Di-RAIL Research Project


Journal Publications

  1. Ghiasi, R., Khan, M. A., Sorrentino, D., Diaine, C., & Malekjafarian, A. (2024). An unsupervised anomaly detection framework for onboard monitoring of railway track geometrical defects using one-class support vector machine. Engineering Applications of Artificial Intelligence133, 108167.DOI:
  2. Malekjafarian, A., Sarrabezolles, C. A., Khan, M. A., & Golpayegani, F. (2023). A machine-learning-based approach for railway track monitoring using acceleration measured on an in-service train. Sensors23(17), 7568.DOI: https://doi.org/10.3390/s23177568
  3. Ghiasi, R., & Malekjafarian, A. (2023). Feature subset selection in structural health monitoring data using an advanced binary slime mould algorithm. Journal of Structural Integrity and Maintenance8(4), 209-225. DOI:https://doi.org/10.1080/24705314.2023.2230398
  4. Malekjafarian, A., Khan, M. A., OBrien, E. J., Micu, E. A., Bowe, C., & Ghiasi, R. (2022). Indirect monitoring of frequencies of a multiple span bridge using data collected from an instrumented train: a field case study. Sensors22(19), 7468. DOI: https://doi.org/10.3390/s22197468

Conference Publications

  1. Ghiasi, R., & Malekjafarian, A. (2023, August). A Data-Driven Approach for Monitoring Railway Tracks Using Dynamic Responses Collected by an In-service Train. In International Conference on Experimental Vibration Analysis for Civil Engineering Structures (pp. 165-174). Cham: Springer Nature Switzerland.
  2. Khan, M. A., OBrien, E. J., McCrum, D., & Malekjafarian, A. (2022), “Bridge Global Damage Detection Using Direct Acceleration Data”, Civil Engineering Research in Ireland (CERI), Dublin, 2022.
  3. Ghiasi, R., & Malekjafarian, A. (2022), “An Advanced Binary Slime Mould Algorithm For Feature Subset Selection in Structural Health Monitoring Data”, Civil Engineering Research in Ireland (CERI), Dublin, 2022.