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
Modeling the Mechanical Behavior of Carbonate Sands Using Artificial Neural Networks and Support Vector Machines
TL;DR: In this article, two artificial intelligence-based models, namely artificial neural networks and support vector machines, are used together and comparatively to predict the mechanical behavior of different carbonate sands.
Journal ArticleDOI
Application of artificial intelligence in geotechnical engineering: A state-of-the-art review
TL;DR: A detailed review of the performance of AI methods and algorithms used in geotechnical engineering can be found in this paper , where Artificial Neural Network (ANN) emerged as the most widely used and preferred AI method with 52% of studies relying on it.
Journal ArticleDOI
Application of an Artificial Neural Network for Modeling the Mechanical Behavior of Carbonate Soils
TL;DR: In this paper, a new approach based on artificial neural networks is presented to predict the mechanical behavior of different carbonate soils, including relative density, axial strain, maximum void ratio, calcium carbonate content, and confining pressure.
Journal ArticleDOI
Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models
Journal ArticleDOI
Novel Approach to Resilient Modulus Using Routine Subgrade Soil Properties
TL;DR: In this paper, Gene Expression Programming (GEP) models were developed to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design applications, and two different correlations were developed using different combinations of the influencing parameters.
References
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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.
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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.
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TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
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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.
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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.