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Niloufar Ghazanfari, Shayan Gholami, Alireza Emad & Mohammad Shekarchi

Evaluation of GMDH and MLP Networks for Prediction of Compressive Strength and Workability of Concrete

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Abstract

Measurement of compressive strength and workability of concrete as the most important mechanical properties of concrete have always been a significant topic to designers. Considering a large number of parameters influencing the mixed design of concrete and the reduction in the number of test samples, today various inferential algorithms are used for discovering relationships, predicting mechanical properties, and identifying nonlinear patterns along with optimizing concrete mixtures. Among these algorithms, artificial neural networks are highly effective and reliable alternative for complicated systems with uncertain structure in which the aim is to obtain different kinds of relationship between the input and output variables with high orders. The methodology used in this study is based on MLP and GMDH artificial neural networks, which lack theoretical basis and are known as inverse engineering (IE) or black box models. To present a model for prediction of these parameters, two artificial neural network approaches were investigated and the performance of GMDH model, when comparing the laboratory results with the predicted values, suggests acceptable accuracy of this model in evaluation of compressive strength and slump.

Keywords : artificial neural network, concrete compressive strength, workability, ANN, multi-layer perceptron, MLP, group method of data handling, GMDH

Pour citer cet article

Niloufar Ghazanfari, Shayan Gholami, Alireza Emad & Mohammad Shekarchi, «Evaluation of GMDH and MLP Networks for Prediction of Compressive Strength and Workability of Concrete», Bulletin de la Société Royale des Sciences de Liège [En ligne], Volume 86 - Année 2017, Special issue, 855 - 868 URL : http://popups.ulg.ac.be/0037-9565/index.php?id=7032.

A propos de : Niloufar Ghazanfari

Department of Civil Engineering, University of Tehran, Tehran, Iran, N.ghazanfari@ut.ac.ir

A propos de : Shayan Gholami

Department of Civil Engineering, University of Tehran, Tehran, Iran

A propos de : Alireza Emad

Construction Material Institute (CMI), University of Tehran, Iran

A propos de : Mohammad Shekarchi

Department of Civil Engineering, University of Tehran, Tehran, Iran