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首页> 外文会议>International Symposium on Structural Engineering for Young Experts; 20060818-21; Fuzhou amp; Xiamen(CN) >A New Method of Concrete Strength Evaluation
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A New Method of Concrete Strength Evaluation

机译：混凝土强度评估的新方法

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摘要

The accuracy of concrete strength evaluation has a great influence on the safety assessment of the structure. In the study,adaptive neuro —fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to evaluate concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So we adopt common logarithms of rebound value and strength value as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system,condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. Then,the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10. 316% and relative standard error is 12. 895% over all the 508 samples,which are satisfied with the requirements of practical engineering. The method efficiently maps the complex non-linear relationship between the concrete strength values and the rebound values, and provides a new way for the concrete strength evaluation.
机译：混凝土强度评估的准确性对结构的安全评估影响很大。在研究中，建立了基于Takagi-Sugeno规则的自适应神经模糊推理系统（ANFIS）来评估混凝土强度。根据专家经验，回弹值与混凝土强度之间的关系倾向于幂函数。因此，我们采用回弹值和强度值的常见对数作为ANFIS的输入和输出。系统参数集通过混合学习算法根据输入和输出数据样本进行迭代调整。在系统中，条件参数集可以通过反向传播梯度下降法确定，结论参数集可以通过最小二乘法确定。然后，可以通过模糊推理来推断混凝土强度。该方法充分利用了模糊神经网络（FNN）的能力特征，包括自动学习，生成和模糊逻辑推理。实验表明，在508个样本中，预测结果的平均相对误差为10. 316％，相对标准误差为12. 895％，满足了实际工程的要求。该方法有效地映射了混凝土强度值与回弹值之间的复杂非线性关系，为混凝土强度评估提供了新途径。

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