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Surface mortar detection and performance evaluation of recycled aggregates based on hyperspectral technology

  • Wenqian Liu

    Wenqian Liu received his B.Sc. degree from Jiujiang University in 2022. He is currently a master student at Huaqiao University. His main research interests include quality evaluation and strengthening method of recycled aggregates.

         , Huaiying Fang

    Huaiying Fang received her Ph.D. degree from Huaqiao University in 2012. She is currently a professor at Huaqiao University. Her main research interests include system development for recycled aggregates quality evaluation.

         , Jianhong Yang

    Jianhong Yang received his M.Sc. degree from Huaqiao University in 2004 and received his Ph. D. degree from Huaqiao University and Tohoku University in 2010. He is currently a professor at Huaqiao University. His main research interests include multimodal vision inspection method and system development and multi-platform based machine deep learning algorithms.

     EMAIL logo     and Guoyi Tan

    Guoyi Tan received his M.Sc. degree from Huaqiao University in 2023. His main research interests include method and system development for quality testing of recycled aggregates.
Published/Copyright: October 25, 2023
tm - Technisches Messen
From the journal tm - Technisches Messen Volume 91 Issue 12

Abstract

The quality of recycled aggregates is affected by the residual mortar. It is significant to detect the surface mortar distribution of recycled aggregates after mortar removal by mechanical crushing. From this perspective, a method to accurately detect the surface mortar distribution of recycled aggregates is proposed. The processed hyperspectral features were obtained by applying data filtering and screening, L2 norm processing, feature transforming and dimensionality reduction. Then these features were put into an extreme learning machine (ELM) for offline training, and a sliding window processing mechanism was added to the trained model, which was used to detect the recycled aggregates and output the category images. Finally, two characterization parameters of the proportion of mortar area and the mortar volume were extracted from the images. The regression models of water absorption (WA) and apparent density (AD) of recycled aggregates were obtained based on the proportion of mortar area and the mortar volume, with the determination coefficients of 0.99. The results demonstrated that the proposed approach could be profitably applied to evaluate the quality of the recycled aggregates, which lays a foundation for visual identification and intelligent sorting of recycled aggregates.

Zusammenfassung

Die Qualität von rezyklierten Gesteinskörnungen wird durch den Restmörtelanteil beeinflusst. Es ist von großer Bedeutung, die Verteilung des Oberflächenmörtels von rezyklierten Gesteinskörnungen nach der Entfernung des Mörtels durch mechanisches Zerkleinern zu erkennen. In dieser Perspektive wird eine Methode vorgeschlagen, um die Oberflächenmörtelverteilung von rezyklierten Gesteinskörnungen genau zu erkennen. Die verarbeiteten hyperspektralen Merkmale wurden durch Datenfilterung und -screening, L2-Norm-Verarbeitung, Merkmalserkennung und Dimensionsreduzierung erhalten. Dann wurden diese Merkmale in eine extreme learning maschine (ELM) zum Offline-Training eingegeben und ein Mechanismus zur Verarbeitung von gleitenden Mittelwerten wurde dem trainierten Modell hinzugefügt, das zur Erkennung der rezyklierten Aggregate dient und die Bilder Kategorien zuordnet. Schließlich wurden aus den Bildern zwei Charakterisierungsparameter des Anteils der Mörtelfläche und des Mörtelvolumens extrahiert. Die Regressionsmodelle für Wasseraufnahme (WA) und scheinbare Dichte (AD) von rezyklierten Gesteinskörnungen wurden auf der Grundlage des Anteils der Mörtelfläche und des Mörtelvolumens mit einem Bestimmtheitsmaß von 0,99 erhalten. Die Ergebnisse zeigten, dass der vorgeschlagene Ansatz gut zur Bewertung der Qualität des Recyclingmaterials angewendet werden kann, was eine Grundlage für die visuelle Identifikation und intelligente Sortierung von rezyklierten Gesteinskörnungen darstellt.

Keywords: recycled aggregates; mortar distribution; hyperspectral technology; regression models
Schlüsselwörter: Rezyklierte Gesteinskörnungen; Mörtelverteilung; Hyperspektraltechnologie; Regressionsmodelle
Corresponding author: Jianhong Yang, College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, China, E-mail:

Funding source: Major Program of Industry and University Cooperation of Fujian Province

Award Identifier / Grant number: 2021H6029

Funding source: Science and Technology Project of Quanzhou

Award Identifier / Grant number: 2022GZ3

About the authors

Wenqian Liu

Wenqian Liu received his B.Sc. degree from Jiujiang University in 2022. He is currently a master student at Huaqiao University. His main research interests include quality evaluation and strengthening method of recycled aggregates.

Huaiying Fang

Huaiying Fang received her Ph.D. degree from Huaqiao University in 2012. She is currently a professor at Huaqiao University. Her main research interests include system development for recycled aggregates quality evaluation.

Jianhong Yang

Jianhong Yang received his M.Sc. degree from Huaqiao University in 2004 and received his Ph. D. degree from Huaqiao University and Tohoku University in 2010. He is currently a professor at Huaqiao University. His main research interests include multimodal vision inspection method and system development and multi-platform based machine deep learning algorithms.

Guoyi Tan

Guoyi Tan received his M.Sc. degree from Huaqiao University in 2023. His main research interests include method and system development for quality testing of recycled aggregates.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. The primary contributions of each author are as follows: Wenqian Liu, Huaiying Fang, Guoyi Tan: performed the investigation and formal analysis. Wenqian Liu, Huaiying Fang: wrote the original draft. Jianhong Yang: provided the methodology and supervision. Jianhong Yang, Huaiying Fang: wrote review & editing.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: The Major Program of Industry and University Cooperation of Fujian Province (2021H6029). The Science and Technology Project of Quanzhou (2022GZ3).

  5. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2023-07-12
Accepted: 2023-09-28
Published Online: 2023-10-25
Published in Print: 2025-12-17

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Measurement systems and sensors with cognitive features III
  4. Research Articles
  5. Opportunities of artificial intelligence in the field of calibration services
  6. Neuronale Netze zur Startwertschätzung bei der Identifikation piezoelektrischer Materialparameter
  7. Spectral reconstruction using neural networks in filter-array-based chip-size spectrometers
  8. Prediction of water absorption of recycled coarse aggregate based on deep learning image segmentation
  9. Surface mortar detection and performance evaluation of recycled aggregates based on hyperspectral technology
  10. A comparative study of classification methods for state recognition in injection molding
https://doi.org/10.1515/teme-2023-0106
Keywords for this article
recycled aggregates; mortar distribution; hyperspectral technology; regression models

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Measurement systems and sensors with cognitive features III
  4. Research Articles
  5. Opportunities of artificial intelligence in the field of calibration services
  6. Neuronale Netze zur Startwertschätzung bei der Identifikation piezoelektrischer Materialparameter
  7. Spectral reconstruction using neural networks in filter-array-based chip-size spectrometers
  8. Prediction of water absorption of recycled coarse aggregate based on deep learning image segmentation
  9. Surface mortar detection and performance evaluation of recycled aggregates based on hyperspectral technology
  10. A comparative study of classification methods for state recognition in injection molding
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