The Impact of Climate Change on the Geometry of Railway Tracks in Iran: A Deep Learning Approach

Climate is one of the most important factors in railway infrastructure, and future changes in climate and extreme weather conditions will increase the risks concerning its stability and performance. Understanding and mitigating these risks is vital for ensuring the longevity and safety of railway networks. The research work presented here examines the association of climatic conditions with the geometric degradation of railway lines in Iran using Deep Learning models. The research is based on the geometric data obtained from track measurement machines over ۱۴ years, encompassing track gauge, profile, and twist. These data were matched with meteorological records such as temperature, humidity, rainfall, and wind speed for different regions of Iran. By combining these datasets, deep learning models are developed to analyze and predict the pattern of track degradation resulting from various climatic conditions. The added value of using meteorological data in training predictive models is assessed by comparing the performance of models trained with and without meteorological data. More precisely, one model predicts track degradation without considering meteorological data, while the other includes climatic information. This comparison allows for an evaluation of the effectiveness of weather data in improving the accuracy of track degradation predictions. This research is, therefore, likely to contribute to critical knowledge of how climatic variables influence railway infrastructure, allowing for more realistic forecasts of track life and better planning for maintenance schedules. Given the challenges associated with climate change, the present study addresses the call to develop more resilient railway infrastructure for operational safety across varied climatic conditions.

کلیدواژه ها:

degradation of railway track ، meteorological data ، deep learning ، climate impacts