TY - GEN
T1 - Integration of building information modeling (BIM) and artificial intelligence (AI) to detect combined defects of infrastructure in the railway system
AU - Sresakoolchai, Jessada
AU - Kaewunruen, Sakdirat
PY - 2021/10/29
Y1 - 2021/10/29
N2 - 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 extreme climate is also more severe. These result in the deterioration of the railway infrastructure which cause defects to the railway infrastructure. Defects can affect passenger comfort and operating safety of the railway system. Detecting defects of the railway infrastructure in the early stage of defect 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 artificial intelligence (AI) to develop the detection system of defects in railway infrastructure. In this study, dipped joint and settlement are used as examples 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 railway 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.
AB - 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 extreme climate is also more severe. These result in the deterioration of the railway infrastructure which cause defects to the railway infrastructure. Defects can affect passenger comfort and operating safety of the railway system. Detecting defects of the railway infrastructure in the early stage of defect 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 artificial intelligence (AI) to develop the detection system of defects in railway infrastructure. In this study, dipped joint and settlement are used as examples 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 railway 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.
KW - Artificial intelligence
KW - Building information modeling
KW - Dipped joint
KW - Railway defects
KW - Railway infrastructure
KW - Settlement
UR - https://www.springer.com/series/15087?detailsPage=titles
UR - https://vcdrr.nitk.ac.in/
UR - https://doi.org/10.1007/978-981-16-6978-1
U2 - 10.1007/978-981-16-6978-1_30
DO - 10.1007/978-981-16-6978-1_30
M3 - Conference contribution
SN - 9789811669774
T3 - Lecture Notes in Civil Engineering
SP - 377
EP - 386
BT - Resilient Infrastructure
A2 - Kolathayar, Sreevalsa
A2 - Ghosh, Chandan
A2 - Adhikari, Basanta Raj
A2 - Pal, Indrajit
A2 - Mondal, Arpita
PB - Springer Nature
T2 - Virtual Conference on DISASTER RISK REDUCTION
Y2 - 15 March 2021 through 20 March 2021
ER -