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Automated Distinction between Cement Paste and Aggregates of Concrete Using Laser-Induced Breakdown Spectroscopy.
Pakdad Pourbozorgi Langroudi1, Gesa Kapteina1, Marcus Illguth1
1Civil Engineering Department, The University of the Built Environment and Metropolitan Development, HafenCity Universität Hamburg (HCU), Henning-Voscherau-Platz 1, 20457 Hamburg, Germany.
Materials (Basel, Switzerland)
|August 27, 2021
Summary
A new machine learning method automates distinguishing cement paste from aggregates in concrete using laser-induced breakdown spectroscopy (LIBS) data. This improves elemental analysis accuracy for heterogeneous materials.
Area of Science:
- Materials Science
- Analytical Chemistry
- Geoscience
Background:
- Laser-induced breakdown spectroscopy (LIBS) offers fast, high-resolution elemental analysis.
- Concrete analysis requires distinguishing cement paste from aggregates for accurate elemental quantification.
- Existing methods struggle with cement paste and aggregate chemical similarity, leading to inaccuracies.
Purpose of the Study:
- To develop and validate a machine learning method for automated cement paste and aggregate distinction in LIBS data.
- To improve the reliability of elemental analysis in heterogeneous materials like concrete.
Main Methods:
- Utilized machine learning algorithms to classify LIBS spectral data.
- Developed a method to differentiate between cement paste and aggregate components within concrete samples.
Main Results:
- Successfully automated the distinction between cement paste and aggregates using LIBS data.
- The machine learning approach provides reliable elemental distribution information.
Conclusions:
- The developed machine learning method enhances the accuracy of LIBS analysis for concrete.
- This approach has potential applications for analyzing other complex, heterogeneous materials.
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