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Advances in Nano Research
  Volume 11, Number 5, November 2021, pages 565-579
DOI: https://doi.org/10.12989/anr.2021.11.5.565
 


Analysis of the superplasticizer demand using computer simulation
Arian Heirati, Yousef Zandi, Shahriar Tavousi Tafreshi and Manuchehr Behruyan

 
Abstract
    The merits of self-consolidating concrete (SCC) such as high deformability, excellent resistance to segregation, and usability without applying vibration is highly common. To gain an environment-friendly approach or improving SCC properties, cement in SCC can be partially replaced with other materials. However, identifying the most effective parameters on the Superplasticizer demand (SP demand) of SSC would not be easy after the replacement. The main aim of this study is to identify the most influencing approaches on SP demand prediction. Hence, five different approaches in SP demand prediction, including Jring test, V funnel test, Ubox test, 3-min slump value, and 50-min slump value have been considered. Then, different models of an artificial intelligence approach are developed and the most influential one in an accurate SP demand prediction was determined. In comparison with other methods, it was indicated that in estimating the SP demand, V-funnel can be a better technique because of producing the lowest RMSE.
 
Key Words
    ANFIS; prediction; self consolidating concrete; superplasticizer demand
 
Address
Arian Heirati, Shahriar Tavousi Tafreshi and Manuchehr Behruyan: Department of Civil Engineering, Faculty of Civil and Earth Resources Engineering, Central Tehran Branch,
Islamic Azad University, Tehran, Iran

Yousef Zandi: Department of Civil Engineering,Tabriz Branch, Islamic Azad University, Tabriz, Iran
 

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