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Abstract
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Article Information:
Presenting an Appropriate Neural Network for Optimal Mix Design of Roller Compacted Concrete Dams
Taha Mehmannavaz, Vahid Khalilikhorram, Seyed Mahdi Sajjadi and Mostaf Samadi
Corresponding Author: Taha Mehmannavaz
Submitted: June 17, 2013
Accepted: July 19, 2013
Published: March 05, 2014 |
Abstract:
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In general, one of the main targets to achieve the optimal mix design of concrete dams is to reduce the amount of cement, heat of hydration, increasing the size of aggregate (coarse) and reduced the permeability. Thus, one of the methods which is used in construction of concrete and soil dams as a suitable replacement is construction of dams in roller compacted concrete method. Spending fewer budgets, using road building machinery, short time of construction and continuation of construction all are the specifications of this construction method, which have caused priority of these two methods and finally this method has been known as a suitable replacement for constructing dams in different parts of the world. On the other hand, expansion of the materials used in this type of concrete, complexity of its mix design, effect of different parameters on its mix design and also finding relations between different parameters of its mix design have necessitated the presentation of a model for roller compacted concretemix design. Artificial neural networks are one of the modeling methods which have shown very high power for adjustment to engineering problems. A kind of these networks, called Multi-Layer Perceptron (MLP) neural networks, was used as the main core of modeling in this study along with error-back propagation training algorithm, which is mostly applied in modeling mapping behaviors.
Key words: Concrete, dam, MATLAB, MLP, RCC, ,
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Cite this Reference:
Taha Mehmannavaz, Vahid Khalilikhorram, Seyed Mahdi Sajjadi and Mostaf Samadi, . Presenting an Appropriate Neural Network for Optimal Mix Design of Roller Compacted Concrete Dams. Research Journal of Applied Sciences, Engineering and Technology, (9): 1872-1877.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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