摘要
波动方程的有限元数值解表明,对锚杆灌浆体内部缺陷的探测,可用声速、幅值、波形等声参数作为判断的依据。基于小波包分析得到的能量特征向量,可以作为缺陷特征向量进行无损检测。人工神经网络这类非线性动力学系统运用于该灰色系统的质量预测,可取得良好的效果。
FEA numeric solution of wave equation indicates that deductions must comply with acoustic parameters such as velocity of sound, amplitude, wave shape etc. when inspecting interior flaws of grout of anchor bar. Basing on wavelet packet analysis, energy eigenvector is a flaw vector, which could be regarded as basis in anchors' non-destructive inspection. As a nonlinear dynamical system, artificial neural networks dealing with quality inspection of gray system have been proved efficient.
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2003年第2期262-265,共4页
Rock and Soil Mechanics
基金
国家计委高新技术(No. [1999]2062)
