NDLI: Discussion on questions about using artificial neural network for predicting of concrete property

Content Provider	IEEE Xplore Digital Library
Author	Chen Bin Mao Qian Hu Zhaoyuan Wu Wei
Copyright Year	2011
Description	Author affiliation: Department of hydraulic engineering, Zhejiang Water Conservancy and Hydropower College, Hangzhou, China (Chen Bin; Mao Qian) \|\| Binhai Concrete Mixing Plant, Yuyao Mingfeng Building Materials Co. Ltd, Ningbo, China (Hu Zhaoyuan; Wu Wei)
Abstract	Several questions about predicting concrete property using BP artificial neural network have been discussed, including the selection of network structure, the determination of sample capacity and grouping method, the protection from over-fitting, and the comparison on precision of prediction. For the network-structure, it has been found that directly apply the consumption of raw-material and other crucial quality indices as the units of input can bring about a satisfactory result of prediction, in which a single hide layer holds 10 units and the workability along with the strength and durability formed two sub-networks simultaneously. For the sample capacity and grouping method, at least 100 sets of samples are necessary to find the intrinsic regularity, among them 1/3–1/4 should be taken as test samples. A new tactics for error-tracking has been proposed which is verified effective to avoid the over-fitting. The comparison of effectiveness and feasibility between BP neural networks and linear regression algorithm showed that BP neural networks have better performance in accuracy of prediction. Finally, an applicable software has been developed and used as examples to predict 163 sets of mixes for a ready-mixed concrete plant, to show its application in detail.
Starting Page	5863
Ending Page	5867
File Size	832914
Page Count	5
File Format	PDF
ISBN	9781457702891
e-ISBN	9781457702907
DOI	10.1109/ICETCE.2011.5775143
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Workability Automation Hydroelectric power generation Artificial neural networks Prediction Prediction algorithms Concrete Artificial neural network
Content Type	Text
Resource Type	Article

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