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SPECTROSCOPY AND CHEMOMETRICS FOR RAPID CLASSIFICATION OF CONSTRUCTION AND DEMOLITION WASTE AS A POTENTIAL APPLICATION IN POST-EARTHQUAKE WASTE MANAGEMENT

  • Giuseppe Bonifazi - Department of Chemical Engineering, Materials & Environment (DICMA), Sapienza University of Rome, Italy
  • Giuseppe Capobianco - Department of Chemical Engineering, Materials & Environment (DICMA), Sapienza University of Rome, Italy
  • Riccardo Gasbarrone - Research and Service Center for Sustainable Technological Innovation (Ce.R.S.I.Te.S.), Sapienza University of Rome, Italy
  • Roberta Palmieri - Department of Chemical Engineering, Materials & Environment (DICMA), Sapienza University of Rome, Italy
  • Silvia Serranti - Department of Chemical Engineering, Materials & Environment (DICMA), Sapienza University of Rome, Italy

DOI 10.31025/2611-4135/2026.20619

Released under All rights reserved

Copyright: © 2026 CISA Publisher

Abstract

Construction and demolition waste (C&DW, chapter 17 of the European Waste Catalogue: EWC) is highly heterogeneous, and its rapid characterization is important for safe handling, sorting, recycling and appropriate waste management. These challenges become particularly critical in emergency and post-earthquake scenarios, where large quantities of mixed construction materials may be generated and rapidly require preliminary screening and routing. This study proposes a spectroscopy–chemometrics framework based on visible–short‑wave infrared (Vis–SWIR, 350–2500 nm) reflectance measurements, acquired with an ASD FieldSpec 4 full‑range spectroradiometer, to classify earthquake‑generated C&DW according to EWC codes, main material categories and hazard status. Representative samples of major C&DW material groups, including concrete and mortar, bricks and ceramics, natural aggregates, insulation materials and potentially contaminated components, were characterized in reflectance mode and labelled according to their assigned EWC code, material macro‑class, and hazardous/non‑hazardous status. Partial Least Squares–Discriminant Analysis (PLS‑DA) models were developed in hierarchical mode focusing on the binary distinction between pre-defined hazardous and non-hazardous classes and on the classification of EWC codes. The hierarchical PLS-DA approach achieved high prediction accuracy, demonstrating the potential of Vis–SWIR spectroscopy and multivariate analysis as a rapid screening and decision-support tool for C&DW classification and EWC-based sorting, with relevance to post-earthquake waste management.

Keywords

Editorial History

  • Received: 26 Jun 2026
  • Revised: 10 Sep 2026
  • Accepted: 16 Sep 2026
  • Available online: 30 Sep 2026

References

Directive, E.C. (2008). Directive 2008/98/EC of the European Parliament and of the Council of 19 November 2008 on waste and repealing certain Directives. Official Journal of the European Union L, 312(3), 22

Ballabio, D., & Consonni, V. (2013). Classification tools in chemistry. Part 1: linear models. PLS-DA. Analytical Methods, 5(16), 3790-3798.
DOI 10.1039/C3AY40582F

Ballabio, D., Grisoni, F., & Todeschini, R. (2018). Multivariate comparison of classification performance measures. Chemometrics and Intelligent Laboratory Systems, 174, 33-44.
DOI 10.1016/j.chemolab.2017.12.004

Bonifazi, G., Capobianco, G., Serranti, S., Palmieri, R., & Trotta, O. (2025a). Support vector machine-discriminant analysis applied to post-earthquake building waste classification based on hyperspectral imaging. Paper presented at the Proc.SPIE

Bonifazi, G., Bellagamba, S., Capobianco, G., Gasbarrone, R., Lonigro, Iv., Malinconico, S., . . . Serranti, S. (2025b). Short-Wave Infrared Spectroscopy for On-Site Discrimination of Hazardous Mineral Fibers Using Machine Learning Techniques. Sustainability, 17(3), 972.
DOI 10.3390/su17030972

Bonifazi, G., Gasbarrone, R., Gattabria, D., Palmieri, R., & Serranti, S. (2026). Earthquake-generated construction and demolition waste recovery using hyperspectral imaging aided by shallow neural networks technique. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 353, 127560.
DOI 10.1016/j.saa.2026.127560

Bonifazi, G., Grosso, Chiara, P., Roberta, & Serranti, S. (2025c). Current trends and challenges in construction and demolition waste recycling. Current Opinion in Green and Sustainable Chemistry, 53, 101032.
DOI 10.1016/j.cogsc.2025.101032

Bonifazi, G., Palmieri, R., & Serranti, S. (2015). Short wave infrared hyperspectral imaging for recovered post-consumer single and mixed polymers characterization. Paper presented at the Image Sensors and Imaging Systems 2015

Chien, C.-F., Aviso, K., Tseng, M.-L., Fujii, M., & Lim, M.K. (2023). Solid waste management in emerging economies: opportunities and challenges for reuse and recycling. Resources, Conservation and Recycling, 188, 106635.
DOI 10.1016/j.resconrec.2022.106635

Chileshe, N., Jayasinghe, R.S., & Rameezdeen, R. (2019). Information flow-centric approach for reverse logistics supply chains. Automation in Construction, 106, 102858.
DOI 10.1016/j.autcon.2019.102858

Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27(8), 861-874

Gasbarrone, R., Currà, A., Cardillo, A., Bonifazi, G., & Serranti, S. (2018). Near infrared spectroscopy of human muscles (Vol. 10489): SPIE

Huang, B., Wang, X., Kua, Harnwei, G., Yong, B., Raimund, & Ren, J. (2018). Construction and demolition waste management in China through the 3R principle. Resources, Conservation and Recycling, 129, 36-44.
DOI 10.1016/j.resconrec.2017.09.029

Inc., ASD. (2008). RS3™ User Manual, ASD Document 600545. In (Rev. E ed.): ASD Inc., Boulder, CO, United States

Inc., ASD. (2015). FieldSpec® 4 User Manual, ASD Document 600979. Retrieved from

Osmani, M. (2011). Chapter 15 - Construction Waste. In Trevor M. Letcher & Daniel A. Vallero (Eds.), Waste (pp. 207-218). Boston: Academic Press

Radica, F., Iezzi, G., Trotta, O., Bonifazi, G., Serranti, S., & de Brito, J. (2024). Characterization of CDW types by NIR spectroscopy: Towards an automatic selection of recycled aggregates. Journal of Building Engineering, 88, 109005.
DOI 10.1016/j.jobe.2024.109005

Rinnan, Åsmund, Berg, Frans Van Den, & Engelsen, Søren Balling. (2009). Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry, 28(10), 1201-1222.
DOI 10.1016/j.trac.2009.07.007

Sakthibala, R.K., Vasanthi, P., Hariharasudhan, C., & Partheeban, P. (2025). A critical review on recycling and reuse of construction and demolition waste materials. Cleaner Waste Systems, 12, 100375.
DOI 10.1016/j.clwas.2025.100375

Sirimewan, D., Harandi, M., Peiris, H., & Arashpour, M. (2024). Semi-supervised segmentation for construction and demolition waste recognition in-the-wild: Adversarial dual-view networks. Resources, Conservation and Recycling, 202, 107399.
DOI 10.1016/j.resconrec.2023.107399

Su, Y. (2020). Multi-agent evolutionary game in the recycling utilization of construction waste. Science of The Total Environment, 738, 139826.
DOI 10.1016/j.scitotenv.2020.139826

Tchobanoglous, G., Theisen, H., Vigileditors, S. (1993). Integrated solid waste management: engineering principles and management issues: New York McGraw-Hill, Inc

Wan, T., Jiang, Yu, H., Xiaolong, Zheng, Y., & Ma, W. (2026). Intelligent construction and demolition waste detection in complex environments using an improved CLRD-YOLO model with knowledge distillation. Case Studies in Construction Materials, 25, e06230.
DOI 10.1016/j.cscm.2026.e06230

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