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http://dx.doi.org/10.12989/cac.2013.12.2.151

Prediction of lightweight concrete strength by categorized regression, MLR and ANN  

Tavakkol, S. (Hydraulic Structures Division, Water Research Institute)
Alapour, F. (Dept. of Civil and Environmental Engineering, Amirkabir University of Technology)
Kazemian, A. (Dept. of Civil and Environmental Engineering, Amirkabir University of Technology)
Hasaninejad, A. (Dept. of Civil Engineering, Shahed University)
Ghanbari, A. (Dept. of Computer Engineering and IT, Amirkabir University of Technology)
Ramezanianpour, A.A. (Concrete Technology and Durability Research Center, Amirkabir University of Technology)
Publication Information
Computers and Concrete / v.12, no.2, 2013 , pp. 151-167 More about this Journal
Abstract
Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.
Keywords
compressive strength; lightweight aggregate concrete; LECA; multiple linear regression; neural networks; data cleansing;
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Times Cited By KSCI : 2  (Citation Analysis)
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