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Modeling of Strength of High Performance Concrete with Artificial Neural Network and Mahalanobis Distance Outlier Detection Method  

Hong, Jung-Eui (Department of Industrial and Management Engineering, Chungju National University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.33, no.4, 2010 , pp. 122-129 More about this Journal
Abstract
High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance (MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction performance.
Keywords
Mahalanobis Distance; Neural Network; Multiple Regression;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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