Journal ArticleDOI
Neural Network Modeling of Resilient Modulus Using Routine Subgrade Soil Properties
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TLDR
In this paper, Artificial Neural Network (ANN) models are developed to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design application, and a database is developed containing grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulation results for 97 soils from 16 different counties in Oklahoma.Abstract:
Artificial neural network (ANN) models are developed in this study to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design application. A database is developed containing grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulus results for 97 soils from 16 different counties in Oklahoma. Of these, 63 soils (development data set) are used in training, and the remaining 34 soils (evaluation data set) from two different counties are used in the evaluation of the developed models. A commercial software, STATISTICA 7.1, is used to develop four different feedforward-type ANN models: linear network, general regression neural network, radial basis function network, and multilayer perceptrons network (MLPN). In each of these models, the input layer consists of seven nodes, one node for each of the independent variables, namely moisture content (w) , dry density ( γd ) , plasticity index (PI), percent passing ...read more
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Journal ArticleDOI
Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties
Abstract: The Mechanistic–Empirical Pavement Design Guide (MEPDG) considers a hierarchical approach to determine the input values necessary for most design parameters. Level 1 requires site-specific measurem...
DissertationDOI
Modelling Stiffness and Shear Strength of Compacted Subgrade Soils
TL;DR: In this article, the stiffness and shear strength properties of compacted soils, collectively denoted as Ω in this thesis, fluctuate with moisture content changes that result from the influence of environmental factors such as the evaporation and infiltration.
Journal Article
Unconsolidated Undrained Shear Strength Of Remoulded Clays By Anns Technique
TL;DR: In this article, an attempt is made to predict unconsolidated undrained shear strength parameters cohesion and angle of shear resistance of remoulded clayey soils from basic soil parameters applying General Regression Neural Networks (GRNN) and multilayer perceptrons (MLPs) neural network techniques.
Book ChapterDOI
An Artificial Intelligence Approach to Predict the Resilient Modulus of Subgrade Pavement or Unbound Material
Journal ArticleDOI
An evolutionary computing approach for reducing bias in the dynamic modulus predictive models of hot mix asphalt
TL;DR: In this paper , Gene Expression Programming (GEP) based approach was proposed to reduce bias issues with dynamic modulus (E∗) predictive models for the hot mix asphalt.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Book
Self Organization And Associative Memory
TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
Book
Pattern recognition and neural networks
Brian D. Ripley,N. L. Hjort +1 more
TL;DR: Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks in this self-contained account.
Journal ArticleDOI
Artificial neural networks: a tutorial
TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.