Journal ArticleDOI
A concrete mix proportion design algorithm based on artificial neural networks
Tao Ji,Tingwei Lin,Xujian Lin +2 more
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TLDR
In this paper, a concrete mix proportion design algorithm based on a way from aggregates to paste, a least paste content, Modified Tourfar's Model and ANNs was proposed, which is expected to reduce the number of trial and error, save cost, laborers and time.About:
This article is published in Cement and Concrete Research.The article was published on 2006-07-01. It has received 138 citations till now. The article focuses on the topics: Water–cement ratio & Slump.read more
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Technical and commercial progress in the adoption of geopolymer cement
TL;DR: In the absence of an in-service track record comparable in scale and longevity to Portland cement, research is essential to validate durability testing methodology and improve geopolymer cement technology Colloid and interface science, gel chemistry, phase formation, reaction kinetics, transport phenomena, comminution, particle packing and rheology.
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Modeling slump flow of concrete using second-order regressions and artificial neural networks
TL;DR: In this article, the methods for modeling the slump flow of concrete using second-order regression and artificial neural network (ANN) are described, which led to the following conclusions: (1) The slump flow model based on ANN is much more accurate than that based on regression analysis, and (2) It has become convenient and easy to use ANN models for numerical experiments to review the effects of mix proportions on concrete flow properties.
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Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN
TL;DR: In this article, an artificial neural network (ANN) was used to predict the 28-day compressive strength of self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash.
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The role of particle technology in developing sustainable construction materials
TL;DR: A brief review of the role of particle technology in the development of low-CO 2 aluminosilicate "geopolymer" binders and concretes as an alternative to traditional Portland cement-based materials is presented in this paper.
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Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks
TL;DR: By hybridizing ANN with GA, the convergence speed of ANN and its accuracy of prediction can be improved and the trained hybrid model can be used for predicting slump of concrete for a given concrete design mix in quick time without performing multiple trials with different design mix proportions.
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
Concrete Mixture Proportioning: A Scientific Approach
TL;DR: In this article, the authors present a flowchart for mixture simulation, showing the relationship between mix composition and properties of concrete, including deformation of Hardened Concrete, compressive strength and tensile strength.
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Mixture-proportioning of high-performance concrete
TL;DR: In this article, the authors present a new approach to design concrete mixtures based upon a set of models relating composition and engineering properties of concrete, to be implemented into software, linked with a material database.
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Packing of aggregates : An alternative tool to determine the optimal aggregate mix
TL;DR: In this paper, the aggregate selection and combination of the aggregates has a dominating influence on the quality and price of the concrete and should receive more thorough attention, and an alternative mix design may often lead to a better and cheaper concrete.
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Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming
TL;DR: A method of optimizing high-performance concrete mix proportioning for a given workability and compressive strength using artificial neural networks and nonlinear programming is described.