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- 저자 : Yuguang Fu (검색결과 4 건)
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Yuguang Fu,Tu Hoang,Kirill Mechitov,Jong R. Kim,Dichuan Zhang,Billie F. Spencer Jr 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.28 No.4
Seismic structural health monitoring (SHM) of structures is critical not only to detect earthquakes to send early warning, but also to enable rapid structural condition assessment to ensure safety. Traditional monitoring systems using wired sensors are expensive. Wireless sensors offer tremendous opportunity to reduce costs, which remains elusive for seismic structural monitoring due to two main obstacles. First, there are constraints on power resources. Most wireless sensors are dutycycled to preserve limited battery power; and hence, can miss an earthquake in power-saving sleep mode. Second, there is a lack of support for rapid post-event data collection and processing. Conventional data transmission after sensing can introduce significant delays, and real-time data acquisition that eliminates these delays has limited throughput. In this paper, an intelligent wireless monitoring system, <i>xShake</i>, is developed for cost-effective real-time seismic SHM. It consists of: 1) energy-efficient wireless sensor prototypes utilizing on-demand sensing technique, 2) live-streaming framework that supports high-throughput real-time data acquisition, and 3) a rapid condition assessment application, enabling real-time data visualization and processing for end users. The performance of the <i>xShake</i> is validated through lab tests, demonstrating that it can capture high-fidelity synchronized data under earthquakes and enable real-time structural condition assessment.
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Yuguang Fu,Tarutal Ghosh Mondal,Jau-Yu Chou,Jian-Xiao Mao 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.3
This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.
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Hao Wang,Youhao Ni,Jian-Xiao Mao,Yuguang Fu,Zhuo Xi 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.1
Steel bolted joint is an important part of steel structure, and its damage directly affects the bearing capacity and durability of steel structure. Currently, the existing research mainly focuses on the identification of corroded bolts and corroded bolts respectively, and there are few studies on multiple states. A detection framework of corroded and loosened bolts is proposed in this study, and the innovations can be summarized as follows: (i) Vision Transformer (ViT) is introduced to replace the third and fourth C3 module of you-only-look-once version 5s (YOLOv5s) algorithm, which increases the attention weights of feature channels and the feature extraction capability. (ii) Three states of the steel bolts are considered, including corroded bolt, bolt missing and clean bolt. (iii) Line segment detector (LSD) is introduced for bolt rotation angle calculation, which realizes bolt looseness detection. The improved YOLOv5s model was validated on the dataset, and the mean average precision (mAP) was increased from 0.902 to 0.952. In terms of a lab-scale joint, the performance of the LSD algorithm and the Hough transform was compared from different perspective angles. The error value of bolt loosening angle of the LSD algorithm is controlled within 1.09%, less than 8.91% of the Hough transform. Furthermore, the proposed framework was applied to fullscale joints of a steel bridge in China. Synthetic images of loosened bolts were successfully identified and the multiple states were well detected. Therefore, the proposed framework can be alternative of monitoring steel bolted joints for management department.
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SHM data anomaly classification using machine learning strategies: A comparative study
Shieh-Kung Huang,Jau-Yu Chou,Yuguang Fu,Chia-Ming Chang 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.
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