data collection methodology as well as recommendations for nonlinear testing with DIC, which paves the way for video-based investigation of nonlinear system identification. In Part-II (Jin et al., 2021) of this work, the same data set is used for a rigorous assessment of nonlinear system identification with full-field DIC data.Item Measurement and identification of the nonlinear dynamics of a jointed structure using full-field data; Part II - Nonlinear system identification(Elsevier, 2022) Jin, Mengshi; Kosova, Giancarlo; Cenedese, Mattia; Chen, Wei; Singh, Aryan; Jana, Debasish; Brake, Matthew R.W.; Schwingshackl, Christoph W.; Nagarajaiah, Satish; Moore, Keegan J.; Noël, Jean-PhilippeThe dynamic responses of assembled structures are greatly affected by the mechanical joints, which are often the cause of nonlinear behavior. To better understand and, in the future, tailor the nonlinearities, accurate methods are needed to characterize the dynamic properties of jointed structures. In this paper, the nonlinear characteristics of a jointed beam is studied with the help of multiple identification methods, including the Hilbert Transform method, Peak Finding and Fitting method, Dynamic Mode Decomposition method, State-Space Spectral Submanifold, and Wavelet-Bounded Empirical Mode Decomposition method. The nonlinearities are identified by the responses that are measured via accelerometers in a series of experiments that consist of hammer testing, shaker ringdown testing, and response/force-control stepped sine testing. In addition to accelerometers, two high-speed cameras are used to capture the motion of the whole structure during the shaker ringdown testing. Digital Image Correlation (DIC) is then adopted to obtain the displacement responses and used to determine the mode shapes of the jointed beam. The accuracy of the DIC data is validated by the comparison between the identification results of acceleration and displacement signals. As enabled by full-field data, the energy-dependent characteristics of the structure are also presented. The setup of the different experiments is described in detail in Part I (Chen et al., 2021) of this research. The focus of this paper is to compare nonlinear system identification methods applied to different measurement techniques and to exploit the use of high spatial resolution data.Item Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing(MDPI, 2023) Jana, Debasish; Nagarajaiah, SatishIn civil, mechanical, and aerospace structures, full-field measurement has become necessary to estimate the precise location of precise damage and controlling purposes. Conventional full-field sensing requires dense installation of contact-based sensors, which is uneconomical and mostly impractical in a real-life scenario. Recent developments in computer vision-based measurement instruments have the ability to measure full-field responses, but implementation for long-term sensing could be impractical and sometimes uneconomical. To circumvent this issue, in this paper, we propose a technique to accurately estimate the full-field responses of the structural system from a few contact/non-contact sensors randomly placed on the system. We adopt the Compressive Sensing technique in the spatial domain to estimate the full-field spatial vibration profile from the few actual sensors placed on the structure for a particular time instant, and executing this procedure repeatedly for all the temporal instances will result in real-time estimation of full-field response. The basis function in the Compressive Sensing framework is obtained from the closed-form solution of the generalized partial differential equation of the system; hence, partial knowledge of the system/model dynamics is needed, which makes this framework physics-guided. The accuracy of reconstruction in the proposed full-field sensing method demonstrates significant potential in the domain of health monitoring and control of civil, mechanical, and aerospace engineering systems.Item Vision and Learning based Sensing for Structural Health Monitoring(2022-02-28) Jana, Debasish; Nagarajaiah, SatishCivil infrastructures like bridges play an essential role in maintaining the socio-economic growth of a nation. These important structures are designed to have a long service life, but the structural health may degrade due to natural hazards, aging, and man-made operation. Therefore, Structural Health Monitoring (SHM) is necessary to circumvent any system malfunctioning that might cause severe economic loss. Vibration measuring sensors are deployed for data acquisition to the structure of interest, a prerequisite to the vibration-based SHM. This dissertation focuses explicitly on increasing the efficiency of state-of-the-art sensing techniques – for both non-contact (vision-based) and contact-based sensors. All the proposed methods are derived and modified from the computer vision and machine learning algorithms; and applied to various structural components of the bridge. Algorithms developed in the first two chapters are applied to the stay-cable tension estimation of a cable-stayed bridge; frameworks proposed in the following three chapters are applied to the beams (girders) and plates (deck) of the bridge; deep learning technique presented in the last chapter is applied to deck pavement crack detection. First, a non-contact video-based cable tension estimation technique is proposed where a video is recorded using a moving handheld camera at a significant distance from the structure. Here, a camera movement cancellation technique is proposed to use vision-based motion estimation algorithms to estimate the cable tension. Second, contact-based wireless sensors are generally mounted on the stay-cables for acquiring vibration data. But these wireless sensors often undergo data-packet loss during data transmission – which may interrupt the online/real-time health monitoring process. Therefore, a sliding windowed compressive sensing-based data reconstruction technique is proposed to estimate the real-time stay-cable tension. Third, full-field sensing is mandatory to discover the possible damage location of a structure. A compressive sensing-based framework is proposed for estimating the full-field response time history from the time histories of a few sensors. Both `fixed sensors' and `mobile sensor' scenarios are explored. In both these scenarios, two subcases are considered – (a) If the system dynamics is unknown, the basis functions for compressive sensing is obtained by Dictionary Learning from a full-field experiment; (b) if a partial knowledge of the model dynamics is available, the basis functions are derived from the closed-form solution of the governing generalized partial differential equation. For mobile sensing-based full-field response estimation – instead of manually driving the vehicles – a multi-agent formation control strategy is proposed to automate the driving process. Finally, a deep learning (DL) framework is proposed for crack segmentation which can process the image data from an additional camera mounted on the mobile robots. This DL framework is based on the encoder-decoder convolution neural network (UNet) with transfer learning, outperforming the state-of-the-art segmentation framework in terms of performance and computational efficiency. Even though all the proposed techniques are applied to different bridges components, this is also implementable to any other structure. Real-time application of these techniques will lead to better maintenance, recovery time, and resilience estimation.