Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool.

• Publication year

  2016

• Venue

  Advances in Materials Science and Engineering

• Publication date

  2016-01-10

• Fields of study

  Materials Science, Computer Science, Engineering

• Identifiers
  DOI 10.1155/2016/7648467
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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