Author(s):
Ramos, Ana ; Correia, A. Gomes ; Nasrollahi, Kourosh ; Nielsen, Jens C. O. ; Calçada, Rui
Date: 2024
Persistent ID: https://hdl.handle.net/1822/96001
Origin: RepositóriUM - Universidade do Minho
Project/scholarship:
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04029%2F2020/PT;
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04708%2F2020/PT;
info:eu-repo/grantAgreement/EC/H2020/826255/EU;
info:eu-repo/grantAgreement/EC/H2020/101012456/EU;
Subject(s): Machine learning algorithms; Permanent deformation in railways; Prediction; Infrastructure; Life-cycle management; Validation; Indústria, inovação e infraestruturas; Engenharia e Tecnologia::Engenharia Civil
Description
To enhance track geometry maintenance planning and reduce infrastructure costs, accurate predictions of accumulated permanent track deformation (settlement) caused by cyclic loading of ballast and subgrade is crucial for railway infrastructure managers. This paper proposes a novel approach to predict long-term settlement with reduced computational cost, based on an extensive parameter study using a hybrid methodology to evaluate both short- and long-term track performance. Various machine learning techniques are compared and employed to develop predictive models, which are validated using measured results from a filed demonstrator of ballasted track. The performance and accuracy of each model are assessed using multiple metrics, and a sensitivity analysis is conducted to identify influential explanatory variables. Notably, the developed random forest model demonstrates good agreement with field measured settlement data. This approach bridges the gap between numerical simulation and empirical data, offering an improved holistic understanding of permanent track deformation. The methodology holds potential for implementation in a computational decision support system for railway track maintenance and renewal management.
This work was partially carried out under the framework of In2Track3, a research project of Shift2Rail. This work was also partly financed by FCT / MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020. It has also been financially supported by: Base Funding – UIDB/04708/2020 of the CONSTRUCT – Institute of R&D in Structures and Construction – funded by national funds through the FCT/MCTES (PIDDAC). The field validation chapter (Section 6) is part of the ongoing activities in CHARMEC – Chalmers Railway Mechanics (www.chalmers.se/charmec). Here, parts of the study have been funded from the European Union's Horizon 2020 research and innovation programme in the projects In2Track2 and In2Track3 under grant agreements Nos 826255 and 101012456.
