Applying Back-Propagation Neural Network for Estimating the Slump of Concrete

Abstract:

This paper proposes the back-propagation neural network (BPN) and applies it to estimate the slump of high-performance concrete (HPC). It is known that HPC is a highly complex material whose behaviour is difficult to model, especially for slump. To estimate the slump, it is a nonlinear function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. Therefore, slump estimation is set as a function of the content of these seven concrete ingredients and additional four important ratios. The results show that BPN obtains a more accurate mathematical equation through learning procedures which outperforms the traditional multiple linear regression analysis (RA), with lower estimating errors for predicting the HPC slump.

• Periodical: Advanced Materials Research (Volume 651)
• Pages: 986-989
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.651.986
• Online since: January 2013
• Authors: Chin Ming Kao, Li Chen, Chang Huan Kou, Shih Wei Ma
• Keywords: Back-Propagation Neural Network (BPN), High-Performance Concrete (HPC), Regression Analysis, Slump

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