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- 저자 : Yanbo Niu (검색결과 2 건)
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Investigation of the Dynamic Behavior of a Super High-rise Structure using RTK-GNSS Technique
Chunbao Xiong,Yanbo Niu 대한토목학회 2019 KSCE JOURNAL OF CIVIL ENGINEERING Vol.23 No.2
Super high-rise structures have a significant deformation under ambient excitations such as earthquake and wind, which may lead to structural instability and even damage. To capture the dynamic characteristics of a super high-rise structure under construction (i.e., Tianjin 117 tower), Real Time Kinematic - Global Navigation Satellite Systems (RTK-GNSS) sensors are employed to derive the horizontal displacement of the structure. Considering the defects in measurement accuracy of RTK-GNSS sensors, a Type 1 Chebyshev high-pass digital filter is firstly employed and thus the output results are smoothed. Subsequently, based on the smoothed signals, the natural frequencies and the corresponding damping ratios are extracted via FFT (Fast Fourier Transform) and RDT-LDM (random decrement technique combined with logarithmic decrement method). It is found that the results from the field measurement coincide with the numerical simulation. Finally, the structural parameters are successfully obtained and illustrated graphically.
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Jiangpeng Shu,Gaoyang Liu,Yanbo Niu,Weijian Zhao,Yuan-Feng Duan 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.
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