地震产生的建筑和拆除废弃物回收利用超光谱成像技术,辅助浅层神经网络技术
Giuseppe Bonifazi1, Riccardo Gasbarrone2, Davide Gattabria2
1Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, 00184 Rome, Italy.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|February 12, 2026
概括
这项研究引入了一种结合X射线光和高光谱成像的新方法,用于分类建筑和拆除废物. 该方法精确地对废物进行分类,提高循环经济的回收效率.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 建筑和拆除废物 (C&DW) 是一个主要的环境问题,回收率很高,但回收量很大.
- 实现循环经济目标需要改进C&DW的分类和利用.
研究的目的:
- 开发和验证一个综合分析方法,以快速准确地分类地震相关的C&DW.
- 评估组合便携式X射线光 (pXRF),近红外高光谱成像 (NIR-HSI) 和浅层神经网络 (SNN) 进行C&DW分类的可行性.
主要方法:
- 分析了意大利中部的30个C&DW样本,使用pXRF来定义材料类:基于混凝土的 (CON),富含陶的 (CER) 和天然聚合物 (NAT).
- 处理NIR-HSI频谱 (1000-1700nm) 来训练一个SNN分类器.
- 使用统计测试和主要组件分析 (PCA) 来确认类差异化.
主要成果:
- 在CON,CER和NAT类中,SNN分类器实现了优异的性能指标 (精度,回忆,特异性,F1分数≥0.98).
- 错误分类是最小的,主要发生在边界情况下,如玻璃陶.
- 综合方法在分类C&DW方面表现出高准确性和稳定性.
结论:
- 结合NIR-HSI和SNN,为自动化C&DW分类提供了一个快速,稳健和可转移的策略.
- 这种方法通过提高材料回收和回收效率来支持循环经济目标.
- 该研究验证了C&DW表征和分类的高性能框架.
关键词:
建筑和拆除废物 (C&DW) 是指建筑和拆除废物的废物.超频谱成像 (HSI) 是一种高频谱成像技术.NN (神经网络) 是一个神经网络.VIS-NIR (可见和近红外) 系统这是X射线光 (XRF).更多相关视频
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