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 Experimental Vibration Analysis for Civil Engineering Structures tenutosi a Milano) [10.1007/978-3-031-39117-0_9].

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

Quaranta, Giuseppe;
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
Experimental Vibration Analysis for Civil Engineering Structures
Bouncing · Braking · Bridge · Fragility curve · Genetic programming · Machine learning · Moving load · Neural network · Pitching · Roughness · Surrogate model · Vehicle-bridge interaction
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
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 Experimental Vibration Analysis for Civil Engineering Structures tenutosi a Milano) [10.1007/978-3-031-39117-0_9].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1689576
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