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
State-of-the-Art: Prediction of Resilient Modulus of Unsaturated Subgrade Soils
Zhong Han,Sai K. Vanapalli +1 more
TL;DR: In this article, the authors summarized the state-of-the-art equations that have been proposed over the past four decades to predict the variation of the resilient modulus with respect to soil suction for pavement base-course materials and subgrade soils.
Journal ArticleDOI
Relationship between resilient modulus and suction for compacted subgrade soils
Zhong Han,Sai K. Vanapalli +1 more
TL;DR: In this paper, a simple model based on the soil-water characteristic curve and MR values at saturated and optimum moisture content conditions for prediction, is used to predict the measured resilient modulus of subgrade soils.
Journal ArticleDOI
35 Years of (AI) in Geotechnical Engineering: State of the Art
TL;DR: The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researched.
Journal ArticleDOI
Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases
Mosbeh R. Kaloop,Deepak Kumar,Pijush Samui,Alaa R. Gabr,Jong Wan Hu,Xinghan Jin,Bishwajit Roy +6 more
TL;DR: In this paper, a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) was used to predict the performance of stabilized aggregate bases subjected to wet-dry cycles.
Journal ArticleDOI
Artificial Neural Network Modeling for Dynamic Modulus of Hot Mix Asphalt Using Aggregate Shape Properties
TL;DR: In this paper, an ANN-based model was developed for the estimation of dynamic modulus of hot mix asphalt (HMA) using aggregate shape parameters, i.e., angularity, texture, form, and sphericity.
References
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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
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