Pile foundation defect detection based on wavelet analysis and neural network
-
摘要: 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。Abstract: 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.
-
-
[1] 程志和,邓友生,孙雅妮,等.桩基检测技术的发展与应用研究[J].混凝土与水泥制品,2019(3):80-84. [2] 张丽萍.基于小波概率神经网络的单桩竖向承载力预测模型及应用研究[J].工业建筑,2012,42(9):107-109,161. [3] 谢洪阳,殷金泉,黄频波,等.小波变换与近似熵原理在桩基检测中的应用[J].无损检测,2012,34(4):35-37,41. [4] 周西峰,索会迎,郭前岗,等.基于小波包-BP网络的超声检测缺陷类型识别[J].解放军理工大学学报(自然科学版),2012,13(5):521-526. [5] 陈换过,江金寿,李剑敏,等.基于提升小波包和神经网络的结构损伤检测[J].振动·测试与诊断,2013,33(1):116-121,169. [6] 郭庆军.基于小波神经网络的旋耕装置机械故障诊断研究[J].农机化研究,2019,41(9):194-198,232. [7] 万振凯,王占刚.基于神经网络的复合材料缺陷超声波检测研究[J].纺织学报,2010,31(2):54-59. [8] 梁凯,韩庆邦.小波包能量谱和BP神经网络在波纹管压浆超声检测中的应用[J].声学技术,2020,39(2):151-156. [9] 李敬德,康维新.基于信息熵和BP神经网络的信号奇异点智能检测[J].牡丹江师范学院学报(自然科学版),2017(4):1-5,43. [10] 孙建平,王逢瑚,于海鹏.小波分析与人工神经网络在木质材料无损检测中的应用[J].木材工业,2004,18(5):24-26,33.
计量
- 文章访问数: 4
- HTML全文浏览量: 0
- PDF下载量: 3
下载:
