Optimization of Concrete Mix Design Under the Aggressive Environment With Gmdh-type Neural Networks
Publish place: 8th International Congress on Civil Engineering
Publish Year: 1388
نوع سند: مقاله کنفرانسی
زبان: English
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ICCE08_785
تاریخ نمایه سازی: 28 آبان 1387
Abstract:
One of important causes for failure of concrete structures particular in Persian Gulf region is diffusion of chloride into concrete. Prediction of oncrete diffusion factor is an important issue as a key parameter in the being cycle of concrete structures.
In addition concrete diffusion factor, increasing in compressive strength and reduction in initial cost is inevitable.
The important conflicting objectives that have been considered in this paper are, namely diffusion factor and 28 days- compressive strength. These objective functions have been selected for two objective optimization process. Group Method of Data Handing (GMDH) algorithm is self-organizing approach by which gradually complicated models are generated based on the evaluation of their performances on asset of multi-input-single-output data pairs .The GMDH was firstly developed by Ivakhenko as a multivariate analysis method for complex system modeling and identification. In this way, GMDH was used to circumvent the difficulty of knowing prior knowledge of mathematical model of the process being
considered. In other word, GMDH can be used to model complex system without having specific knowledge of the systems .The main idea of GMDH is to build an analytical function in a feed-forward network based on a quadratic node transfer function whose coefficient are obtained using regression technique. In fact, real GMDH algorithm in which model coefficient are estimated by means of the least squares method has been classified in two complete induction and incomplete induction, which represent the combinational and multilayered iterative algorithms, respectively.
NSGAII algorithm is used for multi-objective optimization, this algorithm has some prblem in the crowding distance subroutine, therefore a new diversity preserving algorithm, named å_elimination, is proposed to enhanced the performance of multi-objective evolutionary algorithms in optimization problems.
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Authors
H. Beheshti Nezhad
Faculty Member, Department of Civil, Islamic Azad University, Birjand Branch, Iran
N. Nariman-Zadeh
Faculty Member, Department of Engineering, University of Guilan , Rasht, Iran
M. M. Ranjbar
Faculty Member, Department of Civil, University of Guilan , Rasht, Iran
H. Aivani
Master Degree in Structural Engineering, , University of Guilan , Rasht, Iran
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