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RISS 인기검색어
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- 저자 : M. Gandomi (검색결과 5 건)
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A data mining approach to compressive strength of CFRP-confined concrete cylinders
S.M. Mousavi,A.H. Alavi,A.H. Gandomi,M. Arab Esmaeili,M. Gandomi 국제구조공학회 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.36 No.6
In this paper, compressive strength of carbon fiber reinforced polymer (CFRP) confined concrete cylinders is formulated using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA, and a robust variant of GP, namely multi expression programming (MEP). Straightforward GP/SA and MEP-based prediction equations are derived for the compressive strength of CFRP-wrapped concrete cylinders. The models are constructed using two sets of predictor variables. The first set comprises diameter of concrete cylinder, unconfined concrete strength, tensile strength of CFRP laminate, and total thickness of CFRP layer. The most widely used parameters of unconfined concrete strength and ultimate confinement pressure are included in the second set. The models are developed based on the experimental results obtained from the literature. To verify the applicability of the proposed models, they are employed to estimate the compressive strength of parts of test results that were not included in the modeling process. A sensitivity analysis is carried out to determine the contributions of the parameters affecting the compressive strength. For more verification, a parametric study is carried out and the trends of the results are confirmed via some previous studies. The GP/SA and MEP models are able to predict the ultimate compressive strength with an acceptable level of accuracy. The proposed models perform superior than several CFRP confinement models found in the literature. The derived models are particularly valuable for pre-design purposes.
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S.M. Mousavi,A.H. Gandomi,A.H. Alavi,M. Vesalimahmood 국제구조공학회 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.36 No.2
In this study, a hybrid search algorithm combining genetic programming with orthogonal least squares (GP/OLS) is utilized to generate prediction models for compressive strength of high performance concrete (HPC) mixes. The GP/OLS models are developed based on a comprehensive database containing 1133 experimental test results obtained from previously published papers. A multiple least squares regression (LSR) analysis is performed to benchmark the GP/OLS models. A subsequent parametric study is carried out to verify the validity of the models. The results indicate that the proposed models are effectively capable of evaluating the compressive strength of HPC mixes. The derived formulas are very simple, straightforward and provide an analysis tool accessible to practicing engineers.
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A data mining approach to compressive strength of CFRP-confined concrete cylinders
Mousavi, S.M.,Alavi, A.H.,Gandomi, A.H.,Esmaeili, M. Arab,Gandomi, M. Techno-Press 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.36 No.6
In this paper, compressive strength of carbon fiber reinforced polymer (CFRP) confined concrete cylinders is formulated using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA, and a robust variant of GP, namely multi expression programming (MEP). Straightforward GP/SA and MEP-based prediction equations are derived for the compressive strength of CFRP-wrapped concrete cylinders. The models are constructed using two sets of predictor variables. The first set comprises diameter of concrete cylinder, unconfined concrete strength, tensile strength of CFRP laminate, and total thickness of CFRP layer. The most widely used parameters of unconfined concrete strength and ultimate confinement pressure are included in the second set. The models are developed based on the experimental results obtained from the literature. To verify the applicability of the proposed models, they are employed to estimate the compressive strength of parts of test results that were not included in the modeling process. A sensitivity analysis is carried out to determine the contributions of the parameters affecting the compressive strength. For more verification, a parametric study is carried out and the trends of the results are confirmed via some previous studies. The GP/SA and MEP models are able to predict the ultimate compressive strength with an acceptable level of accuracy. The proposed models perform superior than several CFRP confinement models found in the literature. The derived models are particularly valuable for pre-design purposes.
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Mousavi, S.M.,Gandomi, A.H.,Alavi, A.H.,Vesalimahmood, M. Techno-Press 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.36 No.2
In this study, a hybrid search algorithm combining genetic programming with orthogonal least squares (GP/OLS) is utilized to generate prediction models for compressive strength of high performance concrete (HPC) mixes. The GP/OLS models are developed based on a comprehensive database containing 1133 experimental test results obtained from previously published papers. A multiple least squares regression (LSR) analysis is performed to benchmark the GP/OLS models. A subsequent parametric study is carried out to verify the validity of the models. The results indicate that the proposed models are effectively capable of evaluating the compressive strength of HPC mixes. The derived formulas are very simple, straightforward and provide an analysis tool accessible to practicing engineers.
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New Synthesis of Perhydrotriazolotriazoles Catalyzed by TiCl<sub>4</sub> under Ambient Conditions
Safari, J.,Gandomi-Ravandi, S.,Ghotbinejad, M. Korean Chemical Society 2012 대한화학회지 Vol.56 No.1
Aromatic 2,3-diazabuta-1,3-dienes in glacial acetic acid with isothiocyanate in the presence of catalyst $TiCl_4$ at room temperature produced via criss-cross cycloaddition reactions the corresponding perhydro[1,2,4]triazolo[1,2-a][1,2,4] triazole-1,5-dithiones in relatively high yields and short reaction time.
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