With increasing traffic volume, the traffic load grade given by design codes has gradually increased. For new bridges, there is no problem, and the traffic load can be met through the requirements of the new code. However, for existing bridges, there is a lack of uniform standards on whether they can continue to be used. It is not clear whether these bridges will be judged according to the new code or the original design code. The traffic loading effects of different codes on medium‐ and small‐span girder bridges in China are investigated in this study. Three codes are introduced: JTJ 021‐89, JTG D60‐2004, and JTG D60‐2015. Simply supported girder bridges and continuous girder bridges are discussed. The traffic loading effects calculated based on JTG D60‐2015 are significantly larger than those calculated based on JTJ 021‐89. For simply supported girder bridges, most of the differences range from 20% to 40%, and the maximum value is almost larger than 60%. For continuous girder bridges, most of the differences in the positive bending moments are concentrated in the 20%∼40% range, while the differences in the negative bending moments range from 10% to 20%. Therefore, the differences in traffic loading effects calculated based on various codes cannot be ignored in actual bridge engineering. The conclusion in this study can provide a basis for bridge structure evaluation and life prediction.
The exact dynamic transfer matrix is derived for a straight and uniform Bernoulli-Euler thin-walled beam element whose elastic and inertial axes are not coincident by directly solving the governing differential equations of motion of the beam. The Bernoulli-Euler bending-torsion coupled beam theory is used, and the cross-section is asymmetric. The bending vibrations in two perpendicular directions are coupled with torsional vibration and the effect of warping stiffness is included. The dynamic transfer matrix can be used for calculation of exact natural frequencies and mode shapes for asymmetrical thin-walled beams and its assemblages. Numerical results are given for an example of thin-walled beam with a variety of boundary conditions, and numerical solutions of natural frequencies are tabulated for comparison. The effect of warping stiffness on natural frequencies and mode shapes is also discussed. The results show that when the warping effect is neglected, the associated errors become increasingly large as the frequency order increases.
This paper studies code trace circuit of the frequence skipping system in the wireless data transmission equipment. A broad band signal optimum trace question is proposed and a total time former and later uninterrupt code traoe circuit of fre-quency skipping and frequency extending system are given. The input and output relation of code trace circuit, primary performance parameters of the output power density, discriminative characteristic diagram, code trace shake of the deky locked discriminator are amalyzed.
Nonparametric control charts that can detect arbitrary distributional changes are highly desirable due to their flexibility to adapt to different distributional assumptions and distributional changes.However, most of nonparametric control charts in the literature either can only detect location changes, or involve intensive computation.In this paper, we propose a new nonparametric adaptive CUSUM chart.The proposed control chart can detect arbitrary distributional changes and is efficient in computation.Its selfstarting nature makes the proposed control chart applicable to situations where no sufficiently large reference data are available.Our proposed control chart also has a built-in post-signal diagnostics function that can identify what kind of distributional changes have occurred after an alarm.Our simulation study and real data analysis show that the proposed control chart performs well across a broad range of settings, and compares favorably with existing nonparametric control charts.
This paper proposes an enhanced vibration decomposition approach based on analytical mode decomposition (AMD) and multisynchrosqueezing transform (MSST). Although AMD-based low-pass filter has been applied for signal decomposition with time-varying cutoff frequencies, these cutoff frequencies are usually manually selected from the wavelet scalogram of the target signal. This process therefore significantly reduces the computational efficiency and could affect the accuracy of using AMD-based low-pass filter for non-stationary signal analysis. To overcome this problem, in this study, MSST with a time-varying cutoff frequency detection algorithm is used to automatically define the time-varying bisecting frequencies for the AMD analysis. Once the time-varying cutoff frequencies are identified, AMD can be used to adaptively decompose the non-stationary signal into individual components. To investigate the effectiveness of the proposed approach, termed as MSST–AMD, for vibration signal decomposition and its application, numerical studies on a non-stationary signal with overlapped frequency components are conducted. To further apply the proposed approach for structural vibration response analysis, a three-story shear-type structure with varying stiffness subjected to earthquake excitations is simulated in this study for instantaneous modal parameter identification. In experimental verifications, the proposed MSST–AMD approach combined with a damage index is further extended to evaluate the damage severity of a structure under earthquake excitations. The results in both numerical simulations and experimental validations demonstrate that the proposed approach is reliable and accurate for non-stationary signal analysis and vibration decomposition, which can be further used for instantaneous modal parameter identification and structural damage detection.
