Computers and Concrete

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Bond strength prediction of steel bars in low strength concrete by using ANN

  • Ahmad, Sohaib (Department of Civil and Structural Engineering, University of Sheffield) ;
  • Pilakoutas, Kypros (Department of Civil and Structural Engineering, University of Sheffield) ;
  • Rafi, Muhammad M. (Department of Civil Engineering, NED University of Engineering and Technology) ;
  • Zaman, Qaiser U. (Department of Civil and Environmental Engineering, University of Engineering and Technology)
  • Received : 2017.04.15
  • Accepted : 2018.08.07
  • Published : 2018.08.25
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Abstract

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

Keywords

Acknowledgement

Supported by : Higher Education Commission (HEC)

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