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RISS 인기검색어
- 검색키워드
- 저자 : Cafer Kayadelen (검색결과 3 건)
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Kose, M. Metin,Kayadelen, Cafer Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.47 No.3
In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.
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M. Metin Kose,Cafer Kayadelen 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.47 No.3
In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.
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Ahmet Baylar,Mehmet Unsal,Fahri Ozkan,Cafer Kayadelen 대한토목학회 2014 KSCE JOURNAL OF CIVIL ENGINEERING Vol.18 No.6
The primary purpose of a weir is to measure discharge. Moreover, it can be used as a tool to increase aeration efficiency in rivers,fish hatcheries, and water treatment plants. A free overfall jet from a weir plunging into downstream water causes entrainment of theair bubbles if the free overfall jet velocity exceeds a certain critical value and hence aeration occurs. In recent years, different softcomputing systems have been successfully employed for the solution of complex problems. The aim of this study is to developmodels to estimate air entrainment and aeration efficiencies of weirs using soft computing systems. For this aim, genetic expressionprogramming, a recently developed artificial intelligence technique, is used. Genetic Expression Programming (GEP) is a geneticalgorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or moregenetic operators. GEP is preferred since it generates a mathematical function which fits to given experimental data. The resultsindicate that there are good agreements between the measured values and the values obtained by the genetic expression programmingmodels. These good agreements confirm the validity of the developed genetic expression programming models.
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