A decision support approach for condition-based maintenance of rails based on big data analysis
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ML can help improve operation of low-carbon modes, for example by reducing rail operations and maintenance costs and predicting track degradation.
“ML can also help to improve the operation of low-carbon modes, for example, by reducing the operations and maintenance costs of rail [383] and predicting track degradation [746].”
From Tackling climate change with machine learning (2023) · cites this paper as [383]
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
(2014) · Cited by 33,419 · see citing works
Beyond the hype: Big data concepts, methods, and analytics
Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226 · see citing works
Control of systems integrating logic, dynamics, and constraints
Bemporad, A., & Morari, M. (1999) · Automatica · doi:10.1016/s0005-1098(98)00178-2 · Cited by 3,048 · see citing works
Deep convolutional neural networks for detection of rail surface defects
Faghih‐Roohi, S., Hajizadeh, S., Núñez, A. A., Babuška, R., & Schutter, B. D. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727522 · Cited by 366 · see citing works
Self-citationLewis, R., & Olofsson, U. (2009) · Woodhead Publishing Limited eBooks · doi:10.1533/9781845696788 · Cited by 346 · see citing works
Recent applications of big data analytics in railway transportation systems: A survey
Ghofrani, F., He, Q., Goverde, R. M. P., & Liu, X. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.03.010 · Cited by 319 · see citing works
Analysis of Causes of Major Train Derailment and Their Effect on Accident Rates
Liu, X., Saat, M. R., & Barkan, C. P. L. (2012) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2289-20 · Cited by 273 · see citing works
Scheduling preventive railway maintenance activities
Budai, G., Huisman, D., & Dekker, R. (2005) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2602085 · Cited by 241 · see citing works
Improving rail network velocity: A machine learning approach to predictive maintenance
Li, H., Parikh, D., He, Q., Qian, B., Li, Z., Fang, D., & Hampapur, A. (2014) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2014.04.013 · Cited by 213 · see citing works
Self-citationAutomatic Detection of Squats in Railway Infrastructure
Molodova, M., Li, Z., Núñez, A. A., & Dollevoet, R. P. B. J. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2307955 · Cited by 213 · see citing works
Self-citationAn investigation into the causes of squats—Correlation analysis and numerical modeling
Li, Z., Zhao, X., Esveld, C., et al. (2008) · Wear · doi:10.1016/j.wear.2008.02.037 · Cited by 199 · see citing works
Self-citationDevelopment of a multi‐criteria hierarchical framework for maintenance performance measurement (MPM)
Parida, A., & Chattopadhyay, G. (2007) · Journal of Quality in Maintenance Engineering · doi:10.1108/13552510710780276 · Cited by 197 · see citing works
Data-driven optimization of railway maintenance for track geometry
Sharma, S. S., Cui, Y. H., He, Q., Mohammadi, R., & Li, Z. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.02.019 · Cited by 165 · see citing works
Self-citationPrincipal components analysis and track quality index: A machine learning approach
Lasisi, A., & Attoh-Okine, N. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.04.001 · Cited by 156 · see citing works
Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis
Fumeo, E., Oneto, L., & Anguita, D. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.07.321 · Cited by 154 · see citing works
The effect of slip ratio on the rolling contact fatigue property of railway wheel steel
MAKINO, T., Kato, T., & Hirakawa, K. (2011) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2011.08.014 · Cited by 142 · see citing works
Fuzzy logic for piping risk assessment (pfLOPA)
Markowski, A. S., & Mannan, M. S. (2009) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2009.06.011 · Cited by 139 · see citing works
A Big Data Analysis Approach for Rail Failure Risk Assessment
Jamshidi, A., Faghih‐Roohi, S., Hajizadeh, S., et al. (2017) · Risk Analysis · doi:10.1111/risa.12836 · Cited by 131 · see citing works
Self-citationXu, L., & Zhai, W. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2016.10.010 · Cited by 120 · see citing works
Squat growth—Some observations and the validation of numerical predictions
Li, Z., Dollevoet, R. P. B. J., Molodova, M., et al. (2010) · Wear · doi:10.1016/j.wear.2010.10.051 · Cited by 116 · see citing works
Self-citationLi, Z., Molodova, M., Núñez, A. A., & Dollevoet, R. P. B. J. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2389761 · Cited by 113 · see citing works
Self-citationCamastra, F., Ciaramella, A., Giovannelli, V., et al. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.09.041 · Cited by 105 · see citing works
