Journal ArticleDOI
Artificial neural network modeling (ANN) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates
TLDR
In this paper, an ANN model was used to predict the final deformation of asphalt concrete mixtures modified by nano-additives, including micro silica and nano TiO 2 /SiO 2.About:
This article is published in Construction and Building Materials.The article was published on 2015-06-15. It has received 103 citations till now. The article focuses on the topics: Asphalt concrete & Asphalt.read more
Citations
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Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques
TL;DR: In this article, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques.
Journal ArticleDOI
Recognition of asphalt pavement crack length using deep convolutional neural networks
TL;DR: The result indicates that the training strategy including two processes overcomes the lack of crack labelled images and improves the accuracy of the network, combining with quadrature encoding and stochastic gradient descent.
Journal ArticleDOI
Performance evaluation of dry process crumb rubber-modified asphalt mixtures with nanomaterial
TL;DR: Using crumb rubber (CR) especially at high percentages, in asphalt pavement using dry process has less popularity than wet process due to the adverse effect of this method on adhesion and/or cohes...
Journal ArticleDOI
Prediction and sensitivity analysis of long-term skid resistance of epoxy asphalt mixture based on GA-BP neural network
TL;DR: In this paper, the authors investigated the relationship between long-term skid resistance of epoxy asphalt mixture (EAM) and multiple engineering parameters involving mixture design parameters, construction parameters and operation parameters.
Journal ArticleDOI
Predicting rutting performance of carbon nano tube (CNT) asphalt binders using regression models and neural networks
TL;DR: In this article, the authors investigated the effects of loading frequency and temperature on rutting susceptibility of CNT asphalt binders, using regression models and artificial neural networks (ANN).
References
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Book
Pavement analysis and design
TL;DR: In this paper, the authors present the theory of pavement design and review the methods developed by several organizations, such as the American Association of State Highway and Transportation Officials (AASHTO), the Asphalt Institute (AI), and the Portland Cement Association (PCA).
NAtioNAl CooperAtive HigHwAy reseArCH progrAm
TL;DR: Warm mix asphalt (WMA) has been gaining acceptance across the United States and Canada in recent years as mentioned in this paper, and many of these demonstrations were conducted with only one or two WMA technologies.
Journal Article
Characterization of modified asphalt binders in superpave mix design
TL;DR: This report documents the results of a study on the applicability of Superpave specification (AASHTO MP1, "Standard Specification for Performance Graded Asphalt Binder") and protocols developed for asphalt cements to modified asphalt binders.
Journal ArticleDOI
Rheological Properties and Chemical Bonding of Asphalt Modified with Nanosilica
Hui Yao,Zhanping You,Liang Li,Chee Huei Lee,David Wingard,Yoke Khin Yap,Xianming Shi,Shu Wei Goh +7 more
TL;DR: In this article, the authors evaluated the rheological properties and chemical bonding of nano-modified asphalt binders blended with nanosilica and found that the nanosilsilica was added to the control asphalt at contents of 4% and 6% based on the weight of asphalt binder.
Book
Neural Networks in Bioprocessing and Chemical Engineering
Donald Richard Baughman,Y.A. Liu +1 more
TL;DR: Introduction to neural networks fundamental and practical aspects of neural computing classification - fault diagnosis and feature categorization prediction and optimization process forecasting, modelling and control of time-dependent systems development of expert networks.