Prediction of railway track geometry deterioration using artificial neural networks: a case study for Turkish state railways

Abstract

The main goal of this paper is to model track geometry deterioration using a comprehensive field investigation gathered over a period of 2 years on approximately 180km of railway line. Artificial neural networks (ANNs) were adapted for this research. The railway line was divided into analytical segments (ASs). For each AS, the following data were collected: track structure, traffic characteristics, track layout, environmental factors, track geometry, and maintenance and renewal data. ANN models were developed for the main track geometry parameters and produced significant relationships between the variables. In addition, sensitivity analyses were performed to compute the importance of each predictor in determining the neural network. The obtained results proved that ANN may be an alternative method for predicting track geometry deterioration.