Analysis of Derailments by Accident Cause
Liu, X., Barkan, C. P. L., & Saat, M. R. (2011) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2261-21 · Cited by 98 · see citing works
A Bayesian model to assess rail track geometry degradation through its life-cycle
Andrade, A. R., & Teixeira, P. F. (2012) · Research in Transportation Economics · doi:10.1016/j.retrec.2012.03.011 · Cited by 88 · see citing works
Squats and squat-type defects in rails: the understanding to date
Grassie, S. L. (2011) · Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · doi:10.1177/0954409711422189 · Cited by 86 · see citing works
Ultrasonic surface wave propagation and interaction with surface defects on rail track head
Fan, Y., Dixon, S., Edwards, R. S., et al. (2007) · NDT & E International · doi:10.1016/j.ndteint.2007.01.008 · Cited by 84 · see citing works
He, Q., Li, H., Bhattacharjya, D., et al. (2014) · Journal of the Operational Research Society · doi:10.1057/jors.2014.7 · Cited by 83 · see citing works
Uncertainty in Rail-Track Geometry Degradation: Lisbon-Oporto Line Case Study
Andrade, A. R., & Teixeira, P. F. (2011) · Journal of Transportation Engineering · doi:10.1061/(asce)te.1943-5436.0000206 · Cited by 73 · see citing works
Su, Z., Jamshidi, A. A., Núñez, A. A., Baldi, S., & Schutter, B. D. (2017) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2017.08.018 · Cited by 72 · see citing works
Self-citationDetection of Damage and Crack in Railhead by Using Eddy Current Testing
Song, Z., Yamada, T., Shitara, H., et al. (2011) · Journal of Electromagnetic Analysis and Application · doi:10.4236/jemaa.2011.312082 · Cited by 71 · see citing works
Optimal Clustering of Railroad Track Maintenance Jobs
Peng, F., & Ouyang, Y. (2013) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12036 · Cited by 70 · see citing works
Automated vision inspection of rail surface cracks: A double-layer data-driven framework
Li, Z., Wang, L., Zhang, Z., & Tsui, K. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.05.007 · Cited by 70 · see citing works
Self-citationNoncontact ultrasonic guided wave inspection of rails
Mariani, S., Nguyen, T. V., Phillips, R., et al. (2013) · Structural Health Monitoring · doi:10.1177/1475921713498533 · Cited by 68 · see citing works
Track maintenance production team scheduling in railroad networks
Peng, F., & Ouyang, Y. (2012) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2012.07.004 · Cited by 64 · see citing works
Maintenance performance indicators (MPIs) for benchmarking the railway infrastructure
Åhrén, T., & Parida, A. (2009) · Benchmarking An International Journal · doi:10.1108/14635770910948240 · Cited by 61 · see citing works
Influence of Short-Pitch Wheel/Rail Corrugation on Rolling Contact Fatigue of Railway Wheels
Nielsen, J. C. O., Ekberg, A., & Lundén, R. (2005) · Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · doi:10.1243/095440905x8871 · Cited by 61 · see citing works
Incident Duration Prediction with Hybrid Tree-based Quantile Regression
He, Q., Kamarianakis, Y., Jintanakul, K., & Wynter, L. (2013) · Complex networks and dynamic systems · doi:10.1007/978-1-4614-6243-9_12 · Cited by 59 · see citing works
Optimization of preventive condition-based tamping for railway tracks
Wen, M., Li, R., & Salling, K. B. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.01.024 · Cited by 59 · see citing works
Fundamentals of Robotics: Linking Perception to Action
Xie, M. (2003) · CERN Document Server (European Organization for Nuclear Research) · Cited by 49 · see citing works
Actual Data Analysis of Alignment Irregularity Growth and its Prediction Model
Kawaguchi, A., Miwa, M., & Terada, K. (2005) · Quarterly Report of RTRI · doi:10.2219/rtriqr.46.262 · Cited by 45 · see citing works
Classification of Automatic Radar Plotting Aid targets based on improved Fuzzy C-Means
Ma, F., Wu, Q., Yan, X., et al. (2014) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2014.12.001 · Cited by 43 · see citing works
Tractive effort, curving and surface damage of rails
Grassie, S. L., & Elkins, J. A. (2004) · Wear · doi:10.1016/j.wear.2004.03.064 · Cited by 42 · see citing works
MAKINO, T., Neishi, Y., SHIOZAWA, D., et al. (2016) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2016.02.015 · Cited by 40 · see citing works
Big Data and Differential Privacy
Attoh-Okine, N. (2017) · Wiley eBooks · doi:10.1002/9781119229070 · Cited by 40 · see citing works
Experimental evaluation of rolling contact fatigue in railroad wheels
Sciammarella, C. A., Chen, R. J. S., Gallo, P., et al. (2016) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2016.05.035 · Cited by 37 · see citing works
TRACK-QUALITY INDICES AND TRACK-DEGRADATION MODELS FOR MAINTENANCE-OF-WAY PLANNING
