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

https://doi.org/10.1016/j.jrmge.2019.03.005Get rights and content
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Abstract

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.

Keywords

Flexible pavement design
Unbound granular materials
Permanent deformation (PD)
Repeated load triaxial test (RLTT)
Prediction models
Artificial neural network (ANN)

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Ali Alnedawi obtained his BSc and MSc degrees in Highway and Transportation Engineering from AlMustansiriya University, Iraq, in 2005 and 2012, respectively. He worked as a civil engineer from 2005 to 2015, and he is currently a PhD student at Deakin University, Australia. His research interests include pavement engineering and management, and geotechnical engineering.

Peer review under responsibility of Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.