Fracture mode identification in cementitious materials using supervised pattern recognition of acoustic emission features
Fracture mode identification in cementitious materials using supervised pattern recognition of acoustic emission features
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DOI：
10.1016/j.conbuildmat.2014.05.015
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发表时间：
2014-09-30
影响因子：
7.4
通讯作者：
Aggelis, Dimitrios G.
中科院分区：
文献类型：
--
作者：
Farhidzadeh, Alireza;Mpalaskas, Anastasios C.;Aggelis, Dimitrios G.
混凝土作为一种普遍存在于土木结构中的胶凝材料，其开裂问题一直是世界范围内工程领域的一个重要问题。声发射（AE）在监测这些结构的研究和实验室实验中显示出有希望的结果，导致大量的混凝土结构报告，文章和建议。这些研究大多集中在裂纹模式检测上，以估计损伤的重要性，因为一般来说，剪切现象表明严重的损伤，发生在拉伸（弯曲）开裂之后。在一些声发射参数中，如ra值和平均频率（AF），嵌入了裂缝模式的独特标志。剪切断裂发出的信号比拉伸断裂发出的信号具有更高的ra值和更小的AF。然而，由于构件几何形状、材料特性、传感器位置和响应等参数的影响，这些特征的分类并没有统一的固定边界。此外，声发射虽然是由一组随机数据组成，但在数据处理中并没有充分考虑到不确定性的作用。为了克服这些不足，本文提出了一种称为支持向量机的模式分类器技术。进行小尺度断裂实验，施加可控的裂缝模式，记录每种裂缝模式的声发射数据，并评估分类器的性能。结果表明，该方法能较好地估计声发射特征及其相关不确定度的分类边界。传感器距离作为一个重要参数对分类边界变化的影响是可以量化的。此外，还检查了其他特征集（即RA和AF以外的特征集）用于分类的充分性。(C) 2014 Elsevier Ltd.版权所有。
Cracking in concrete as a ubiquitous cementitious material in civil structures has been a worldwide critical issue in the field of engineering. Acoustic emission (AE) has demonstrated promising outcomes in research and laboratory experiments for monitoring these structures that led to plethora of reports, articles and recommendations for concrete structures. Many of these studies focus on cracking mode detection to estimate the significance of damage because in general, shear-like phenomena indicate severe damage and occur after tensile (flexural) cracking. The distinctive signs of the cracking modes are embedded in some AE parameters like the RA-value and average frequency (AF). Signals emitted from shear fracture exhibit higher RA-values with smaller AF than tensile ones. However, there are no universally fixed boundaries for classification of these features due to the parameters like member geometry, material properties sensor location and response. In addition, although AE consists of a random set of data, the role of uncertainty is not fully taken into account in data processing. To overcome these deficiencies, this article proposes a pattern classifier technique titled support vector machines. Small-scale fracture experiments were carried out to impose controlled cracking modes, record AE data for each cracking mode, and evaluate the performance of classifiers. The results show that the classification boundaries for AE features and their associate uncertainties could be successfully estimated. The effect of sensor distance as an imperative parameter in variation of classification boundaries could be quantified. Furthermore, the adequacy of other feature sets (i.e., other than RA and AF) for classification was also examined. (C) 2014 Elsevier Ltd. All rights reserved.