Automated Mineral Identification of Pozzolanic Materials Using XRD Patterns
Publication: Journal of Materials in Civil Engineering
Volume 36, Issue 12
Abstract
Use of pozzolanic materials in concrete has become increasingly popular due to their ability to improve long-term strength and durability. To better understand their reactivity and reaction mechanism, proper characterization of their chemical properties is required. X-ray diffraction (XRD) is a widely used nondestructive testing method that can identify the different constituent phases of pozzolanic materials. However, the conventional qualitative approach for XRD analysis can be challenging due to the significant peak overlap and an amorphous hump in the baseline of XRD patterns. To address this issue, this study proposes a deep learning–based automated method for accurately identifying the minerals in pozzolanic materials using XRD patterns. The proposed method involves the deep learning–based peak picking algorithm, establishment of mineral candidates, and automated mineral identification using an optimization process. The performance of the model was validated using eight pozzolanic materials belonging to four classes (slag, fly ash, ground bottom ash, and silica fume).
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by an internal grant (Carbon Neutrality in Cement Industry: Database and Machine Learning for Ecofriendly Construction Materials, Code: 20220482-001) from the Korea Institute of Civil Engineering and Building Technology (KICT), Republic of Korea.
Author contributions: Hyunjun Kim: conceptualization, methodology, validation, and writing–original draft. Jinyoung Yoon: writing–original draft, writing–review and editing, and supervision.
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Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 36 • Issue 12 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Oct 23, 2023
Accepted: May 2, 2024
Published online: Sep 27, 2024
Published in print: Dec 1, 2024
Discussion open until: Feb 27, 2025
ASCE Technical Topics:
- Ashes
- Automatic identification systems
- Chemical processes
- Chemical properties
- Chemistry
- Detection methods
- Engineering fundamentals
- Engineering materials (by type)
- Engineering mechanics
- Environmental engineering
- Fly ash
- Geomechanics
- Geotechnical engineering
- Material durability
- Material mechanics
- Material properties
- Materials engineering
- Methodology (by type)
- Minerals
- Radiation
- Soil mechanics
- Soil properties
- Strength of materials
- Waste management
- Waste treatment
- X rays
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