Since the 20th century, the rail network’s growth has been significantly in-creased worldwide to support passenger demands. One key to success is the safe service from the rail network, which shows a lower number than other public transportations. Nevertheless, many rail authorities have significantly increased safety level and reduced risks for a passenger. The causes of rail-way accident happened from various factors; however, primary accidents re-lating to infrastructure failures caused excessive damage to train and peo-ple’s lives. All failures, which occurred in multi-parts of rail’s infrastructure (i.e., roadbed, track, rail bridge), could be the primary causes of train colli-sion and derailment. The overall goal of this study is to analysing uncertain-ties of railway accidents and, evaluating risk and resilience of rail’s infra-structure after occurring an accident. The outcomes are expected to provid-ing safety policies on the railway network. The research precisely conducts long-term global accident data sets, which related to infrastructure failures. The data sets are analysed by using Bayes’ and decision tree methods through Python programming. One practical advantage of the study illus-trates that the outcomes can apply to the railway networks’ safety, reliabil-ity, and maintenance policies. Also, the research leads to sustainability surge railway’s safety performances from avoiding infrastructure failures problems. As a result, the study reveals that the risk level from infrastructure failures shows at ‘high risk’ level that scored 18 of 32. Therefore, the research pro-vides a practical recommendation to railway authorities to increase the infra-structure’s safety level.
|Name||Lecture Notes in Civil Engineering|
|Conference||Virtual Conference on DISASTER RISK REDUCTION |
|Period||15/03/21 → 20/03/21|