چکیده مقاله
In rail infrastructure, modelling the degradation prediction of rail track is an important step toward developing preventive maintenance strategies Using quality data anddatasets without the least error and noises can improve the accuracy and reliability of the models’ predictions In this study by the application of two outlier detection methods including Interquartile Range IQR and Median Absolute Deviation MAD , datasets required for the development of track degradation prediction models have been prepared In this research, data of tram track gauge from Melbourne’s tram system have been used Based on each outlier detection technique, different datasets have been created For prediction modelling of the tram track degradation, Artificial Neural Network ANN algorithm has been used The results of the study show that the models based on IQR and MAD filtered data can provide more reliable forecasts than the model based on the unfiltered data
کلیدواژهها
نویسندگان
Amir Falamarzi
Civil and Infrastructure Engineering Discipline, School of Engineering, RMIT University, Australia
Sara Moridpour
Holmesglen Institute, Victoria, Australia
Majidreza Nazem
Associate Professor at RMIT University, Melbourne, Australia;
Samira Cheraghi
Research scholar at Holmesglen Institute of Tafe, Melbourne, Australia
شیوه ارجاع
Falamarzi, Amir and Moridpour, Sara and Nazem, Majidreza and Cheraghi, Samira,1398,Improvement of Rail Track Degradation Prediction Models by Detecting Outliers and Enhancing the Dataset,The 18th International Conference on Traffic and Transportation Engineering,Tehran
ارائهشده در
مجموعه مقالات هجدهمین کنفرانس بین المللی مهندسی حمل و نقل و ترافیک6 اسفند 1398 · تهران