Computers and Concrete

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A comparative assessment of bagging ensemble models for modeling concrete slump flow

  • Aydogmus, Hacer Yumurtaci (Department of Industrial Engineering, Alanya Alaaddin Keykubat University) ;
  • Erdal, Halil Ibrahim (Turkish Cooperation and Coordination Agency (TIKA)) ;
  • Karakurt, Onur (Department of Civil Engineering, Gazi University) ;
  • Namli, Ersin (Department of Industrial Engineering, Istanbul University Engineering Faculty) ;
  • Turkan, Yusuf S. (Department of Industrial Engineering, Istanbul University Engineering Faculty) ;
  • Erdal, Hamit (Institude of Social Sciences, Ataturk University)
  • Received : 2014.11.01
  • Accepted : 2015.11.06
  • Published : 2015.11.25
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Abstract

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

Keywords

References

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