Techno Press
Techno Press

Computers and Concrete   Volume 16, Number 3, September 2015, pages 343-356
Concrete properties prediction based on database
Bin Chen, Qian Mao, Jingquan Gao and Zhaoyuan Hu

Abstract     [Full Text]
    1078 sets of mixtures in total that include fly ash, slag, and/or silica fume have been collected for prediction on concrete properties. A new database platform (Compos) has been developed, by which the stepwise multiple linear regression (SMLR) and BP artificial neural networks (BP ANNs) programs have been applied respectively to identify correlations between the concrete properties (strength, workability, and durability) and the dosage and/or quality of raw materials\'. The results showed obvious nonlinear relations so that forecasting by using nonlinear method has clearly higher accuracy than using linear method. The forecasting accuracy rises along with the increasing of age and the prediction on cubic compressive strength have the best results, because the minimum average relative error (MARE) for 60-day cubic compressive strength was less than 8%. The precision for forecasting of concrete workability takes the second place in which the MARE is less than 15%. Forecasting on concrete durability has the lowest accuracy as its MARE has even reached 30%. These conclusions have been certified in a ready-mixed concrete plant that the synthesized MARE of 7-day/28-day strength and initial slump is less than 8%. The parameters of BP ANNs and its conformation have been discussed as well in this study.
Key Words
    concrete; mix proportioning; prediction; database; linear regression; artificial neural network
Bin Chen, Qian Mao and Jingquan Gao, Institute of Water Resources and Environmental Engineering, Zhejiang University of Water Resources and Electric power, Xiasha University District, Hangzhou, China
Zhaoyuan Hu, Cixi Mingfeng Building Materials Co. Ltd, Ningbo, China

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