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Showing 2 results for Hosseinpour

Y. Sharifi, M. Hosseinpour,
Volume 9, Issue 2 (4-2019)

In the current study two methods are evaluated for predicting the compressive strength of concrete containing metakaolin. Adaptive neuro-fuzzy inference system (ANFIS) model and stepwise regression (SR) model are developed as a reliable modeling method for simulating and predicting the compressive strength of concrete containing metakaolin at the different ages. The required data in training and testing state obtained from a reliable data base. Then, a comparison has been made between proposed ANFIS model and SR model to have an idea about the predictive power of these methods.
A. Moghbeli, M. Hosseinpour , Y. Sharifi,
Volume 12, Issue 3 (4-2022)

The lateral-torsional buckling (LTB) strength of cellular steel girders that were subjected to web distortion was rarely examined. Since no formulation has been presented for predicting the capacity of such beams, in the current paper an extensive numerical investigation containing 660 specimens was modeled using finite element analysis (FEA) to consider the ultimate lateral-distortional buckling (LDB) strength of such members. Then, a reliable algorithm based on the artificial neural networks (ANNs) was developed and the most accurate model was chosen to derive an efficient formula to evaluate the LDB capacity of steel cellular beams. The input and target data required in the ANN models were provided using the ANN analyzes. An attempt was made to include the proposed formula in all the variables affecting the LDB of cellular steel beams. In the next step, the validity of the proposed formula was proved by several statistical criteria, and also the most influential input variable was discussed. eventually, a comparison study was executed between the results provided by the ANN-based equation and the AS4100, EC3, and AISC codes. It was revealed that the presented equation is accurate enough and can be used by practical engineers.

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