A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement-Reference-Cited by-同舟云学术

A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement

Published:2020-06-20 Issue: Volume: Page:
ISSN:0177-0667
Container-title:Engineering with Computers
language:en
Short-container-title:Engineering with Computers

Author:

Shariati Mahdi,Mafipour Mohammad Saeed,Ghahremani Behzad,Azarhomayun Fazel,Ahmadi Masoud,Trung Nguyen Thoi,Shariati Ali

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,General Engineering,Modeling and Simulation,Software

Link

https://link.springer.com/content/pdf/10.1007/s00366-020-01081-0.pdf

Reference86 articles.

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2. Shariati M et al (2019) Experimental investigation on the effect of cementitious materials on fresh and mechanical properties of self-consolidating concrete. Adv Concr Constr 8(3):225–237

3. Chopra P, Sharma RK, Kumar M (2016) Prediction of compressive strength of concrete using artificial neural network and genetic programming. Adv Mater Sci Eng

4. Toghroli A et al (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct 17(5):623–639

5. Naderpour H, Rafiean AH, Fakharian P (2018) Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 16:213–219

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