Optimal allocation method of electric/air braking force of high-speed train considering axle load transfer

Reasonable distribution of braking force is a factor for a smooth,safe,and comfortable braking of trains.A dynamic optimal allocation strategy of electric-air braking force is proposed in this paper to solve the problem of the lack of consideration of adhesion difference of train wheelsets in the existing high-speed train electric-air braking force optimal allocation strategies.In this method,the braking strategy gives priority to the use of electric braking force.The force model of a single train in the braking process is analyzed to calculate the change of adhesion between the wheel and rail of each wheelset after axle load transfer,and then the adhesion of the train is estimated in real time.Next,with the goal of maximizing the total adhesion utilization ratio of trailer/motor vehicles,a linear programming distribution function is constructed.The proportional coefficient of adhesion utilization ratio of each train and the application upper limit of braking force in the function is updated according to the change time point of wheelset adhesion.Finally,the braking force is dynamically allocated.The simulation results of Matlab/Simulink show that the proposed algorithm not only uses the different adhesion limits of each trailer to reduce the total amount of braking force undertaken by the motor vehicle,but also considers the adhesion difference of each wheelset.The strategy can effectively reduce the risk and time of motor vehicles during the braking process and improve the stability of the train braking.

DAMAGE TOLERANCE ANALYSIS ON HOLLOW AXLES OF HIGH SPEED MOTOR TRAINS

According to the rules of UIC515-3, the service loads of the axles are defined, which include some different loads cases as follows： the static loads; the impact loads resulted from running through the rail joints and unevenness rails; the loads through curves and from braking. Through the calculating and analysis, the stress distribution of the hollow axles is obtained for 200 km/h high speed motor trains used in China. At the same time, the fatigue crack growth of hollow axles is studied, and the initial surface cracks of 2 mm depth caused by hard objects strike or the other causes are discussed. On the basis of the linear elastic fracture mechanics theory, the stress intensity factor of the crack of the geometry transition outside the wheel seat is also studied. Associated with fatigue crack propagation equation and the corresponding crack propagation threshold, the crack propagation characteristics under different shapes are calculated. Then the running distances are educed with different shapes propagating to the critical length, and the estimation of the residual lives about hollow axles which are the reference values of examine and repair limit of the hollow axle is given.

Investigation of cost-effectiveness of highway asphalt pavement maintenance treatments based on rutting development analysis

To investigate the cost-effectiveness of different maintenance treatments of highways in Jiangsu Province, the historical pavement maintenance records, traffic load information and pavement performance data in the pavement management system （PMS） are recorded and analyzed. Compared with the growth model, the linear model, the logarithm model and the exponential model, the cubic model has higher regression accuracy R2 and it can capture the sigmoid shape of the deterioration curve. So it is selected to simulate the pavement rotting development. The benefit over cost ratio is calculated to quantify the treatment cost- effectiveness. The analysis results show that thin hot mix asphalt （HMA） overlays and micro surfacing are more cost- effective than the. other two treatments on light and moderate traffic roads. Hot in-place recycling and thick HMA overlays have much longer service lives and greater cost-effectiveness under heavy or extra heavy traffic.

Hierarchical methodology to evaluate the quality of disparate axle load data sources for pavement design

Axle load data are an essential input for pavement design,yet for most North American agencies,there is uncertainty about the quality of axle load data obtained from weigh-inmotion(WIM)systems,the applicability of these data for pavement design,and potential opportunities to integrate axle load data from disparate sources.This article presents a novel and practical methodology to evaluate the quality of axle load data from WIM systems and roadside weigh scales through a series of hierarchical analyses designed to test data validity.When applied using data from Manitoba,Canada,the methodology quantified the uncertainty of axle loads measured at the weigh scales and piezo-quartz WIM,concluding that both could be used for pavement design applications.Data collected at piezo-polymer WIM sites exhibited poorer data validity;however,application of site-specific temperature correction factors significantly improved data validity at these sites.The article describes how other data quality dimensions,including spatial coverage,temporal coverage,and long-term data availability,could be considered when determining the suitability of disparate axle load data sources for pavement design.Application of the methodology enables a pragmatic evaluation of the quality and limitations of commonlyavailable axle load data,revealing uncertainties and data needs relevant for pavement design practice.

Use of regression trees to predict overweight trucks from historical weigh-in-motion data

The traffic of overloaded trucks is a critical problem in highways.It affects pavement performance life,reduces the service life of bridges,and has a negative impact on road safety,average speed and level of service.There are several practices to prevent the truck overloading issue,i.e.,enforcement activities to verify the truck’s compliance with the legal weight limits.This paper investigates the development of a method that uses available weigh-in-motion(WIM)data to identify overloaded truck weight and travel patterns.The proposed approach is based on regression trees method,a simple and easily understandable analytic tool used to build prediction models from a large set of data.An overall analysis of the overloaded truck regression tree model shows that the most important variable to classify and predict overloading is the truck type.Regarding the axle overloading,the most significant variable is the time of the day(most of the overloaded trucks travel at late night or early morning).The regression tree results can be used to optimize the efficiency of administration activities by planning truck enforcement operations based on the more critical scenarios.Also,the results improve the knowledge about the load characteristics of trucks,which can lead to more effective pavement management systems and more assertive pavement structure designs.