scholarly journals Multi-Factor Modeling Method of the Load Sharing Ratio under Moving Train Loads

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yong Liu ◽  
Shiyu Zhang ◽  
Yang Jin ◽  
Yuxiang Song
Keyword(s):  
Prediction Model ◽  
Load Sharing ◽  
Railway Track ◽  
Loading Method ◽  
Vibration Prediction ◽  
Track System ◽  
Environmental Vibration ◽  
Factor Modeling ◽  
The Impact ◽  
Moving Train

In railway engineering, the load sharing ratio (LSR) is the ratio of the rail seat load (RSL) to the axle load, which is affected by many factors. The LSR can be used in the design and analysis of railway track structures as well as in the research of predicting the dynamic influence of railway tunnels and the environment. The “static loading method” commonly used to study the LSR does not conform to reality; using it, it is difficult to obtain a complete LSR curve, limiting its application. Besides, there is currently a lack of LSR prediction methods considering the impact of multiple factors. Therefore, this paper proposes a “moving loading method” for investigating the LSR under moving train excitation, verified to be rational by comparing with the experimental results. At the same time, a procedure for establishing the LSR multi-factor prediction model is put forward, namely, we (1) determine the LSR function form and the fitting algorithm; (2) perform parameter sensitivity analysis to determine the main influencing parameters of the LSR function; and (3) design a quadratic regression orthogonal test to obtain the prediction formula of the LSR function coefficients. Once establishing the prediction model for a type of train-track system, the LSR of similar systems can be calculated by adjusting the main parameters of the model. Shijiazhuang Metro Line 1 using the A-type vehicle and the monolithic trackbed is taken as a case study to develop a corresponding LSR multi-factor prediction model by the moving loading method and the procedure mentioned above. The results indicate that the proposed method performs well and can be adopted to enhance the accuracy of track design or tunnel and environmental vibration prediction.

Rail vehicle model motion analysis on curved track with vertical irregularity

2020 ◽  
Vol 130 ◽  
pp. 75-84
Author(s):  
Mirosław Dusza
Keyword(s):  
Contact Forces ◽  
Railway Track ◽  
Temperature Changes ◽  
Track System ◽  
Standards And Regulations ◽  
Vehicle Motion ◽  
Track Irregularity ◽  
Atmospheric Phenomena ◽  
The Impact ◽  
Track Deformation

The properties of a classic railway track largely depend on the properties of the sub-grade, which is most often a natural creation. Atmospheric phenomena (e.g. temperature changes, heavy rainfall) can locally reduce the elasticity of the subgrade and create conditions conducive to permanent track deformation. One of the most common forms of a track fragment destruction is the loss of foundation support (one or several neighbouring sleepers) resulting from the indentation of the ballast material in the subgrade. The pressure of a vehicle passing through a damaged section of the track causes the so-called dynamic track irregularity. The impact of dynamic track vertical irregularity on the values of wheel-rail contact forces of a passing vehicle was investigated. The model of the passenger wagon-track system was created using the VI-Rail tool. The vehicle motion on curves with different values of track radius and superelevations was investigated. Vertical track irregularities occur on the internal rail only. The lengths of the track irregularity correspond to one, two or three sleepers unsupported on one side. The test results are presented in the form of diagrams and referred to applicable standards and regulations.

An Investigation into the Dynamic Effects on the Track of Wheelflats on Railway Vehicles

1979 ◽  
Vol 21 (4) ◽  
pp. 287-297 ◽  
Author(s):  
S. G. Newton ◽  
R. A. Clark
Keyword(s):  
Experimental Data ◽  
Theoretical Models ◽  
Railway Track ◽  
Dynamic Effects ◽  
Railway Vehicles ◽  
Track Dynamics ◽  
Track System ◽  
Dynamic Forces ◽  
Mathematical Techniques ◽  
The Impact

