Materials and Structures

, Volume 42, Issue 4, pp 469–484 | Cite as

Evaluating and forecasting the initial and final setting times of self-compacting concretes containing mineral admixtures by neural network

  • Erhan Güneyisi
  • Mehmet Gesoglu
  • Erdoğan Özbay
Original Article


An experimental study was conducted to investigate the effects of using binary, ternary, and quaternary cementitious blends of portland cement (PC), fly ash (FA), ground granulated blast furnace slag (GBS), silica fume (SF), and metakaolin (MK) on initial and final setting times of self-compacting concretes (SCCs). For this purpose, a total of 65 SCC mixtures were prepared at two different water binder ratios. Furthermore, based on the experimental results, neural network (NN) model-based explicit formulations were developed to predict the initial and final setting times of SCCs in terms of the amount of concrete constituents, namely mixing water, PC, FA, GBS, SF, MK, fine (fa) and coarse (ca) aggregates, and superplasticizer (SP). The test results have revealed that the mineral admixtures were very effective on the initial and final setting times of SCCs. Besides, it was found that the model developed by using NN seemed to have a high prediction capability of initial and final setting times of SCCs.


Final set Initial set Mineral admixture Neural network Penetration resistance Self-compacting concrete 



Authors would like to acknowledge the supports of Gaziantep University- Coordination office of Scientific Research Projects (GÜBAP) to carry out the present study. Moreover, the assistances of Mr. Mustafa Şeker, Mr. Serkan Taşatan, and Mr. Bekir Çeviksever during the experimental phase of the study are gratefully acknowledged.


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Copyright information

© RILEM 2008

Authors and Affiliations

  • Erhan Güneyisi
    • 1
  • Mehmet Gesoglu
    • 1
  • Erdoğan Özbay
    • 2
  1. 1.Department of Civil EngineeringGaziantep UniversityGaziantepTurkey
  2. 2.Kilis Vocational High SchoolUniversity of GaziantepGaziantepTurkey

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