Point cloud completion task aims to predict the missing part of incomplete point clouds and generate complete point clouds with details. In this paper, we propose a novel point cloud completion network, namely CompleteDT. Specifically, features are learned from point clouds with different resolutions, which is sampled from the incomplete input, and are converted to a series of \textit{spots} based on the geometrical structure. Then, the Dense Relation Augment Module (DRA) based on the transformer is proposed to learn features within \textit{spots} and consider the correlation among these \textit{spots}. The DRA consists of Point Local-Attention Module (PLA) and Point Dense Multi-Scale Attention Module (PDMA), where the PLA captures the local information within the local \textit{spots} by adaptively measuring weights of neighbors and the PDMA exploits the global relationship between these \textit{spots} in a multi-scale densely connected manner. Lastly, the complete shape is predicted from \textit{spots} by the Multi-resolution Point Fusion Module (MPF), which gradually generates complete point clouds from \textit{spots}, and updates \textit{spots} based on these generated point clouds. Experimental results show that, because the DRA based on the transformer can learn the expressive features from the incomplete input and the MPF can fully explore these feature to predict the complete input, our method largely outperforms the state-of-the-art methods.
To obtain the health status of long-span cable-stayed bridges, multiple sensors are applied to the health monitoring system for data collection. The optimal layout of sensors that aims to obtain as much structural information as possible with fewer sensors is important to ensure the effectiveness of the health monitoring system. Sensors are usually placed in typical locations where the structural response is obvious, and most studies utilize static response for the determination of sensor location. In fact, bridges primarily suffer the dynamic load, of which the response has a significant impact on the structural health. In this article, an optimal sensor layout method for a long-span cable-stayed bridge based on dynamic response is proposed under the consideration of vehicle–bridge coupled vibration. With vehicle load applied onto different lanes, the dynamic responses of different bridge members are obtained, and the number and the location of cable force sensors are determined according to the distribution of cable dynamic coefficient D C , and the number and the location of displacement and strain sensors are determined according to the distribution of D GD and D GM , which are the dynamic load allowance for girder deflection and bending moment, respectively. The results prove that this method can reduce the number of sensors effectively and obtain bridge state information more perfectly.
This article presents a novel data-driven structural damage detection method named moving embedded principal component analysis to monitor the bridge condition and detect the damage occurrence using only one sensor. A fixed moving window is used to cut out the time series of the recorded data for the analysis. The data set inside the window is embedded to be a multidimensional state space using time delay method. The matrix of the state space is analyzed using the standard principal component analysis method, and a novel damage index R j defined with the eigenvalue is proposed to identify structural damage occurrence. The window length is determined by a new approach through examining the convergent spectrum of the contribution ratio of the first principal component of the embedded state space. The time delay is determined by the autocorrelation function of the response, and the embedding dimension is obtained by the cumulative contribution ratio of the state space. The windowed damage index can be calculated continuously by moving the window along the recorded vibration data. To demonstrate the performance of the proposed method, responses of a beam bridge model subjected to stochastic loads obtained with numerical simulations and experimental tests are analyzed to monitor the structural conditions. The results demonstrate that the proposed method can accurately identify the occurrence of damage and the abnormal behavior of the structure. The recorded data on a large suspension bridge are also analyzed. The analysis successfully identified an incident on this bridge when it was slightly scraped by the mast of a sand ship. This further verifies the effectiveness of the proposed method.
With the rapid development of energy-saving and clean new energy, power fluctuations bring great challenges to frequency control of power grid. The main ways of dealing with the instantaneous power gap depends on primary frequency compensation(PFC) of thermal power units. The characteristics and questions of automatic generation control (AGC) and PFC were analyzed, and the optimization of unit control were offered. It can ensure that the frequency modulation and peak shaving capacity of the thermal power unit meets the requirements of power grid standards, and effectively ensure the priority of the PFC action when the grid frequency fluctuates, improve the coordinated operation of the AGC and PFC.
Particle filter (PF) is an important way for target tracking in wireless sensor network (WSN). In the paper we proposed an improved particle filter algorithm which outperforms general PF when target suddenly changes movement direction. Our algorithm used estimated direction of motion based on the current measurements to optimize the prediction in PF. It modified the deviation of estimated mean of particle steam that is possibly produced by sudden changes in direction. Simulation results show that the new particle filter algorithm can achieve better tracking performance than other filter algorithms.
