High-Speed Railway (HSR) technology requires the deployment of noise barriers to mitigate noise pollution affecting nearby residents. As train speeds increase, so does the magnitude of aerodynamic effects such as aerodynamic noise and the pressure on these barriers, meaning that these structures require robust sound insulation and structural load-bearing capacities. Train-induced aerodynamic loads must therefore be accounted for in the structural design of HSR noise barriers, and accurate characterization of these loads is vital for ensuring noise barrier performance and safety.

Current European standards primarily evaluate aerodynamic loads on noise barriers based on train speed and the distance to the track centre. However, geometric differences between high-speed trains (HSTs) from different countries and regions necessitate the validation and potential revision of existing load calculation models. This thesis aims to enhance the characterization of train-induced aerodynamic pressure on HSR noise barriers and develop more accurate models for its calculation, focusing on the most common barrier type—vertical noise barriers.

Initially, a thorough literature review was conducted to assimilate current knowledge on this topic and pinpoint existing gaps and challenges. Multiple factors including the geometric properties of trains and the heights of noise barriers were then analysed using computational fluid dynamics (CFD) simulations to evaluate their impact on the train-induced aerodynamic pressure on vertical noise barriers. Finally, the suitability of existing pressure calculation models was evaluated using literature data and a modified calculation model building on the EN 14067-4 model was developed. 

A key finding is that the general applicability of existing pressure calculation models is limited because of the wide variation in HST geometries and noise barrier heights. The amplitude of train-induced aerodynamic pressure on vertical noise barriers increases with train height and width but decreases as nose length increases. While taller noise barriers experience greater aerodynamic pressures, the in-crease in pressure with barrier height is not significant. The proposed modified pressure calculation model that accounts for train geometry and the height distribution coefficient predicts the train-induced aerodynamic pressure on vertical noise barriers more accurately than existing models and could thus improve the structural design and safety of HSR noise barriers across a wide range of conditions.

Höghastighetsjärnvägsteknik (HSR) kräver installation av bullerskärmar för att begränsa bullerföroreningar som påverkar närboende. I takt med att tåghastigheterna ökar, ökar även omfattningen av aerodynamiska effekter såsom aerodynamiskt buller och tryckbelastning på bullerskärmarna, vilket innebär att dessa konstruktioner måste uppfylla höga krav både avseende ljudisolering och bärförmåga. Tågalstrade aerodynamiska laster måste därför beaktas i den konstruktiva dimensioneringen av bullerskärmar längs höghastighetsjärnvägar, och en noggrann karakterisering av dessa laster är avgörande för att säkerställa bullerskärmarnas funktion och säkerhet.

Nuvarande europeiska standarder utvärderar huvudsakligen aerodynamiska laster på bullerskärmar baserat på tåghastighet och avstånd till spårmitt. Emellertid innebär geometriska skillnader mellan höghastighetståg (HST) från olika länder och regioner att befintliga lastberäkningsmodeller behöver valideras och i vissa fall revideras. Syftet med denna avhandling är att förbättra karakteriseringen av tågalstrat aerodynamiskt tryck på bullerskärmar längs höghastighetsjärnvägar samt att utveckla mer noggranna beräkningsmodeller för detta ändamål, med fokus på den vanligaste bullerskärmstypen – vertikala bullerskärmar.

Inledningsvis genomfördes en omfattande litteraturöversikt för att sammanställa befintlig kunskap inom området och identifiera nuvarande kunskapsluckor och utmaningar. Därefter analyserades flera faktorer, däribland tågens geometriska egenskaper och bullerskärmarnas höjd, med hjälp av numeriska simuleringar baserade på computational fluid dynamics (CFD), för att utvärdera deras inverkan på det tågalstrade aerodynamiska trycket på vertikala bullerskärmar. Slutligen bedömdes lämpligheten hos befintliga tryckberäkningsmodeller med hjälp av data från litteraturen, och en modifierad beräkningsmodell baserad på EN 14067-4 utvecklades.

Ett centralt resultat är att den generella tillämpbarheten hos befintliga tryckberäkningsmodeller är begränsad på grund av den stora variationen i både höghastighetstågens geometri och bullerskärmarnas höjd. Amplituden hos det tågalstrade aerodynamiska trycket på vertikala bullerskärmar ökar med ökande tåghöjd och tågbredd, men minskar med ökad noslängd. Även om högre bullerskärmar utsätts för större aerodynamiska tryck, är ökningen av trycket med skärmhöjd relativt begränsad. Den föreslagna modifierade tryckberäkningsmodellen, som tar hänsyn till tågens geometri samt en höjdfördelningskoefficient, ger en mer noggrann prediktion av det tågalstrade aerodynamiska trycket på vertikala bullerskärmar än befintliga modeller och kan därmed bidra till förbättrad konstruktiv dimensionering och ökad säkerhet för bullerskärmar längs höghastighetsjärnvägar under ett brett spektrum av förhållanden.

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.