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1-D CNN deep learning of impedance signals for damage monitoring in concrete anchorage
Quoc-Bao Ta,Quang-Quang Pham,Ngoc-Lan Pham,Jeong-Tae Kim Techno-Press 2023 Structural monitoring and maintenance Vol.10 No.1
Damage monitoring is a prerequisite step to ensure the safety and performance of concrete structures. Smart aggregate (SA) technique has been proven for its advantage to detect early-stage internal cracks in concrete. In this study, a 1-D CNN-based method is developed for autonomously classifying the damage feature in a concrete anchorage zone using the raw impedance signatures of the embedded SA sensor. Firstly, an overview of the developed method is presented. The fundamental theory of the SA technique is outlined. Also, a 1-D CNN classification model using the impedance signals is constructed. Secondly, the experiment on the SA-embedded concrete anchorage zone is carried out, and the impedance signals of the SA sensor are recorded under different applied force levels. Finally, the feasibility of the developed 1-D CNN model is examined to classify concrete damage features via noise-contaminated signals. The results show that the developed method can accurately classify the damaged features in the concrete anchorage zone.
Ta, Quoc-Bao,Pham, Quang-Quang,Kim, Yoon-Chul,Kam, Hyeon-Dong,Kim, Jeong-Tae Techno-Press 2022 Structural monitoring and maintenance Vol.9 No.3
In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.
Jeong-Tae Kim,Quoc-Bao Ta,Ngoc-Loi Dang,Yoon-Chul Kim,Hyeon-Dong Kam 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.30 No.1
For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and nondestructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures.
Truong Khang Nguyen,Bao Quoc Ta,박익모 한국물리학회 2015 Current Applied Physics Vol.15 No.9
This paper presents an optimum design of a substrate-integrated cavity-type antenna for use in the terahertz frequency range. The antenna was designed with a frequency-selective surface (FSS) and a planar feeding structure that are both patterned on a high-permittivity gallium-arsenide substrate. The FSS, printed on the bottom side of the substrate, is made of a circular hole array that acts as a partially reflecting mirror. Meanwhile, the planar feeding structure, printed on the top side of the substrate, is a center-fed, open-ended slotline whose ground plane acts as a perfect reflective mirror; thus, it forms a FabryePerot resonator. The optimized antenna produced a maximum boresight gain of 14.3 dBi, a radiation efficiency of 62%, and side-lobe levels of -15.1 dB and -15.0 dB for the E- and H-planes, respectively, at a resonance frequency of 320 GHz. The proposed design exhibits compactness, planarity, and light weight compared with the substrate lens-coupled antenna design.
Thanh-Truong Nguyen,Jeong-Tae Kim,Quoc-Bao Ta,Duc-Duy Ho,Thi Tuong Vy Phan,Thanh-Canh Huynh 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.28 No.1
The piezoelectric-based smart interface technique has shown promising prospects for electro-mechanical impedance (EMI)-based damage detection with various successful applications. During the process of EMI monitoring and damage identification, the operational functionality of the smart interface device is a major concern. In this study, common functional degradations that occurred in the smart interface are diagnosed using a deep learning-based method. Firstly, the effect of functional degradations on the EMI responses is analytically discussed. Secondly, a critical structural joint is selected as the test structure from which EM measurement using the smart interface is conducted. Thirdly, a numerical model corresponding to the experimental model is established and updated to reproduce the measured EMI responses. By using the updated numerical model, the EMI responses of the smart interface under the common functional degradations, such as the shear lag effect, the adhesive debonding, the sensor breakage, and the interface detaching, are simulated; then, the functional degradation-induced EMI changes are characterized. Finally, a convolutional neural network (CNN)-based functional assessment method is newly proposed for the smart interface. The CNN can automatically extract and directly learn optimal features from the raw EMI signals without preprocessing. The CNN is trained and tested using the datasets obtained from the updated numerical model. The obtained results show that the proposed method was successful to classify four types of common defects in the smart interface, even under the effect of noises.