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A PRELIMINARY STUDY ON REAL-TIME ASBESTOS RECOGNITION IN TUNNEL SPOIL OF GREEN STONES

  • Sergio Malinconico - Department of new technologies for occupational safety of industrial plants, products and human settlements, Italian Workers’ Compensation Authority, Italy
  • Federica Paglietti - Department of new technologies for occupational safety of industrial plants, products and human settlements, Italian Workers’ Compensation Authority, Italy
  • Sergio Bellagamba - Department of new technologies for occupational safety of industrial plants, products and human settlements, Italian Workers’ Compensation Authority, Italy
  • Silvia Serranti - Research and Service Center for Sustainable Technological Innovation (Ce.R.S.I.Te.S.), Sapienza - University of Rome, Italy
  • Giuseppe Bonifazi - Research and Service Center for Sustainable Technological Innovation, Sapienza - University of Rome, Italy
  • Davide Gattabria - Research and Service Center for Sustainable Technological Innovation, Sapienza - University of Rome, Italy
  • Ivano Lonigro - Research and Service Center for Sustainable Technological Innovation, Sapienza - University of Rome, Italy
  • Riccardo Gasbarrone - Research and Service Center for Sustainable Technological Innovation, Sapienza - University of Rome, Italy

DOI 10.31025/2611-4135/2024.19441

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Copyright: © 2024 CISA Publisher

Abstract

This study focuses on the analysis of serpentinite rocks to detect asbestos minerals, with a particular emphasis at discriminating chrysotile from other minerals and matrices (antigorite, lizardite) in serpentinites. The primary objective of this exploratory study is to lay the groundwork for the development of an innovative system for real-time identification of asbestos on the surface of materials and rocks. In more detail, the study emphasizes the advantages of utilization of rapid and reliable portable instrumentation, aided by machine learning (ML) techniques. Serpentinite rock debris from tunnel excavation activities (i.e. tunnel spoil) were collected at the Cravasco railway tunnelling site (Genoa - Italy) of the TEN-T Rhine-Alps Core Corridor. On-site analysis was conducted using the ASD FieldSpec 4 Standard-Res spectrophotoradiometer (350-2500 nm). A two-class classifier was developed based on acquired reflectance spectra, to discriminate between the "Presence of asbestiform fibers" and the "Absence of asbestiform fibers". The classification model was calibrated through stereomicroscopic inspection and electron microscopy carried on selected sample fractions.

Keywords

Editorial History

  • Received: 27 May 2024
  • Revised: 04 Sep 2024
  • Accepted: 04 Oct 2024
  • Available online: 19 Nov 2024

References

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