当移动荷载接近或远离结构损伤部位时，结构的振动响应的幅值和非线性特征会发生变化，可以从中提取有关结构损伤的信息；近似熵可以表达一个时间序列的复杂性和内在模式。用移动荷载作用于结构上，对其振动响应数据进行近似熵计算，提取其非线性特征值，进而用神经网络进行结构损伤模式识别。通过一个移动荷载作用下简支梁的计算实例考察了这一方法的有效性。移动荷载的作用，使得损伤造成的结构非线性特征更加显著；研究、计算与实测案例表明近似熵能够有效地表征信号的非线性程度，而且对噪声干扰的敏感度低，可以作为神经网络模式识别的特征向量。

The nonlinear character and amplitude of structural vibration will vary with the distance between load point and damage position. The structural damage information can be extracted from it. Approximate entropy can figure the complexity of a time serie. A moving load is put on the structure, calculate approximate entropy value of vibration data, and extract the nonlinear eigenvalue for the structural damage pattern recognition useing neural network. The validity of this method was reviewed by a beam under moving load. Moving load reinforces the structural nonlinear character. The results of simulation and experiment show that approximate entropy can figure the nonlinear grade of signal availably, and it is not sensitive to noise. Approximate entropy can be used for eigenvectors of pattern recognition based on neural network.