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Estimation of optimum design of structural systems via machine learning
Three different structural engineering designs were investigated to determine optimum design variables, and then to estimate design parameters and the main objective function of designs directly, speedily, and effectively. Two different optimization operations were carried out: One used the harmony search (HS) algorithm, combining different ranges of both HS parameters and iteration with population numbers. The other used an estimation application that was done via artificial neural networks (ANN) to find out the estimated values of parameters. To explore the estimation success of ANN models, different test cases were proposed for the three structural designs. Outcomes of the study suggest that ANN estimation for structures is an effective, successful, and speedy tool to forecast and determine the real optimum results for any design model.
Estimation of optimum design of structural systems via machine learning
Three different structural engineering designs were investigated to determine optimum design variables, and then to estimate design parameters and the main objective function of designs directly, speedily, and effectively. Two different optimization operations were carried out: One used the harmony search (HS) algorithm, combining different ranges of both HS parameters and iteration with population numbers. The other used an estimation application that was done via artificial neural networks (ANN) to find out the estimated values of parameters. To explore the estimation success of ANN models, different test cases were proposed for the three structural designs. Outcomes of the study suggest that ANN estimation for structures is an effective, successful, and speedy tool to forecast and determine the real optimum results for any design model.
Estimation of optimum design of structural systems via machine learning
Front. Struct. Civ. Eng.
Bekdaş, Gebrail (Autor:in) / Yücel, Melda (Autor:in) / Nigdeli, Sinan Melih (Autor:in)
Frontiers of Structural and Civil Engineering ; 15 ; 1441-1452
01.12.2021
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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