| dc.description.abstract | Thermal buckling is one of the most critical concerns regarding railroad
management, as it poses several dangers to railway transportation and the economy,
especially in high-temperature conditions. Owing to the increasing demands for
railway transportation caused by explosive urbanization, thermal buckling is also a
significant concern in Vietnam. In conjunction with the increasingly profound effect
of climate change, thermal buckling poses a severe threat to railroad transportation
safety in Vietnam. Several conventional methods (e.g., improve rail quality and
change rail shape) were used to prevent thermal buckling. However, they are costly,
hard to implement for a whole railway system and they are harmful for the
environment. Therefore, this thesis aims to utilize the Statistical Analysis model,
especially the Seasonal autoregressive integrated moving average (SARIMA) model
to develop a rail-temperature-prediction-model (RTPM) and predict the variation of
railroad temperature with time. This model was trained and validated on a monitored
database of an ongoing Metropolitan Railroad project. The performance of the
SARIMA model is evaluated through five standard evaluation metrics used for
appraising the accuracy and performance of a time series forecasting model. The
obtained results proved that the model achieved efficient performance and accuracy
values. Given the flexibility, simplicity, and high computational performance, the
proposed forecast model is expected to be a robust tool for accurately and fast
predicting railway temperature variations compared with other RTPMs. | en_US |
