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Mr. Mahdi Ahmadi. Jalayer

Master of science , Civil Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran

Civil Engineering

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Prediction of compressive strength of cement mortar using chemical composition of raw materials

More than 3 years ago

Mr. Mahdi Ahmadi. Jalayer , Master of science , Civil Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran ,
Ms. Sahar Mahdinia , Ferdowsi Univeristy of Mashhad ,
Dr. Mohammadreza Tavakkolizadeh , Assistant Professor, Department of Civil and Environmental Engineering, Engineering Faculty, Ferdowsi University of Mashhad ,

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Abstract | Highlights | References |

Paper References

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Mahdinia, S. and M. Tavakkolizadeh. Proposed a model for compressive strength of cement mortar by using Multi Expression Programing. in 12th International Congress on Civil Engineering. 2021.
Korouzhdeh, T., H. Eskandari-Naddaf and R. Kazemi, Hybrid artificial neural network with biogeography-based optimization to assess the role of cement fineness on ecological footprint and mechanical properties of cement mortar expose to freezing/thawing. Construction and Building Materials, 2021. 304: p. 124589.
Mahdinia, S., H. Eskandari-Naddaf and R. Shadnia, Effect of cement strength class on the prediction of compressive strength of cement mortar using GEP method. Construction and Building Materials, 2019. 198: p. 27-41
Arabshahi, A., N. Gharaei-Moghaddam and M. Tavakkolizadeh. Development of applicable design models for concrete columns confined with aramid fiber reinforced polymer using Multi-Expression Programming. in Structures. 2020. Elsevier.
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Ghanei, A., H. Eskandari‐Naddaf and A. Davoodi, Corrosion behavior and optimization of air‐entrained reinforced concrete, incorporating microsilica. Structural Concrete, 2018. 19(5): p. 1472-1480.
Arabshahi, A. and M. Tavakkolizadeh, Predictive model for slenderness limit of circular RC columns confined with FRP wraps. Structural Concrete, 2021.
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Kooshkaki, A. and H. Eskandari-Naddaf, Effect of porosity on predicting compressive and flexural strength of cement mortar containing micro and nano-silica by multi-objective ANN modeling. Construction and Building Materials, 2019. 212: p. 176-191

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