Artificial neural network modeling of early-age dynamic young's modulus of normal concrete

Giri Venkiteela*, Amedeo Gregori, Zhihui Sun, Surendra P Shah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In this paper, a comprehensive model for dynamic Young's modulus of early-age normal concrete is proposed based on its original mixture design. Based on the experimental work and artificial neural network (ANN) technique, various cement paste, mortar, and concrete mixtures' elastic moduli were modeled on a single platform. The role of hydration age, water-cement ratio (w/c), curing temperature, aggregate volume percentages, aggregate absorption capacities, and aggregate sizes on the development of elastic moduli were considered Using the generalization capabilities of ANN, effective mixture design parameters affecting the concrete elastic modulus evolution at early ages were identified. From this study, it was possible to quantify the effect of age, w/c, coarse aggregate volume percentage, and curing temperature as main parameters in the evolution of concrete elastic modulus. It was concluded that with no need of complex modeling procedures, early-age concrete elastic modulus can be properly modeled by using an ANN technique based on original mixture proportions.

Original languageEnglish (US)
Pages (from-to)282-290
Number of pages9
JournalACI Materials Journal
Volume107
Issue number3
StatePublished - May 1 2010

Keywords

  • Aggregates
  • Early-age concrete
  • Modulus of elasticity

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Materials Science(all)

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