Journal of Concrete Structures and Materials

Journal of Concrete Structures and Materials

Prediction of Standard Sand cement Mortar Compressive Strength Using Artificial Neural Network and Considering the Effect of Cement Fineness

Document Type : Original Article

Authors
Sahar Mahdinia 1
Mohammadreza Tavakkolizadeh 2
Mahdi Ahmadi Jalayer 3
1 PhD Student, Structural Engineering, Department of Civil Engineering, Ferdowsi University of Mashhad
2 Assistant Professor, Department of Civil Engineering, Ferdowsi University of Mashhad
3 MSc in Construction Management, Zaveh Torbat Cement Factory
10.30478/jcsm.2023.375675.1305
Abstract
The role of cement fineness in the process of hydration and development of compressive strength in the early ages of cement-based materials is irrefutable and it requires that its effect be investigated by predicting models. Therefore, an extensive study including 640 cement composition (1920 cement mortar specimens) from a cement factory with different percentages of raw materials feeding to the cement kiln including SiO2, Al2O3, Fe2O3, CaO, MgO, SO3, K2O, and Na2O were used to predict the 7-day compressive strength of cement mortar by artificial neural network (ANN). To investigate the effect of cement fineness, two models have been developed in two states of with and without fineness effect. Results confirmed the significant role of cement fineness as an input parameter in the performance of predicting model. The findings of this research can be used in cement production facilities in order to reduce the laboratory costs.
Keywords
Compressive Strength
Cement Mortar
Artificial Neural Network
Cement Fineness
Raw Materials
Subjects
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Journal of Concrete Structures and Materials
Volume 7, Issue 2 - Serial Number 14
September 2022
Pages 117-130

  • Receive Date 06 December 2022
  • Revise Date 26 December 2022
  • Accept Date 21 January 2023