(RESEARCH NOTE): CROSS-SECTION OPTIMIZATION OF CONCRETE GRAVITY DAMS BY USING GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORKS

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DEHGHANI TAFTI A.A.; MONTAZER GH.A.; NASIRI F.; GHODSIAN MASOUD

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Journal: MODARES TECHNICAL AND ENGINEERING Year:2006 | Volume:- | Issue:25 Page(s): 99-112

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Title

(RESEARCH NOTE): CROSS-SECTION OPTIMIZATION OF CONCRETE GRAVITY DAMS BY USING GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORKS

Author(s)

DEHGHANI TAFTI A.A. | MONTAZER GH.A. | NASIRI F. | GHODSIAN MASOUD | Issue Writer Certificate

Keywords

CONCRETE GRAVITY DAMSQ2

CROSS-SECTION OPTIMIZATIONQ4

GENETIC ALGORITHMQ3

ARTIFICIAL NEURAL NETWORKQ4

Abstract

The shape optimization of dams is a challenging and interesting task in Hydraulics Engineering. Due to this aim, the geometrical variables such as the bottom length, the slope of upstream and height of dams must satisfy both the stability criteria and economic considerations. Therefore, the shape optimization of dams can be interpreted as a constrained optimization problem whose main constraints are sliding stability, stability against overturning and normal stress in the surface of dams.In this paper, considering active forces on dams, such as weight, pressure, earthquake and uplift forces, the GENETIC ALGORITHM approach has been used to optimize the shape of dam. Verifying the results with available recommendations shows the advantages and accuracy of this intelligent approach.In the next step by using GENETIC ALGORITHM approach, 84 set of data were generated. These data are used as input for ARTIFICIAL NEURAL NETWORK. Then these data divided to two sets as training set and test set. By using a specific type of ARTIFICIAL NEURAL NETWORK (RBF) and a specific height of dam and earthquake acceleration, the bottom length and the slope of upstream were predicted. Results show that the R B F Network can be used for predicting the optimum shape of weight dams.

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APA: Copy

DEHGHANI TAFTI, A.A., MONTAZER, GH.A., NASIRI, F., & GHODSIAN, MASOUD. (2006). (RESEARCH NOTE): CROSS-SECTION OPTIMIZATION OF CONCRETE GRAVITY DAMS BY USING GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORKS. MODARES TECHNICAL AND ENGINEERING, -(25), 99-112. SID. https://sid.ir/paper/25039/en

Vancouver: Copy

DEHGHANI TAFTI A.A., MONTAZER GH.A., NASIRI F., GHODSIAN MASOUD. (RESEARCH NOTE): CROSS-SECTION OPTIMIZATION OF CONCRETE GRAVITY DAMS BY USING GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORKS. MODARES TECHNICAL AND ENGINEERING[Internet]. 2006;-(25):99-112. Available from: https://sid.ir/paper/25039/en

IEEE: Copy

A.A. DEHGHANI TAFTI, GH.A. MONTAZER, F. NASIRI, and MASOUD GHODSIAN, “(RESEARCH NOTE): CROSS-SECTION OPTIMIZATION OF CONCRETE GRAVITY DAMS BY USING GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORKS,” MODARES TECHNICAL AND ENGINEERING, vol. -, no. 25, pp. 99–112, 2006, [Online]. Available: https://sid.ir/paper/25039/en

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