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

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  • Haiying Wang
  • Xinping Wang
  • Chao Wang
  • Jian Xu

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.

Suggested Citation

  • Haiying Wang & Xinping Wang & Chao Wang & Jian Xu, 2019. "Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, February.
    • Handle: RePEc:hin:jnlmpe:4952036
    DOI: 10.1155/2019/4952036
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    Cited by:

    1. Haiying Wang & Linhao Liang & Jian Xu & Hui She & Wuxiang Li, 2020. "A quadratic weighted centroid algorithm for tunnel personnel positioning," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.

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