引入一种小波分析结合神经网络的桩基检测方法，根据桩基中超声波传播的特点，利用小波分析对采集的超声波信号进行小波包分解，对分解后的信号进行归一化处理，将超声波信号矩阵化，构建表征桩基缺陷信息的特征向量；再取多组特征向量作为神经网络的训练样本，对特征向量进行训练学习，并将未诊断样本输入神经网络进行识别验证。试验数据表明，通过小波分析方法获取超声波信号特征向量并构建的神经网络可以有效识别出桩基缺陷以及缺陷类型。

A pile detection method combining wavelet analysis and neural network is introduced. According to the characteristics of ultrasonic propagation in the pile foundation, the collected ultrasonic signals are analyzed by using wavelet analysis. The method performs wavelet packet decomposition, normalizes the decomposed signal and constructs a feature vector of ultrasonic signal to characterize pile foundation defect information. Furthermore, multiple sets of feature vectors were taken as training samples of the neural network in order to train and learn the feature vectors. The non-diagnosed samples were input into the neural network for identification verification. Experimental data shows that the trained neural network can effectively identify pile foundation defects and defect types.