To investigate the dynamic characteristics of a self-anchored suspension bridge (i.e. Tianjin Fumin Bridge), a real time kinematic-global navigation satellite system (RTK-GNSS) is used to achieve the displacement responses of the structure. Seeing the defect in the positioning accuracy of RTK-GNSS, a combined method (EEMDWP) of ensemble empirical mode decomposition (EEMD) and wavelet packet (WP) analysis is firstly put forward to improve the precision of the vibration signals. Subsequently, fast Fourier transform and the random decrement technique are applied to estimate the natural frequency and the corresponding damping ratio of the structure. Meanwhile, to contrast the field measurement results, the finite element model (FEM) of the structure is established. Finally, the analysis results indicate that: (1) the RTK-GNSS technique is a powerful tool for monitoring the deformation of long-span bridges; (2) the proposed EEMDWP method is demonstrated to be better than the single EEMD or WP method; (3) the structural dynamic parameters are successfully obtained (i.e. the first natural frequency: 0.5873 Hz, the corresponding damping ratio: 2.12%); (4) the results from the field measurement are in agreement with the FEM with a difference of about 5% from each other.
This article aims to investigate the dynamic characteristics (e.g. natural frequency and damping ratio) for two super high-rise completed and uncompleted buildings. Real-time kinematic-global navigation satellite system technology is applied to observe the dynamic responses. To improve the positioning accuracy and avoid distortion of the results, a Type 1 Chebyshev high-pass digital filter is used. The natural frequencies and damping ratios of the buildings are determined using the fast Fourier transform analysis and random decrement technique combined with a logarithmic decrement method, respectively. The structural parameters are obtained. The results show that real-time kinematic-global navigation satellite system technology can provide the dynamic responses of super highrise buildings in an efficient manner and that the dynamic characteristics from field measurements agree well with the results of the numerical simulation.
Under the action of wind, traffic, and other influences, long-span bridges are prone to large deformation, resulting in instability and even destruction. To investigate the dynamic characteristics of a long-span concrete-filled steel tubular arch bridge, we chose a global navigation satellite systems-real-time kinematic (GNSS-RTK) to monitor its vibration responses under ambient excitation. A novel approach, the use of complete ensemble empirical mode decomposition with adaptive noise combined with wavelet packet (CEEMDAN-WP) is proposed in this study to increase the accuracy of the signal collected by GNSS-RTK. Fast Fourier transform (FFT) and random decrement technique (RDT) were adopted to calculate structural modal parameters. To verify the combined denoising and modal parameter identification methods proposed in this paper, we established the structural finite element model (FEM) for comparison. Through simulation and comparison, we were able to draw the following conclusions. (1) GNSS-RTK can be used to monitor the dynamic response of long-span bridges under ambient excitation; (2) the CEEMDAN-WP is an efficient method used for the noise reduction of GNSS-RTK signals; (3) after signal filtering and noise reduction, structural modal parameters are successfully derived through RDT and illustrated graphically; and (4) the first-order natural frequency identified by field measurement is slightly higher than the FEM in this work, which may have been caused by bridge damage or the inadequate accuracy of the finite element model.
Aiming at the problem of low intelligent level of shearer, a shearer cutting pattern recognition method is proposed based on the combination of multi-scale fuzzy entropy, Laplace score and support vector machine. By extracting the multi-scale fuzzy entropy of the vibration signal under different cutting modes, the feature vector representing the cutting pattern is mastered. At the same time, the Laplace score is used to select the feature vectors with possessing rich cutting pattern information. The selected features are produced as the learning samples of support vector machine. The experimental system of shearer cutting coal-rock is built, and the vibration signals of rocker arm under different cutting patterns are extracted. The experimental analysis is carried out and the results indicate that the cutting pattern recognition method proposed in this paper has high recognition accuracy, and the correct rate can reach to 98.86%. The research results provide technical support for the intelligent and rapid development of fully mechanized mining face.
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