NDLI: Modeling Slump of Concrete Using the Artificial Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Wen-Huan Chine Li Chen Hsun-Hsin Hsu Tai-Sheng Wang Chang-Hung Chiu
Copyright Year	2010
Abstract	This paper proposes the artificial neural networks (ANNs) and applies it to estimate the slump of high performance concrete (HPC). It is known that HPC is a highly complex material whose behavior 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, super plasticizer, 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 ANNs algorithm presented in this paper has the advantage of processed the complicated multi-variable HPC slump estimation. The results show that ANNs is a powerful method for obtaining a more accurate prediction through learning procedures which outperforms the traditional multiple linear regression analysis (RA), with lower estimating errors for predicting the HPC slump.
Starting Page	236
Ending Page	239
File Size	318772
Page Count	4
File Format	PDF
ISBN	9781424484324
DOI	10.1109/AICI.2010.287
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Aggregates Neurons Highperformance concrete Artificial neural networks Concrete Regression analysis Slump Informatics Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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