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首页> 外文期刊>Spectrochimica Acta, Part B. Atomic Spectroscopy >2D evaluation of spectral Laser Induced Breakdown Spectroscopy data derived from heterogeneous materials using cluster algorithm
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2D evaluation of spectral Laser Induced Breakdown Spectroscopy data derived from heterogeneous materials using cluster algorithm

机译:光谱激光诱导击穿光谱数据的2D评估使用聚类算法衍生自异构材料的击穿光谱数据

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Laser-induced Breakdown Spectroscopy (LIBS) is capable of providing spatially resolved element maps in regard to the chemical composition of the sample. The evaluation of heterogeneous materials is often a challenging task, especially in the case of phase boundaries. In order to determine information about a certain phase of a material, the need for a method that offers an objective evaluation is necessary. This paper will introduce a cluster algorithm in the case of heterogeneous building materials (concrete) to separate the spectral information of non-relevant aggregates and cement matrix. In civil engineering, the information about the quantitative ingress of harmful species like Cl-, Na+ and SO42- is of great interest in the evaluation of the remaining lifetime of structures (Millar et al., 2015; Wilsch et al., 2005). These species trigger different damage processes such as the alkali-silica reaction (ASR) or the chloride-induced corrosion of the reinforcement. Therefore, a discrimination between the different phases, mainly cement matrix and aggregates, is highly important (Weritz et al., 2006). For the 2D evaluation, the expectation-maximization-algorithm (EM algorithm; Ester and Sander, 2000) has been tested for the application presented in this work. The method has been introduced and different figures of merit have been presented according to recommendations given in Haddad et al. (2014). Advantages of this method will be highlighted. After phase separation, non-relevant information can be excluded and only the wanted phase displayed. Using a set of samples with known and unknown composition, the EM-clustering method has been validated regarding to Gustavo Gonzalez and Angeles Herrador (2007). (C) 2017 Elsevier B.V. All rights reserved.
机译:激光诱导的击穿光谱(Libs)能够在样品的化学成分方面提供空间分辨的元件图。异构材料的评估通常是一个具有挑战性的任务,特别是在相界的情况下。为了确定有关材料的某个阶段的信息,需要一种提供提供客观评估的方法。本文将在异构建筑材料(混凝土)的情况下引入集群算法,以分离非相关聚集体和水泥矩阵的光谱信息。在土木工程中,有关Cl-,Na +和SO42等有害物种的定量进入的信息 - 对评估结构的剩余寿命(Millar等,2015; Wilsch等,2005)的评估非常令人兴趣。这些物种触发不同的损伤方法,例如碱二氧化硅反应(ASR)或氯化物诱导的增强腐蚀。因此,不同阶段之间的歧视,主要是水泥基质和聚集体,非常重要(Weritz等,2006)。对于2D评估,预期最大化算法(EM算法; ESER和Sander,2000)已经过测试了本工作中提出的应用。已经引入了该方法,并根据Haddad等人提供的建议呈现了不同的优点图。 (2014)。将突出此方法的优点。在相分离后,可以排除非相关信息,并且仅显示所需的相位。使用具有已知和未知组成的一组样本,EM-Clustering方法已被验证到Gustavo Gonzalez和Angeles Herrador(2007)。 (c)2017 Elsevier B.v.保留所有权利。

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