The effect of vehicle braking can significantly amplify a bridge deflection compared to that induced by a vehicle moving at a constant speed. However, the magnitude of this amplification depends on vehicle bridge interaction (VBI) phenomena activated by the road roughness. The road roughness triggers the vehicle dynamics, thus magnifying the interaction between the vehicle and the bridge. This paper proposes a probabilistic model for the amplification factor. The amplification factor is associated with the vehicle’s hard braking by the mid-span of the bridge under different road roughness classes. The amplification factor, defined as the ratio between the maximum deflections corresponding to a vehicle braking and moving at a constant speed, is estimated as a function of the mass, velocity, natural frequency and damping of the vehicle. The VBI model is obtained by discretizing the coupled governing equations using the finite difference method. The vehicle is modelled as a two-degrees of freedom system corresponding to the bouncing and pitching motions. The computational efficiency of this model supported an expensive set of analyses, where the parameter values were selected using the Latin Hypercube sampling scheme. The model outputs have been validated against a middle-span bridge’s measured experimental displacement response under different scenarios.

Physics-Based and Machine-Learning Models for Braking Impact Factors / Aloisio, Angelo; Quaranta, Giuseppe; Contento, Alessandro; Rosso, MARCO MARTINO. - 433:(2023), pp. 81-88. (Intervento presentato al convegno International Conference on Experimental Vibration Analysis for Civil Engineering Structures tenutosi a Milan (Ita) nel 30 August - 1 September 2023) [10.1007/978-3-031-39117-0_9].

Physics-Based and Machine-Learning Models for Braking Impact Factors

Marco Martino Rosso
2023

Abstract

The effect of vehicle braking can significantly amplify a bridge deflection compared to that induced by a vehicle moving at a constant speed. However, the magnitude of this amplification depends on vehicle bridge interaction (VBI) phenomena activated by the road roughness. The road roughness triggers the vehicle dynamics, thus magnifying the interaction between the vehicle and the bridge. This paper proposes a probabilistic model for the amplification factor. The amplification factor is associated with the vehicle’s hard braking by the mid-span of the bridge under different road roughness classes. The amplification factor, defined as the ratio between the maximum deflections corresponding to a vehicle braking and moving at a constant speed, is estimated as a function of the mass, velocity, natural frequency and damping of the vehicle. The VBI model is obtained by discretizing the coupled governing equations using the finite difference method. The vehicle is modelled as a two-degrees of freedom system corresponding to the bouncing and pitching motions. The computational efficiency of this model supported an expensive set of analyses, where the parameter values were selected using the Latin Hypercube sampling scheme. The model outputs have been validated against a middle-span bridge’s measured experimental displacement response under different scenarios.
2023
978-3-031-39116-3
978-3-031-39117-0
File in questo prodotto:
File Dimensione Formato  
Aloisio Physics-Based and Machine-Learning Models for Braking Impact Factors.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 351.27 kB
Formato Adobe PDF
351.27 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
EVACES_2023_Full_paper_244.pdf

Open Access dal 30/08/2023

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 349.07 kB
Formato Adobe PDF
349.07 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2984676