Open this publication in new window or tab >>2026 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]
This thesis aims to improve the characterization of train-induced aerodynamic loads acting on railway vertical noise barriers and to advance the understanding of their dynamic behavior under realistic service conditions through an integrated approach combining numerical simulations, field measurements, and data-driven modelling. This will enhance the reliability of structural response prediction and support the long-term safety and sustainable design of these structures.
Railway noise barriers are important trackside structures designed to mitigate noise from passing trains to surrounding communities. However, train-induced aerodynamic effects generate significant fluctuating pressures on barrier surfaces, which can excite structural vibrations and accumulate fatigue damage over time, thereby threatening structural safety and serviceability. A comprehensive review of existing aerodynamic load models, together with comparative analyses against available field measurement data, indicates that current models are primarily formulated based on simplified relationships and exhibit limited applicability across different train types and barrier configurations. Moreover, systematic long-term field monitoring data reflecting the structural behavior under realistic service conditions remain scarce. Therefore, the aerodynamic load models and structural dynamic analysis methods currently used in design cannot adequately represent complex service conditions, particularly the combined effects of operating parameters and environmental variations. This limits the ability to accurately assess and predict the key responses and service performance of railway noise barriers.
To address these challenges, computational fluid dynamics (CFD) simulations, validated against field test data, were conducted to systematically quantify the effects of train nose geometry, barrier height, and the layout of vertical noise barriers on train-induced aerodynamic pressure. An enhanced aerodynamic pressure model incorporating both train and barrier parameters was thereby developed. Dynamic finite element analyses (FEA) under idealized boundary conditions were further performed to evaluate the influence of aerodynamic pressure pulse shapes on the dynamic response of vertical railway noise barriers. A simplified load input method suitable for numerical analysis was developed, enabling parametric investigation of the effects of key structural parameters on dynamic response and amplification.
Using the noise barrier along the Arlanda railway line in Stockholm, Sweden as a case study, full-scale field measurements were employed to analyze the actual structural responses under different train speeds and train types. Furthermore, long-term field monitoring data were combined with interpretable machine learning (ML) techniques to establish a data-driven framework for analyzing the influence of environmental variations on aerodynamic pressure and structural dynamic response. Based on an Explainable Boosting Machine (EBM), the contributions of individual influencing factors to pressure and structural response were quantitatively identified, and simplified analytical models for predicting load and stress responses suitable for engineering design were developed. Finally, the integration of long-term field measurements, data-driven analytical models, and stress transfer relationships obtained from FEA also enabled a fatigue assessment procedure for evaluating the long-term performance of the steel posts supporting the noise barrier.
Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2026. p. 86
Series
Trafikverkets publikationerTrafikverkets forskningsportföljer
Keywords
Railway noise barriers, Train-induced aerodynamic loads, Dynamic behaviour, Numerical modelling, Field measurements, Data-driven modelling
National Category
Structural Engineering Infrastructure Engineering
Research subject
Structural Engineering; FOI-portföljer, Bygga
Identifiers
urn:nbn:se:trafikverket:diva-22141 (URN)978-91-8142-003-6 (ISBN)978-91-8142-004-3 (ISBN)
Public defence
2026-05-12, A117, Luleå University of Technology, Luleå, 13:00 (English)
Opponent
Supervisors
Funder
Swedish Transport Administration, TRV 2024/132497
Note
Dessa arbeten ingår också i doktorsavhandlingen men finns ej länkade i DiVA:
Design-oriented aerodynamic load and stress calculation models for railway noise barriers using interpretable machine learning
Fatigue assessment of railway noise barriers based on field monitoring, ML-driven stress prediction and numerical modeling
2026-04-272026-04-272026-04-28Bibliographically approved