Assessment and prediction of cement paste flow behavior; Marsh-funnelflow time and mini-slump values


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Mardanı Aghabaglou A., Öztürk H. T. , Kankal M., Ramyar K.

Construction And Building Materials, vol.301, no.1, 2021 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Volume: 301 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.1016/j.conbuildmat.2021.124072
  • Journal Name: Construction And Building Materials
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, Aerospace Database, CAB Abstracts, Communication Abstracts, INSPEC, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Cement paste, Flowability, Neural Network, Teaching-Learning Based Artificial Bee, Colony (TLABC) Algorithm, SELF-COMPACTING CONCRETE, ADAPTIVE REGRESSION SPLINES, NEURAL-NETWORK, RHEOLOGICAL BEHAVIOR, SUPERPLASTICIZER, OPTIMIZATION, SIMULATION, PARAMETERS, ADMIXTURES, FINENESS

Abstract

In this study, the parameters affecting Marsh-funnel flow time and mini-slump of the paste mixtures

were determined through experimental and modelling studies. Marsh-funnel flow times were modelled

through artificial intelligence and regression methods. A novel model was used to train the coefficients of

artificial neural networks (ANN) with the Teaching-Learning Based Artificial Bee Colony (TLABC)

Algorithm. Accuracy of this method was investigated through ANN-Back Propagation, ANN-Teaching

Learning Based Optimization Algorithm, ANN-Artificial Bee Colony, Multivariate Adaptive Regression

Splines and Classical Regression Analysis methods. ANN-TLABC method showed the best results among

the applied models. The admixture content, cement fineness, solid material content of admixture and

C3A content of cement were found to be the most important parameters affecting the flowability of

the paste. However, C2S, equivalent alkali, C4AF and C3S contents of the cement were observed to have

no considerable effect on the Marsh-funnel flowability of paste.