Combining AI and Machine Learning with Building Data Modeling to Detect and Locate Defects in Railway Infrastructure

Abstract

Owing to the elevated need for the railway system nowadays, the velocity and capacity of locomotives are inclined to
rise. The impact of the harsh climate is concurrently worsening. This leads to degrading railway infrastructures
(RI), resulting in flaws. Deficiencies may impact customer satisfaction and the operational security of the RI. Early
detection of faults in RI helps mitigate operational risks, save maintenance costs, and enhance
resource handling efficiency. This study integrated Artificial Intelligence (AI) and machine learning (ML) with
building data modeling (BDM) to identify and pinpoint faults in RI. The BDM model proposed in the research may
be completely integrated with ML and AI to identify problems in RI using the established process. The Convolutional
Neural Network (CNN) model yields the optimal results. The study's findings indicate that the amalgamation of BDM
with ML and AI is feasible and benefits the railway sector.