Data-Driven Optimization of Construction and Demolition Waste Management: Pattern Recognition and Anomaly Detection

Ana Lopes and Carlos Afonso Teixeira ()
Additional contact information
Ana Lopes: Department of Biology and Environmental, School of Life and Environmental Sciences, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
Carlos Afonso Teixeira: Centre for the Research and Technology of Agroenvironmental and Biological Sciences, Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal

Sustainability, 2025, vol. 17, issue 9, 1-21

Abstract: Construction and Demolition Waste (CDW) forecasting is essential for sustainable waste management and circular economy objectives. Traditional prediction models often face limitations when dealing with small datasets and extreme variability. This study introduces a robust statistical framework that employs the median and Median Absolute Deviation (MAD), applied to standardized CDW indicators: tons per day (t day −1 ) and tons per square meter (t m −2 ). The method enables the detection of statistical anomalies using a ±2·MAD threshold, increasing the model’s resilience to outliers and enhancing its predictive reliability. The analysis is based on a dataset of 16 construction and rehabilitation projects, carried out under consistent technical methodologies, operational practices, and centralized data collection protocols. The results show that median-based predictions offer greater stability than mean-based estimators, particularly in skewed datasets. The framework successfully identifies projects with significant deviations, supporting targeted audits, performance monitoring, and iterative model refinement. Although the current model focuses on the duration and area as predictors, future enhancements should incorporate additional contextual variables. Furthermore, embedding the median–MAD logic within machine learning architectures (e.g., LSTM, ARIMAX) could improve scalability and support real-time CDW monitoring. These findings contribute to the development of data-driven forecasting tools that are aligned with operational efficiency and circularity goals in the construction sector.

Keywords: construction and demolition waste (CDW); anomaly detection; predictive modeling; circular economy (CE); waste generation indicators; statistical analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/9/4211/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/9/4211/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:9:p:4211-:d:1650453

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().