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A robust classification algorithm for separation of construction waste using NIR hyperspectral system.

Wen Xiao1, Jianhong Yang1, Huaiying Fang1

  • 1Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Huaqiao University, Xiamen, Fujian Province, China.

Waste Management (New York, N.Y.)
|May 16, 2019
PubMed
Summary
This summary is machine-generated.

Near-infrared hyperspectral technology and a novel Pythagorean Wavelet Transform (PWT) method effectively classify construction waste. This approach enhances feature extraction, improving accuracy for materials like plastics, rubber, and concrete.

Keywords:
Classification modelConstruction waste recyclingNear-infrared spectrumPotential features

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Area of Science:

  • Materials Science
  • Spectroscopy
  • Environmental Engineering

Background:

  • Construction waste management faces challenges in utilization, cost-efficiency, and processing speed.
  • Near-infrared hyperspectral technology offers potential for automated waste identification.
  • Existing methods may struggle with data redundancy and distinguishing similar spectral signatures.

Purpose of the Study:

  • To develop an efficient method for classifying construction waste using near-infrared hyperspectral technology.
  • To improve the accuracy and robustness of construction waste identification under complex conditions.
  • To reduce processing costs and enhance the utilization rate of construction waste.

Main Methods:

  • Proposed Pythagorean Wavelet Transform (PWT) for characteristic reflectivity extraction, reducing hyperspectral data redundancy.
  • Extracted and evaluated additional features: first derivative and intrinsic mode function (IMF).
  • Employed Random Forest (RF) for trend-feature identification and Extreme Learning Machine (ELM) for amplitude-feature identification.
  • Developed a complementary troubleshooting (CT) method combining ELM and RF for online identification.

Main Results:

  • PWT retained more detailed information and enhanced spectral differences compared to standard Wavelet Transform (WT).
  • First derivative and IMF were identified as effective features for distinguishing similar spectra.
  • The CT method, using ELM for initial identification and RF for verification, improved model robustness.
  • Achieved 100% accuracy in classifying 180 samples across six construction waste types (woods, plastics, bricks, concretes, rubbers, black bricks).

Conclusions:

  • The proposed PWT-based hyperspectral approach combined with a complementary troubleshooting (CT) method offers a highly accurate and robust solution for construction waste classification.
  • This technology can significantly improve construction waste management by enhancing identification accuracy and efficiency.
  • The method demonstrates potential for real-world application in automated waste sorting and recycling processes.