Railway track quality, safety and reliability through traintrack dynamics and advanced machine learning - Research Questions, Methods and Conclusions

Aug 3, 2026

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The presentation examines how railway track characteristics, quality, and reliability affect overall system performance and how dynamic operating conditions change the key influencing factors. It evaluates the value of AI for enhancing track performance and enabling real-time determination of track conditions using train-track dynamics and sensory data. Experimental results show strong performance for both standard CNN and dilated CNN, with the dilated approach achieving the best R2 on the longest samples. The study concludes that irregularities do not significantly affect performance and supports practical deployment via smartphone applications and possible wireless accelerometers on service trains.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages2

Reading Time5 min

AccessFree to download

Research Questions

Contextual Background

Methods

Results

The Findings in Context

Conclusions

Key Publications

University of Birmingham Railway track quality, safety and reliability through train-track dynamics and advanced machine learning Huang, Junhui; Kaewunruen, Sakdirat License: Creative Commons: Attribution (CC BY) Citation for published version (Harvard): Huang, J & Kaewunruen, S 2023, 'Railway track quality, safety and reliability through train-track dynamics and advanced machine learning', Clarke Lecture 2023, Birmingham, United Kingdom, 22/06/23-22/06/23 . Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. •Users may freely distribute the URL that is used to identify this publication. •Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. •User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) •Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive. If you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate. Download date: 02. Aug. 2026 Railway track quality, safety & reliability through traintrack dynamics and advanced machine intelligence Junhui Huang, Dr Sakdirat Kaewunruen, School of Engineering Research Questions 1. How do characteristics, quality and reliability of railway tracks impact system performance? 2. What are the key factors influencing the tracks in dynamic operational conditions? 3. What benefits of leveraging AI in enhancing the performance of track? 4. How can AI be applied to determine real-time track conditions? Contextual Background Railway tracks are subjected to dynamic forces and environmental factors. AI can be used to monitor traintrack Methods Results 1. Both the performance of the standard CNN and dilated CNN are above 0 .90 . 2. Considered factors: two different model types, three sample size, irregularities included. 3. Dilated CNN produces the best R2 of 0 .97 on the longest sample size. The Findings in Context New mobile-phone App for Real-time Ride Comfort Assessment AI models provide satisfied performance Fast calculation with full technical robustness Easy to implement and to connect real-time via 5G Reducing cost, and Empowering passengers New method Robust Easy to use Accurate Conclusions 1. We have proposed a dilated CNN using the sensory data acquired from axle box which is immune to speed up to 120 km/h and bad weather conditions as there are sophisticated accelerometers designed to tackle the extreme weather conditions. 2. We have confirmed that the performance of the proposed model is highly satisfied, the dilated technique contributes to saving computational cost, and the consideration of irregularities makes no significant difference to the model's performance. 3. An implication of this study is the possibility that wireless accelerometers can be mounted onboard service trains. 4. New smartphone App has been developed to assess real-time ride comfort using machine intelligence. Key

FAQs

How do the presentation’s research questions connect track quality to system performance?

They frame track characteristics, quality, and reliability as drivers of system performance, then ask how dynamic operational conditions affect those track-related factors.

Which AI methods are evaluated, and what performance outcome is reported?

The work compares a standard CNN and a dilated CNN, reporting both exceed 0.90, while the dilated CNN achieves the best R2 (0.97) on the longest sample size.

What practical applications and implications does the presentation propose?

It highlights a new mobile app for real-time ride comfort assessment and suggests wireless accelerometers could be mounted on onboard service trains to enable monitoring and comfort evaluation.

Aug 3, 2026

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