【作者】 徐茂辉；

【导师】 谢慧才；

【作者基本信息】 汕头大学 ， 结构工程， 2004， 硕士

【摘要】 在众多的钢筋混凝土无损检测方法中，探地雷达技术具有一些独特的优势，因而日益受到工程界的重视。但目前其在钢筋混凝土结构检测中的应用研究尚不多见，这影响到这一技术的推广应用。因此，本文针对目前探地雷达技术在钢筋混凝土结构检测中存在的一些难点问题，进行了一系列的应用基础研究。这些难点主要有：1)邻近钢筋间的雷达波相互干扰问题；2)混凝土的介电常数ε_r的选取问题，这将直接影响到混凝土深度(或厚度)的检测精度；3)钢筋直径的GPR探测问题，这是在雷达检测中一个长期未能解决的难题。 对前两个问题，本文主要采用模型试验方法。本文首先研究了各种相邻钢筋对雷达波的干扰问题，得出了一些有指导作用的经验。其次通过对试件探测研究了混凝土的相对介电常数ε_r与混凝土深度H之间的关系，研究发现ε_r会随H的增加而增大，因而我们提出按H分段估算ε_r的工程方法，算例表明该法能提高深度或厚度的探测精度。最后我们应用BP人工神经网络法对钢筋直径的识别进行了探索，以实验室制作的混凝土试件中采集到的雷达反射信号的每个正半波作为识别对象，在时域上选取波的峰值、波的能量、波的宽度以及波的斜率作为网络的输入参数，并采用对神经网络进行细分的方法使网络精度得以提高。网络识别结果表明BP神经网络识别出了钢筋的直径，并达到了工程满意的精度。 本文的工作在GPR技术用于钢筋混凝土的无损检测方面颇有新意，解决了一些实践中感兴趣却又难以测定的物理量的检测问题，具有很强的应用前景。更多还原

【Abstract】 Among the Non-Destructive Test methods that are used in the inspection of concrete structures, Ground Penetrating Radar(GPR) has some particular superiorities. So it is more and more attractive to the engineering fields. But up till now, there are few studies in GPR investigations of reinforce concrete structures, it affects the popularization of this technique. In order to resolve the difficulties in applying GPR to reinforce concrete structures, a series of studies have been made in this paper. These difficulties includes:l)the selection of concrete relative permittivity, this value will affect the surveying precision of concrete depths(or thickness) directly;2)the interaction of radar waves that are reflected from rebar groups;3)GPR inspection of rebar diameters, it is an difficulty that is not resolved by GPR survey.We adopt a model-test method to deal with the former two problems. First, Through the test studies of the interaction of radar waves that are reflected from rebar groups, we get some instructional experiences. Second, By surveying two concrete specimens, we find the relationship between concrete depths H and the relative permittivities of concrete E ,: ε r will increase along with the increment of depths. So we brought forward a practical method that ε r should be assessed according to subsections of H.A example shows that this method can improve the precision of the inspection of concrete depths. Finally, we probed into the auto identification of rebar size using a BP neural network. We take each half positive radar wave that collected from laboratory-cast concrete specimens as the identifying object. Then we select wave crest, energy of wave , wavelength and wave slope as the inputs of the BP network. We also subdivide the BP network into several networks which improved the precision of network. The outputs of the networks showed that we succeeded in assessing rebar sizes by the BP networks and the results meet the demand of engineer precision.Our work is original in the application of GPR to the nondestructive test of reinforce concrete structures and we solved some difficulties that are interesting and hard to settle. So our work has bright future in engineer application.更多还原