Review on dynamic analysis of road pavements under moving vehicles and plane strain conditions

This paper reviews works on the dynamic analysis of flexible and rigid pavements under moving vehicles on the basis of continuum-based plane strain models and linear theories.The purpose of this review is to provide in-formation about the existing works on the subject,critically discuss them and make suggestions for further research.The reviewed papers are presented on the basis of the various models for pavement-vehicle systems and the various methods for dynamically analyzing these systems.Flexible pavements are modeled by a homogeneous or layered half-plane with isotropic or anisotropic and linear elastic,viscoelastic or poroelastic material behavior.Rigid pavements are modeled by a beam or plate on a homogeneous or layered half-plane with material properties like the ones for flexible pavements.The vehicles are modeled as concentrated or distributed over a finite area loads moving with constant or time dependent speed.The above pavement-vehicle models are dynamically analyzed by analytical,analytical/numerical or purely numerical methods working in the time or frequency domain.Representative examples are presented to illustrate the models and methods of analysis,demonstrate their merits and assess the effects of the various parameters on pavement response.The paper closes with con-clusions and suggestions for further research in the area.The significance of this research effort has to do with the presentation of the existing literature on the subject in a critical and easy to understand way with the aid of representative examples and the identification of new research areas.

Aerodynamic characteristics of moving vehicles of two trains passing each other on bridge under crosswinds

Two trains passing each other is controlling factor for the wind-vehicle-bridge systems.To test the aerodynamic characteristics of moving vehicles under crosswinds when two trains are passing each other,a wind tunnel test device,which has two moving tracks,was developed.The rationality of the test result was discussed,the effects of intersection mode,yaw angle and lane spacing on the aerodynamic coefficients of the leeward train were analyzed,and the difference of aerodynamic coefficients between the head vehicle and the tail vehicle was discussed.The results show that the proposed test device has good repeatability.The intersection modes have a certain effect on the aerodynamic force of the leeward train when two trains are passing each other,and the results should be more reasonable during the two trains dynamic passing each other.With the decrease of yaw angle,the sudden change of train aerodynamic coefficients is more obvious.The decrease of lane spacing will increase the sudden change of leeward vehicles.In the process of two trains passing each other,the aerodynamic coefficients of the head vehicle and tail vehicle are significantly different,so the coupling vibration analysis of wind-vehicle-bridge system should be considered separately.

DYNAMIC RESPONSE OF PLATES DUE TO MOVING VEHICLES USING FINITE STRIP METHOD

Dynamic response of beam-like structures to moving vehicles has been extensively studied. However, the study on dynamic response of plates to moving vehicles has so far received but scant attention. A plate-vehicle strip for simulating the interaction between a rectangular plate and moving vehicles was described. For the portion of strips that are in direct contact with the moving vehicles, the plate-vehicle strips were employed. Conventional plate finite strips were used to model the portion of strips that are not directly under the action of moving vehicles. In the analysis, each moving vehicle is idealized as a one-foot dynamic system with tire unsprung mass and sprund mass interconnected by a spring and a dashpot. The numerical results obtained from the proposed method agree well with available results.

Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network

In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied.Furthermore,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm.For the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm.The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.

Simulink Based Simulation of the Behavior of a Ribbon Pontoon Bridge

A mathematical model of a ribbon pontoon bridge subjected to moving loads was formulated using the theory of simply supported beams.Two types of moving load models were used, the first a moving-constant-force model and the second a moving-mass model.Using both types of loads, the dynamic behavior of a ribbon pontoon bridge was simulated while subjected to a single moving load and then multiple moving loads.Modeling was done with the Simulink package in MATLAB software.Results indicated that the model is correct.The two types of moving load models made little difference to the response ranges when loads moved on the bridge, but made some difference to the response phases.When loads left, the amplitude of the dynamic responses induced by the moving-constant-force model load were larger than those induced by the moving-mass model.There was a great deal more difference when there were more loads.

Vehicle detection algorithm based on codebook and local binary patterns algorithms

Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.

Automated Detection of Contaminated Radar Image Pixels in Mountain Areas

In mountain areas, radar observations are often contaminated （1） by echoes from high-speed moving vehicles and （2） by point-wise ground clutter under either normal propagation （NP） or anomalous propagation （AP） conditions. Level Ⅱ data are collected from KMTX （Salt Lake City, Utah） radar to analyze these two types of contamination in the mountain area around the Great Salt Lake. Human experts provide the ＂ground truth＂ for possible contamination of either type on each individual pixel. Common features are then extracted for contaminated pixels of each type. For example, pixels contaminated by echoes from high-speed moving vehicles are characterized by large radial velocity and spectrum width. Echoes from a moving train tend to have larger velocity and reflectivity but smaller spectrum width than those from moving vehicles on highways. These contaminated pixels are only seen in areas of large terrain gradient （in the radial direction along the radar beam）. The same is true for the second type of contamination - pointwise ground clutters. Six quality control （QC） parameters are selected to quantify the extracted features. Histograms are computed for each QC parameter and grouped for contaminated pixels of each type and also for non-contaminated pixels. Based on the computed histograms, a fuzzy logical algorithm is developed for automated detection of contaminated pixels. The algorithm is tested with KMTX radar data under different （clear and rainy） weather conditions.

Review about Wireless Sensor Networks and the Internet of Things

Wireless sensor networks(WSNs)are created and affect our daily lives.You can find applications in various fields such as health,accident,life,manufacturing,production management,network management and many other fields.WSN now connects to the Internet of Things,connects the sensor to the Internet,and then uses it for collaboration and collaboration.However,when WSN is part of the internet we need to be able to study and analyze related terms.In this article,we’re going to look at different ways to getWSN online and identify the challenges that address in future as well.

Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories

With the rapid increase of the amount of vehicles in urban areas,the pollution of vehicle emissions is becoming more and more serious.Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making.Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning.Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions,however,they are not effective and efficient enough in the combination and utilization of different inputs.To address this issue,we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator(MOVES)model.Specifically,we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms.These matrices can reflect the attributes related to the traffic status of road networks such as volume,speed and acceleration.Then,our multi-channel spatiotemporal network is used to efficiently combine three key attributes(volume,speed and acceleration)through the feature sharing mechanism and generate a precise prediction of them in the future period.Finally,we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states,road networks and the statistical information of urban vehicles.We evaluate our model on the Xi′an taxi GPS trajectories dataset.Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.