VEqMon2D—Equations of motion generation tool of 2D vehicles with Matlab

Cantero, Daniel

doi:10.1016/j.softx.2022.101103

Cited by 8 publications

(3 citation statements)

References 10 publications

(16 reference statements)

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“…In this paper, a coupled Train-Track Interaction (TTI) model developed by Cantero et al [45,46] is employed. The TTI model consists of two sub-models-the train and the track (see Figure 1)-which interact and couple at the contact points.…”

Section: Numerical Modelingmentioning

confidence: 99%

A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train

Malekjafarian,

Sarrabezolles,

Khan

et al. 2023

Sensors

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In this paper, a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. An Artificial Neural Network (ANN) algorithm is developed that works with the energies of the train acceleration responses. A numerical model of a half-car train coupled with a track profile is employed to simulate the train vertical acceleration. The energy of acceleration signals measured from 100 traversing trains is used to train the ANN for healthy track conditions. The energy is calculated every 15 m along the track, each of which is called a slice. In the monitoring phase, the trained ANN is used to predict the energies of a set of train crossings. The predicted energies are compared with the simulated ones and represented as the prediction error. The damage is modeled by reducing the soil stiffness at the sub-ballast layer that represents hanging sleepers. A damage indicator (DI) based on the prediction error is proposed to visualize the differences in the predicted energies for different damage cases. In addition, a sensitivity analysis is performed where the impact of signal noise, slice sizes, and the presence of multiple damaged locations on the performance of the DI is assessed.

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Section: Numerical Modelingmentioning

confidence: 99%

A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train

Malekjafarian,

Sarrabezolles,

Khan

et al. 2023

Sensors

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show abstract

“…The main bodies are connected to the axle masses by spring and dashpot systems, while the axle masses are connected to the road profile using single springs representing the tyres. The vehicle model has a total of 8 independent Degrees Of Freedom (DOFs) and 1 dependent DOF because of the articulation between tractor and trailer [36,37]. The generic equation of motion of such a vehicle model can be represented as: (8) In Eq.…”

Section: Numerical Modellingmentioning

confidence: 99%

Vehicle Assisted Bridge Damage Assessment Using Probabilistic Deep Learning

Sarwar¹,

Cantero²

2022

SSRN Journal

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“…Thus, there is a need for a general way of modelling such vehicles, that can be automated. Indeed, this problem has been previously recognized by Cantero [24], but has yet to be addressed through a single comprehensive modelling methodology.…”

Section: Introductionmentioning

confidence: 99%