JETI Admin2
Abstract
Monitoring the temperature-induced deformation of recycled polymer sleepers under service conditions is crucial for ensuring safe and seamless train operations. The development of a very robust deformation model of a rubber sleeper under ambient conditions in which data measurement stations are available can save time and cost for laborious experimentation. Thus, in this study, gene expression programming (GEP) was used to determine and predict the deformation of recycled composite sleepers in an ambient environment. To this end, several models have been developed and evaluated. The model with the highest coefficient of correlation (R Squared) and Nash Sutcliffe efficiency (NSE) and the lowest error metric, i.e., the root mean square error (RMSE) and mean absolute error (MAE), for both training and testing as well as the entire dataset was selected to best predict the deformation of rubber composite sleepers in the ambient environment. Thus, GEP models can be utilized to predict the deformation of recycled sleepers without the need for long-term stress experiments. The Prediction model attempted in the study is cardinal in predicting failures or maintenance needs of the rubber composite sleeper in service. This enables proactive maintenance schedules, reducing downtime and costs, thus, allowing for timely maintenance interventions by railway practitioners.
References
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