Prediction of Concrete Strength Based on BP Neural Network

Abstract:

Based on BP neural network, this paper had a prediction analysis on concrete strength at the 28th day. Taking the influences of application amount of cement, application amount of scoria, application amount of fly-ash, water-ash ratio into account, the prediction model of concrete strength at the 28th day based on BP neural network was obtained. It was found that, the average value of absolute value for the relative error of fitting value of concrete strength at the 28th day compared with the observed value for 43 groups of independent variables training BP neural network model was 0.64372%; And the relative error of prediction value of concrete strength at the 28th day was -2.0392%~0.964668% compared with the observed value for 5 groups of independent variables validating BP neural network model, and the average value of absolute value for the relative error was 0.95467%. The following conclusion can be drawn that, the prediction model is accurate and credible, and the BP neural network is a excellent method, and it is worthy to spread its application in the prediction analysis of concrete strength at the 28th day.

• Periodical: Advanced Materials Research (Volumes 341-342)
• Pages: 58-62
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.341-342.58
• Online since: September 2011
• Authors: Jian Ping Jiang
• Keywords: BP Neural Network (BPNN), Concrete, Prediction, Strength

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