Concrete strength evaluation based on fuzzy neural networks
Song-Sen Yang; Jing Xu; Guang-Zhu Yao
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Volume 6, Issue , 26-29 Aug. 2004 Page(s): 3344 - 3347 vol.6
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Summary: The accuracy of concrete strength inspection has a great influence on the safety evaluation of the building. In order to improve the accuracy, fuzzy neural network (FNN) was built to evaluate concrete strength. It takes full advantage of the merits of the common concrete testing methods, i.e. rebounding and drilling core, and the abilities of FNN including self-learning, generation and fuzzy logic inference. Verification test shows that the max relative error of the predicted results is 1.12%, which meets the need of practical engineering. The approach effectively maps the complex non-linear relationship between rebounding value and concrete strength, and provides a efficient way for the concrete strength detection and evaluation.

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