地震で発生した建設廃棄物の回収は,浅層ニューラルネットワーク技術による超スペクトル画像を用いて行われます
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
まとめ
この研究は,建設廃棄物および破壊廃棄物を分類するために,X線光と高スペクトル画像を組み合わせた新しい方法を導入しています. このアプローチは,廃棄物を正確に分類し,循環経済のためのリサイクル効率を高めます.
科学分野:
- 環境科学 環境科学
- マテリアルサイエンス 材料科学
- データサイエンス データサイエンス
背景:
- 建設廃棄物 (C&DW) は主要な環境問題であり,高いリサイクリング率と重要なダウンサイクリングがあります.
- 循環経済目標を達成するには,C&DWの分類と利用を改善する必要があります.
研究 の 目的:
- 地震関連のC&DWの迅速かつ正確な分類のための統合分析方法を開発し,検証する.
- 携帯型X線光 (pXRF),近赤外線超スペクトル画像 (NIR-HSI),および浅層ニューラルネットワーク (SNN) をC&DWの分類のために組み合わせることの実現可能性を評価する.
主な方法:
- イタリア中部から30個のC&DWサンプルをpXRFを用いて分析し,コンクリートベースの (CON),セラミックに富んだ (CER),天然積層 (NAT) の材料クラスを定義した.
- 処理されたNIR-HSIスペクトル (1000-1700 nm) をSNN分類器を訓練するために.
- クラス差別化を確認するために,統計的テストと主要構成要素分析 (PCA) を利用しました.
主要な成果:
- SNN分類器は,CON,CER,NATクラスの優れた性能指標 (精度,リコール,特異性,F1スコア ≥0.98) を達成しました.
- 誤った分類は最小限であり,主にガラス化された陶器などの境界的なケースで発生しました.
- 統合方法は,C&DWの分類において高い正確性と信頼性を示した.
結論:
- NIR-HSIとSNNの組み合わせは,自動化されたC&DW分類のための迅速で,堅牢で,転送可能な戦略を提供します.
- このアプローチは,材料の回収とリサイクル効率を改善することによって,循環経済目標をサポートします.
- この研究は,C&DWの特徴付けと分類のための高性能フレームワークを検証しています.
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