Integration of building information modeling (BIM) and artificial intelligence (AI) to detect combined defects of infrastructure in the railway system

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Colleges, School and Institutes


Due to the high demand for the railway system nowadays, the speed and load of rolling stocks tend to increase. At the same time, the effect of ex-treme climate is also more severe. These result in the deterioration of the railway infrastructure which causes defects to the railway infrastructure. De-fects can affect passenger comfort and operating safety of the railway sys-tem. Detecting defects of the railway infrastructure in the early stage of de-fect development can reduce the risk to the railway operation, cost of maintenance, and make the asset management more efficient. This study aims to apply Building Information Modeling (BIM) integrated with Artifi-cial Intelligence (AI) to develop the detection system of defects in railway infrastructure. In this study, dipped joint and settlement are used as exam-ples of combined defects in the railway infrastructure. To detect defects, AI techniques are applied. Deep Neural Network and Convolutional Neural Network are used to develop predictive models to detect defects in the rail-way infrastructure and rolling stock. The results of the study show that the developed models have the potential to detect defects with accuracies up to 99% and are beneficial for the asset management of the railway system in terms of risk management, passenger comfort, and cost-efficiency.

Bibliographic note

Not yet published as of 24/09/2021.


Original languageEnglish
Title of host publicationProceedings of VCDRR 2021
Publication statusAccepted/In press - 19 Mar 2021
EventVirtual Conference on DISASTER RISK REDUCTION : Civil Engineering for a Disaster Resilient Society -
Duration: 15 Mar 202120 Mar 2021

Publication series

NameLecture Notes in Civil Engineering
ISSN (Electronic)2366-2557


ConferenceVirtual Conference on DISASTER RISK REDUCTION
Abbreviated titleVCDRR2021