Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model

Haiying Wang, Xinping Wang, Chao Wang and Jian Xu

Mathematical Problems in Engineering, 2019, vol. 2019, 1-10

Abstract:

Firstly, a genetic algorithm (GA) and simulated annealing (SA) optimized fuzzy c-means clustering algorithm (FCM) was proposed in this paper, which was developed to allow for a clustering analysis of the massive concrete cube specimen compression test data. Then, using an optimized error correction time series estimation method based on the wavelet neural network (WNN), a concrete cube specimen compressive strength test data estimation model was constructed. Taking the results of cluster analysis as data samples, the short-term accurate estimation of concrete quality was carried out. It was found that the mean absolute percentage error, e 1 , and the root mean square error, e 2 , for the samples were 6.03385% and 3.3682KN, indicating that the proposed method had higher estimation accuracy and was suitable for concrete compressive test data short-term quality estimations.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:4952036

DOI: 10.1155/2019/4952036

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