TY - GEN
T1 - A Back-Analysis Technique for Condition Assessment of Ballasted Railway Tracks
AU - Fathi, Shadi
AU - Mehravar, Moura
PY - 2022
Y1 - 2022
N2 - Track substructure is a key component of railway transportation systems. Similar to the built environment of other surface transportation systems, track substructures are subjected to ageing and deterioration. This frequently leads to failure and collapse of systems and imposing costly repairs and maintenance. Further, limited knowledge about the substructure condition leads to employing inefficient, time-consuming, and expensive maintenance. As the importance of time and budget limitation, there is a need to develop more time and cost-efficient techniques for frequent condition assessment of the existing railway substructures. Falling weight deflectometer (FWD) is recognised as an effective non-destructive test (NDT) for surveying the ballasted railway substructures through the back-analysis process, including a forward analysis of track substructure and an optimisation method. This paper presents a novel hybrid back-analysis technique, including artificial neural network (ANN) and ant colony optimisation for the continuous domain (ACOR) to backcalculate substructure layer moduli of railway track. To this aim, a dynamic finite element (FE) model is developed to generate a reliable dataset which is covering various layer moduli for ANN training. ACOR is employed as an optimisation tool to optimise estimated layer moduli (ANN’s input). Furthermore, a validation study has been conducted using the developed FE model with back-analysed layer moduli values to evaluate the developed technique’s performance. The validation study results show that use of ANNs incorporates ACOR results in excellent performance and robustness of the developed back-analysis technique. The hybrid ANN-ACOR back-analysis technique is a computationally efficient method with no dependency on seed modulus values.
AB - Track substructure is a key component of railway transportation systems. Similar to the built environment of other surface transportation systems, track substructures are subjected to ageing and deterioration. This frequently leads to failure and collapse of systems and imposing costly repairs and maintenance. Further, limited knowledge about the substructure condition leads to employing inefficient, time-consuming, and expensive maintenance. As the importance of time and budget limitation, there is a need to develop more time and cost-efficient techniques for frequent condition assessment of the existing railway substructures. Falling weight deflectometer (FWD) is recognised as an effective non-destructive test (NDT) for surveying the ballasted railway substructures through the back-analysis process, including a forward analysis of track substructure and an optimisation method. This paper presents a novel hybrid back-analysis technique, including artificial neural network (ANN) and ant colony optimisation for the continuous domain (ACOR) to backcalculate substructure layer moduli of railway track. To this aim, a dynamic finite element (FE) model is developed to generate a reliable dataset which is covering various layer moduli for ANN training. ACOR is employed as an optimisation tool to optimise estimated layer moduli (ANN’s input). Furthermore, a validation study has been conducted using the developed FE model with back-analysed layer moduli values to evaluate the developed technique’s performance. The validation study results show that use of ANNs incorporates ACOR results in excellent performance and robustness of the developed back-analysis technique. The hybrid ANN-ACOR back-analysis technique is a computationally efficient method with no dependency on seed modulus values.
KW - Ant colony optimisation
KW - Back-analysis
KW - Hybrid neural network
KW - Railway
UR - http://www.scopus.com/inward/record.url?scp=85113271131&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007/978-3-030-77234-5_76
UR - https://ictg2021.vfairs.com/
U2 - 10.1007/978-3-030-77234-5_76
DO - 10.1007/978-3-030-77234-5_76
M3 - Conference publication
AN - SCOPUS:85113271131
SN - 9783030772338
T3 - Lecture Notes in Civil Engineering
SP - 931
EP - 941
BT - Advances in Transportation Geotechnics IV - Proceedings of the 4th International Conference on Transportation Geotechnics
A2 - Tutumluer, Erol
A2 - Nazarian, Soheil
A2 - Al-Qadi, Imad
A2 - Qamhia, Issam I. A.
PB - Springer
T2 - 4th International Conference on Transportation Geotechnics, ICTG 2021
Y2 - 23 May 2021 through 26 May 2021
ER -