@article{wang_zargar_yuan_2021, title={Augmented reality for enhanced visual inspection through knowledge-based deep learning}, volume={20}, ISSN={["1741-3168"]}, DOI={10.1177/1475921720976986}, abstractNote={A two-stage knowledge-based deep learning algorithm is presented for enabling automated damage detection in real-time using the augmented reality smart glasses. The first stage of the algorithm entails the identification of damage prone zones within the region of interest. This requires domain knowledge about the damage as well as the structure being inspected. In the second stage, automated damage detection is performed independently within each of the identified zones starting with the one that is the most damage prone. For real-time visual inspection enhancement using the augmented reality smart glasses, this two-stage approach not only ensures computational feasibility and efficiency but also significantly improves the probability of detection when dealing with structures with complex geometric features. A pilot study is conducted using hands-free Epson BT-300 smart glasses during which two distinct tasks are performed: First, using a single deep learning model deployed on the augmented reality smart glasses, automatic detection and classification of corrosion/fatigue, which is the most common cause of failure in high-strength materials, is performed. Then, in order to highlight the efficacy of the proposed two-stage approach, the more challenging task of defect detection in a multi-joint bolted region is addressed. The pilot study is conducted without any artificial control of external conditions like acquisition angles, lighting, and so on. While automating the visual inspection process is not a new concept for large-scale structures, in most cases, assessment of the collected data is performed offline. The algorithms/techniques used therein cannot be implemented directly on computationally limited devices such as the hands-free augmented reality glasses which could then be used by inspectors in the field for real-time assistance. The proposed approach serves to overcome this bottleneck.}, number={1}, journal={STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL}, author={Wang, Shaohan and Zargar, Sakib Ashraf and Yuan, Fuh-Gwo}, year={2021}, month={Jan}, pages={426–442} }
 @article{wang_fong_yuan_2021, title={Vibration-based Damage Imaging via High-Speed Cameras with 3D Digital Image Correlation using Wavelet Transform}, volume={11591}, ISSN={["1996-756X"]}, DOI={10.1117/12.2585246}, abstractNote={Advances in computer prerformance, optical devices and digital imaging technology have allowed digital image correlation methods to measure the shape and deformation of the vibrating structures. This paper presents a novel vision-based damage detection framework which can eliminate the sinusoidal shaker step along with a magnified, precise modal shape data especially for higher modes that typical DIC algorithm can hardly detect. For this, an aluminum plate has been selected to make a comparative study on the performance of serval vibration-based damage detection techniques, namely, curvature mode shapes , Gapped smoothing techniques Generalized Fractal Dimension, Modal Strain Energy and Wavelet Transform.}, journal={SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2021}, author={Wang, Shaohan and Fong, Rey-Yie and Yuan, Fuh-Gwo}, year={2021} }
 @article{yuan_zargar_chen_wang_2020, title={Machine Learning for Structural Health Monitoring: Challenges and Opportunities}, volume={11379}, ISSN={["1996-756X"]}, DOI={10.1117/12.2561610}, abstractNote={A physics-based approach to structural health monitoring (SHM) has practical shortcomings which restrict its suitability to simple structures under well controlled environments. With the advances in information and sensing technology (sensors and sensor networks), it has become feasible to monitor large/diverse number of parameters in complex real-world structures either continuously or intermittently by employing large in-situ (wireless) sensor networks. The availability of this historical data has engendered a lot of interest in a data-driven approach as a natural and more viable option for realizing the goal of SHM in such structures. However, the lack of sensor data corresponding to different damage scenarios continues to remain a challenge. Most of the supervised machine-learning/deep-learning techniques, when trained using this inherently limited data, lack robustness and generalizability. Physics-informed learning, which involves the integration of domain knowledge into the learning process, is presented here as a potential remedy to this challenge. As a step towards the goal of automated damage detection (mathematically an inverse problem), preliminary results are presented from dynamic modelling of beam structures using physics-informed artificial neural networks. Forward and inverse problems involving partial differential equations are solved and comparisons reveal a clear superiority of physics-informed approach over one that is purely datadriven vis-a-vis overfitting/generalization. Other ways of incorporating domain knowledge into the machine learning pipeline are then presented through case-studies on various aspects of NDI/SHM (visual inspection, impact diagnosis). Lastly, as the final attribute of an optimal SHM approach, a sensing paradigm for non-contact full-field measurements for damage diagnosis is presented.}, journal={SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2020}, author={Yuan, Fuh-Gwo and Zargar, Sakib Ashraf and Chen, Qiuyi and Wang, Shaohan}, year={2020} }