Wheelflats on railway vehicles are created by wheelslide in braking: the resulting imperfection in the running line generates dynamic forces and stresses at each subsequent revolution. The authors describe this problem and refer to earlier work on this topic. A field experiment is described, in which an irregularity in the railhead was used to simulate a wheelflat for a range of vehicles, and loads and rail stresses were monitored. The structure and solution procedures of three theoretical models of the vehicle/track system are outlined and typical results are compared with the experimental data to establish the adequacy and limitations of each of the models. Although this paper is primarily concerned with railway track dynamics, the mathematical techniques described could be applied to any problem involving the impact loading of beams.

scholarly journals Effect of Train-Induced Wind on the Transmission of COVID-19: A New Insight into Potential Infectious Risks

2021 ◽  
Vol 18 (15) ◽  
pp. 8164
Author(s):  
Simin Zou ◽  
Xuhui He
Keyword(s):  
Social Distance ◽  
Quantitative Research ◽  
Virus Transmission ◽  
Passenger Safety ◽  
The Social ◽  
Gaussian Puff ◽  
The Impact ◽  
The Relationship ◽  
Fresh Insight ◽  
Moving Train

The unprecedented COVID-19 pandemic has caused a traffic tie-up across the world. In addition to home quarantine orders and travel bans, the social distance guideline of about six feet was enacted to reduce the risk of contagion. However, with recent life gradually returning to normal, the crisis is not over. In this research, a moving train test and a Gaussian puff model were employed to investigate the impact of wind raised by a train running on the transmission and dispersion of SARS-CoV-2 from infected individuals. Our findings suggest that the 2 m social distance guideline may not be enough; under train-induced wind action, human respiratory disease-carrier droplets may travel to unexpected places. However, there are deficiencies in passenger safety guidelines and it is necessary to improve the quantitative research in the relationship between train-induced wind and virus transmission. All these findings could provide a fresh insight to contain the spread of COVID-19 and provide a basis for preventing and controlling the pandemic virus, and probe into strategies for control of the disease in the future.

scholarly journals Micro-mechanical investigation of railway ballast behavior under cyclic loading in a box test using DEM: effects of elastic layers and ballast types

2019 ◽  
Vol 21 (4) ◽  
Author(s):  
Nishant Kumar ◽  
Bettina Suhr ◽  
Stefan Marschnig ◽  
Peter Dietmaier ◽  
Christof Marte ◽  
...  
Keyword(s):  
Cyclic Loading ◽  
Early Stage ◽  
Damage Model ◽  
Simulation Models ◽  
Railway Track ◽  
Maintenance Cost ◽  
Particle Movement ◽  
Railway Ballast ◽  
Elastic Layers ◽  
The Impact

Abstract Ballasted tracks are the commonly used railway track systems with constant demands for reducing maintenance cost and improved performance. Elastic layers are increasingly used for improving ballasted tracks. In order to better understand the effects of elastic layers, physical understanding at the ballast particle level is crucial. Here, discrete element method (DEM) is used to investigate the effects of elastic layers – under sleeper pad ($$\text {USP}$$USP) at the sleeper/ballast interface and under ballast mat ($$\text {UBM}$$UBM) at the ballast/bottom interface – on micro-mechanical behavior of railway ballast. In the DEM model, the Conical Damage Model (CDM) is used for contact modelling. This model was calibrated in Suhr et al. (Granul Matter 20(4):70, 2018) for the simulation of two different types of ballast. The CDM model accounts for particle edge breakage, which is an important phenomenon especially at the early stage of a tamping cycle, and thus essential, when investigating the impact of elastic layers in the ballast bed. DEM results confirm that during cyclic loading, $$\text {USP}$$USP reduces the edge breakage at the sleeper/ballast interface. On the other hand, $$\text {UBM}$$UBM shows higher particle movement throughout the ballast bed. Both the edge breakage and particle movement in the ballast bed are found to influence the sleeper settlement. Micro-mechanical investigations show that the force chain in deeper regions of the ballast bed is less affected by $$\text {USP}$$USP for the two types of ballast. Conversely, dense lateral forces near to the box bottom were seen with $$\text {UBM}$$UBM. The findings are in good (qualitative) agreement with the experimental observations. Thus, DEM simulations can aid to better understand the micro-macro phenomena for railway ballast. This can help to improve the track components and track design based on simulation models taking into account the physical behavior of ballast. Graphical Abstract

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

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