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E. Sadeghipour Chahnasir,Y. Zandi,M. Shariati,E. Dehghani,A. Toghroli,E. Tonnizam Mohamad,A. Shariati,M. Safa,K. Wakil,M. Khorami 국제구조공학회 2018 Smart Structures and Systems, An International Jou Vol.22 No.4
The factors affecting the shear strength of the angle shear connectors in the steel-concrete composite beams can play an important role to estimate the efficacy of a composite beam. Therefore, the current study has aimed to verify the output of shear capacity of angle shear connector according to the input provided by Support Vector Machine (SVM) coupled with Firefly Algorithm (FFA). SVM parameters have been optimized through the use of FFA, while genetic programming (GP) and artificial neural networks (ANN) have been applied to estimate and predict the SVM-FFA models' results. Following these results, GP and ANN have been applied to develop the prediction accuracy and generalization capability of SVM-FFA. Therefore, SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors. According to the results, the Firefly algorithm has produced a generalized performance and be learnt faster than the conventional learning algorithms.
Chahnasir, E. Sadeghipour,Zandi, Y.,Shariati, M.,Dehghani, E.,Toghroli, A.,Mohamad, E. Tonnizam,Shariati, A.,Safa, M.,Wakil, K.,Khorami, M. 국제구조공학회 2018 Smart Structures and Systems, An International Jou Vol.22 No.4
The factors affecting the shear strength of the angle shear connectors in the steel-concrete composite beams can play an important role to estimate the efficacy of a composite beam. Therefore, the current study has aimed to verify the output of shear capacity of angle shear connector according to the input provided by Support Vector Machine (SVM) coupled with Firefly Algorithm (FFA). SVM parameters have been optimized through the use of FFA, while genetic programming (GP) and artificial neural networks (ANN) have been applied to estimate and predict the SVM-FFA models' results. Following these results, GP and ANN have been applied to develop the prediction accuracy and generalization capability of SVM-FFA. Therefore, SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors. According to the results, the Firefly algorithm has produced a generalized performance and be learnt faster than the conventional learning algorithms.
Strengthening of bolted shear joints in industrialized ferrocement construction
M. Ismail,M. Shariati,A.S.M. Abdul Awal,C.E. Chiong,E. Sadeghipour Chahnasir,A. Porbar,A. Heydari,M. Khorami 국제구조공학회 2018 Steel and Composite Structures, An International J Vol.28 No.6
This paper highlights results of some experimental work that deals with strengthening of bolted shear joints in thin-walled ferrocement structure where steel wires, bent into U-shape are considered as simple inserts around the bolt hole. The parameters investigated include the number of layers of wire mesh, edge distance of bolt hole, size and location of the inserts. Test results have shown that for small edge distance, failure occurred either in cleavage or shearing mode, and the strength of the joint increased with an increase in the edge distance. This continued up to an upper limit set by either tension or bearing failure. The experimental study further revealed that for a given edge distance the strength of a joint can significantly be enhanced by using U-inserts. The equations developed for predicting joint strength in ferrocement composites can also be modified to include the effects of the inserts with a good level of accuracy.