A
Ali Shariati
Researcher at Ton Duc Thang University
Publications - 38
Citations - 2175
Ali Shariati is an academic researcher from Ton Duc Thang University. The author has contributed to research in topics: Shear strength & Artificial neural network. The author has an hindex of 21, co-authored 38 publications receiving 1110 citations. Previous affiliations of Ali Shariati include University of Malaya.
Papers
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A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement
Mahdi Shariati,Mohammad Saeed Mafipour,Behzad Ghahremani,Fazel Azarhomayun,Masoud Ahmadi,Nguyen Thoi Trung,Ali Shariati +6 more
TL;DR: The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model, and it is deducted that the ELm-GWO model is capable of reaching superior performance indices in comparison with those of the other models.
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Extremely large oscillation and nonlinear frequency of a multi-scale hybrid disk resting on nonlinear elastic foundation
Ali Shariati,Aria Ghabussi,Mostafa Habibi,Hamed Safarpour,Mehran Safarpour,Abdelouahed Tounsi,Maryam Safa +6 more
TL;DR: In this paper, a fundamental study on the nonlinear vibrations considering large amplitude in multi-sized hybrid nano-composites (MHC) disk (MHCD) relying on nonlinear elastic media and located in an environment with gradually changed temperature feature is presented.
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A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques
Mahdi Shariati,Mohammad Saeed Mafipour,Peyman Mehrabi,Ali Shariati,Ali Toghroli,Nguyen Thoi Trung,Musab N.A. Salih +6 more
TL;DR: It is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction and it is demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.
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Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors
E. Sadeghipour Chahnasir,Yousef Zandi,Mahdi Shariati,E. Dehghani,Ali Toghroli,E. Tonnizam Mohamad,Ali Shariati,Maryam Safa,Karzan Wakil,Majid Khorami +9 more
TL;DR: SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors and produce a generalized performance and be learnt faster than the conventional learning algorithms.
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Effect of pumice powder and nano-clay on the strength and permeability of fiber-reinforced pervious concrete incorporating recycled concrete aggregate
Peyman Mehrabi,Mahdi Shariati,Kamyar Kabirifar,Majid Jarrah,Haleh Rasekh,Nguyen Thoi Trung,Ali Shariati,Soheil Jahandari +7 more
TL;DR: In this paper, the authors investigated employing recycled concrete aggregate (RCA) and pozzolanic additives as a partial replacement (PR) of natural coarse aggregate (NCA), and Portland cement, respectively.