Hamid, A., & Gross, A. (1981) · Transportation Research Record Journal of the Transportation Research Board · Cited by 30 · see citing works
Jamshidi, A., Núñez, A. A., Dollevoet, R. P. B. J., et al. (2017) · Journal of Infrastructure Systems · doi:10.1061/(asce)is.1943-555x.0000357 · Cited by 26 · see citing works
Self-citationDutch research results on wheel/rail interface management: 2001–2013 and beyond
Zoeteman, A., Dollevoet, R. P. B. J., & Li, Z. (2014) · Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · doi:10.1177/0954409714524379 · Cited by 25 · see citing works
Self-citationRail inspection of RCF defects
Popović, Z., Radović, V., Lazarević, L., et al. (2013) · GraFar (University of Belgrade, Faculty of Civil Engineering) · Cited by 25 · see citing works
A railway maintenance scheduling problem with customer costs
Heinicke, F., Simroth, A., Scheithauer, G., et al. (2014) · EURO Journal on Transportation and Logistics · doi:10.1007/s13676-014-0071-3 · Cited by 24 · see citing works
Cost effective future derailment mitigation techniques for rail freight traffic management in Europe
Islam, D. M. Z., Laparidou, K., & Burgess, A. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.06.017 · Cited by 22 · see citing works
Strategic Model to Optimize Railway-Track Renewal Operations at a Network Level
Caetano, L. F., & Teixeira, P. F. (2016) · Journal of Infrastructure Systems · doi:10.1061/(asce)is.1943-555x.0000292 · Cited by 22 · see citing works
DEVELOPMENT OF RAILROAD TRACK DEGRADATION MODELS
Bing, A. J., & Gross, A. (1983) · Transportation Research Record Journal of the Transportation Research Board · Cited by 19 · see citing works
Li, Z. (2009) · Elsevier eBooks · doi:10.1533/9781845696788.1.409 · Cited by 19 · see citing works
Self-citationTosun, M., Dinçer, K., & Başkaya, Ş. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.10.081 · Cited by 16 · see citing works
Vo, K., Zhu, H., Tieu, A. K., et al. (2015) · Wear · doi:10.1016/j.wear.2015.02.024 · Cited by 14 · see citing works
Heuristic Analysis of the Effective Range of a Track Tamping Machine
Santos, R. M., & Teixeira, P. F. (2011) · Journal of Infrastructure Systems · doi:10.1061/(asce)is.1943-555x.0000081 · Cited by 14 · see citing works
Risk-Based Optimization of Rail Defect Inspection Frequency for Petroleum Crude Oil Transportation
Liu, X., & Dick, C. T. (2016) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2545-04 · Cited by 14 · see citing works
Simulation study of thermally initiated rail defects
Scott, D. K., Fletcher, D. I., & Cardwell, B. (2012) · Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · doi:10.1177/0954409712465697 · Cited by 12 · see citing works
PERFORMANCE-BASED TRACK-QUALITY MEASURES AND THEIR APPLICATION TO MAINTENANCE-OF-WAY PLANNING
Corbin, J. C., & Fazio, A. E. (1981) · Transportation Research Record Journal of the Transportation Research Board · Cited by 7 · see citing works
Rolling contact fatigue in railway wheels under high axle loads
Mutton, P. J., Epp, C., & Dudek, J. (1990) · Elsevier eBooks · doi:10.1016/b978-0-444-88774-0.50013-4 · Cited by 7 · see citing works
Evaluating Classification Performance with only Positive and Unlabeled Samples
Hajizadeh, S., Li, Z., Dollevoet, R. P. B. J., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44415-3_24 · Cited by 6 · see citing works
Self-citationWang, L., Wang, P., Quan, S., et al. (2012) · 西南交通大学学报：英文版 · doi:10.1007/bf03325792 · Cited by 6 · see citing works
INNOVATIVE MEASURING SYSTEM UNVEILED
Zywiel, J. Z., & Oberlechner, G. (2001) · International railway journal · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Veit, P., 2007. Track quality-luxury or necessity. In: Railway Technical Review Special: Maintenance and Renewal, pp. 8-12.
Cited by
Sorted by citations
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Jamshidi, A., Hajizadeh, S., Su, Z., Naeimi, M., Núñez, A., Dollevoet, R., De Schutter, B., & Li, Z.
- Affiliations
- Delft University of Technology (NL)
- Corresponding author
- Núñez, Alfredo
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 95
- Pages
- 185-206
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2884826024
- Cited by
- 86
- References
- 66
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 9 · 2023: 7 · 2022: 20 · 2021: 7 · 2020: 7 · 2019: 10
- FWCI
- 3.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- NWO/ProRail project (438-12-300)
- Ministry of Economic Affairs (12235)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Railway Engineering and Dynamics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Transportation Systems and Safety
- Keywords
- condition-based maintenance, rails, expert systems, axle box acceleration, rail surface defects, video image analysis, big data, railway infrastructure management, decision support
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