M
Mahdi Shariati
Researcher at Duy Tan University
Publications - 105
Citations - 6300
Mahdi Shariati is an academic researcher from Duy Tan University. The author has contributed to research in topics: Compressive strength & Shear (sheet metal). The author has an hindex of 35, co-authored 101 publications receiving 3254 citations. Previous affiliations of Mahdi Shariati include University of Malaya & Anhui University of Technology.
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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Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength
Maryam Safa,Mahdi Shariati,Zainah Ibrahim,Ali Toghroli,Shahrizan Baharom,Norazman Mohamad Nor,Dalibor Petković +6 more
TL;DR: In this paper, the shear strength of steel-concrete composite beams was analyzed based on the respective variable parameters and the methodology used by ANFIS (Adaptive Neuro Fuzzy Inference System) was adopted for this purpose.
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Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete
Mahdi Shariati,Mohammad Saeed Mafipour,Peyman Mehrabi,Alireza Bahadori,Yousef Zandi,Musab N.A. Salih,Hoang Nguyen,Jie Dou,Xuan Song,Shek Poi-Ngian +9 more
TL;DR: Investigation of the application of a hybrid artificial neural network–particle swarm optimization (ANN-PSO) model in the behavior prediction of channel connectors embedded in normal and high-strength concrete (HSC) revealed that an ANN model could properly predict the behavior of channel connector and eliminate the need for conducting costly experiments to some extent.
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Improving construction and demolition waste collection service in an urban area using a simheuristic approach: A case study in Sydney, Australia
Maziar Yazdani,Kamyar Kabirifar,Boadu Elijah Frimpong,Mahdi Shariati,Mirpouya Mirmozaffari,Azam Boskabadi +5 more
TL;DR: This research proposes a novel simheuristic based on an integrated simulation-optimization approach, in which an efficient hybrid Genetic Algorithm is applied in order to optimize vehicle route planning for C&D waste collection from construction projects to recycling facilities.
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Prediction of shear capacity of channel shear connectors using the ANFIS model
Nor Hafizah Ramli Sulong,Ali Toghroli,Mohammad Mohammadhassani,Mahdi Shariati,Meldi Suhatril,Zainah Ibrahim +5 more
TL;DR: The outcome shows that the use of ANFIS produces highly accurate, precise and satisfactory results as opposed to the classical linear regressions.