prediction of compressive strength of concrete using artificial neural network and genetic programming

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ID: 231497
2016
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Abstract
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.
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chopra2016advancesprediction Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Palika Chopra;Rajendra Kumar Sharma;Maneek Kumar
Journal bulletin of the korean chemical society
Year 2016
DOI
10.1155/2016/7648467
URL
http://dx.doi.org/10.1155/2016/7648467
Keywords
materials of engineering and construction mechanics of materials

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