Three-dimensional tire-pavement contact stresses prediction by deep learning approach

The demand for fast and accurate tire-pavement contact modelling is becoming increasingly prevalent with the advancement of pavement design and finite-element modelling. This paper presents a tool for fast and accurate prediction of non-uniform tire-pavement contact stresses utilising deep learning....

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Bibliographic Details
Published in:The international journal of pavement engineering 2022-12, Vol.23 (14), p.4991-5002
Main Authors: Liu, Xiuyu, Al-Qadi, Imad L.
Format: Article
Language:English
Subjects:
Contact stresses
Deep learning
Finite element method
Machine learning
Mathematical models
Modelling
Multilayer perceptrons
Multilayers
neural network
Neural networks
Pavement design
Pavements
Polynomials
regression methods
Tire-pavement contact
Tires
truck tires
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Summary:The demand for fast and accurate tire-pavement contact modelling is becoming increasingly prevalent with the advancement of pavement design and finite-element modelling. This paper presents a tool for fast and accurate prediction of non-uniform tire-pavement contact stresses utilising deep learning. Two truck tires, under various wheel loading, inflation pressure, and slip ratio conditions, were considered. The developed deep learning model, ContactNet, is a deconvolutional neural network consisting of two fully connected layers, one reshape layer, and five deconvolution layers with millions of neurons. Two validated finite-element truck tire models were used to generate a contact stresses database with 1800 simulated results. The database was then used for training and testing of the ContactNet. The ContactNet resulted in average errors of 0.80%, 0.77%, 0.90%, and 0.57% in predicting maximum vertical stress, effective contact area, maximum longitudinal stress, and maximum transverse stress. The mean absolute error of the ContactNet prediction is 0.91 kPa. This significantly outperformed four conventional machine-learning regression methods investigated in this study, including polynomial regression, k-nearest neighbours, multi-layer perceptron, and random forests.
ISSN:1029-8436
1477-268X
DOI:10.1080/10298436.2021.1990288