模糊神经网络在混凝土无损检测系统中的应用

Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788) Pub Date : 2004-06-15 DOI:10.1109/WCICA.2004.1341938

Jing Xu, Qingchun Meng, Song-Sen Yang, Wen Zhang, Changhong Song

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引用次数: 0

摘要

混凝土强度检测的准确性对建筑物的安全评价有很大的影响。为了提高混凝土强度评价的准确性，利用模糊神经网络的自动学习、生成和模糊逻辑推理能力，建立了模糊神经网络(FNN)对混凝土强度进行评价。实验表明，预测结果的最大相对误差为1.12%，满足工程要求。该方法有效地映射了钻孔值与回弹值之间复杂的非线性关系，为混凝土强度检测与评价提供了一种有效的方法。

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Application of fuzzy neural network in the system of concrete undamaged inspection

The accuracy of concrete strength inspection has a great influence on the safety evaluation of the building. In order to increase the accuracy, Fuzzy Neural Network (FNN) was built up to evaluate concrete stmngth: It takes full advantage of the characteristics of the common concrete testing methods: drill and rebound, and the abilities of FNN including automatic learning, generation and fuzzy logic inference. The experiment shows that the max relative error of the predicted results is 1.12%, which is satisfied with the requirements of the engineering. The method effieieatly maps the complex non-linear relationship between the drill values and the rebound values, and provides a efficient way for the concrete strength inspection and evaluation.

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Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788)

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