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Neural network-based model for prediction of permanent deformation of unbound granular materials

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Version 2 2022-08-16, 01:15
Version 1 2021-01-17, 14:41
journal contribution
posted on 2022-08-16, 01:15 authored by A Alnedawi, R Al-Ameri, Kali NepalKali Nepal
Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation (PD) of unbound granular materials (UGMs), which make these methods more conservative. In addition, there are limited regression models capable of predicting the PD under multi-stress levels, and these models have regression limitations and generally fail to cover the complexity of UGM behaviour. Recent researches are focused on using new methods of computational intelligence systems to address the problems, such as artificial neural network (ANN). In this context, we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads. Extensive repeated load triaxial tests (RLTTs) were conducted on base and subbase materials locally available in Victoria, Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks. Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix. The ANN model consists of one input layer with five neurons, one hidden layer with twelve neurons, and one output layer with one neuron. The five inputs were the number of load cycles, deviatoric stress, moisture content, coefficient of uniformity, and coefficient of curvature. The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%. It shows that the ANN method is rapid and efficient to predict the PD, which could be implemented in the Austroads pavement design method.

History

Volume

11

Issue

6

Start Page

1231

End Page

1242

eISSN

2589-0417

ISSN

1674-7755

Publisher

Elsevier BV

Additional Rights

CC BY-NC-ND 4.0

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2019-03-07

External Author Affiliations

Deakin University

Era Eligible

  • Yes

Journal

Journal of Rock Mechanics and Geotechnical Engineering