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Mohamed Kamruzzaman,MohdZamin Jumaat,N.H. Ramli Sulong,Kambiz Narmashiri,Khaled Ghaedi,Md. Akter Hosen 한국강구조학회 2019 International Journal of Steel Structures Vol.19 No.3
In recent decades, the application of carbon fi bre-reinforced polymer (CFRP) composites for strengthening structural elements has become an effi cient option to meet the increased cyclic loads, or repair due to fatigue cracking. The premature failure due to end-debonding is a key limitation to achieve high fatigue performance of strengthened steel beams with externally bonded CFRP plates. The objective of this study is to explore the reinforcing techniques using the CFRP in-plane end cutting shapes and the triangular spew fi llet of adhesive at the tips of the plate to care for fatigue damaged of wide-fl ange steel I-beams due to end-debonding. Four in-plane CFRP end cutting shapes were chosen, namely: rectangular, semi-elliptical, semi-circular and trapezoidal. The application of the trapezoidal end shape was found to be the best confi guration for delaying the end-debonding failure mode and high fatigue life compared to the other CFRP in-plane end cutting shapes. Applying the triangular spew fi llets of adhesive signifi cantly increased the end-debonding and steel beam fracture initiation life of the strengthened beams.
Application of the ANFIS model in deflection prediction of concrete deep beam
Mohammad Mohammadhassani,Hossein Nezamabadi-Pour,MohdZamin Jumaat,Mohammed Jameel,S.J.S.Hakim,Majid Zargar 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.3
With the ongoing development in the computer science areas of artificial intelligence and computational intelligence, researchers are able to apply them successfully in the construction industry. Given the complexities indeep beam behaviour and the difficulties in accurate evaluation of its deflection,the current study has employed the Adaptive Network-based Fuzzy Inference System (ANFIS) as one of the modelling tools to predict deflection for high strength self compacting concrete (HSSCC) deep beams. In this study, about 3668measured data on eight HSSCC deep beams are considered. Effective input data and the corresponding deflection as output data were recorded at all loading stages up to failure load for all tested deep beams. The results of ANFIS modelling and the classical linear regression were compared and concluded that the ANFIS results are highly accurate, precise and satisfactory.
Application of the ANFIS model in deflection prediction of concrete deep beam
Mohammadhassani, Mohammad,Nezamabadi-Pour, Hossein,Jumaat, MohdZamin,Jameel, Mohammed,Hakim, S.J.S.,Zargar, Majid Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.3
With the ongoing development in the computer science areas of artificial intelligence and computational intelligence, researchers are able to apply them successfully in the construction industry. Given the complexities indeep beam behaviour and the difficulties in accurate evaluation of its deflection, the current study has employed the Adaptive Network-based Fuzzy Inference System (ANFIS) as one of the modelling tools to predict deflection for high strength self compacting concrete (HSSCC) deep beams. In this study, about 3668measured data on eight HSSCC deep beams are considered. Effective input data and the corresponding deflection as output data were recorded at all loading stages up to failure load for all tested deep beams. The results of ANFIS modelling and the classical linear regression were compared and concluded that the ANFIS results are highly accurate, precise and satisfactory.