Integrating metaheuristic optimization algorithms with random forest to predict waste generation in construction and demolition projects

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dc.authorid0009-0001-2619-1840
dc.contributor.authorAwad, Ruba
dc.contributor.authorBudayan, Cenk
dc.contributor.authorCalik, Idil
dc.contributor.authorGurgun, Asli Pelin
dc.contributor.authorKoc, Kerim
dc.date.accessioned2026-04-04T18:55:27Z
dc.date.available2026-04-04T18:55:27Z
dc.date.issued2026
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractThe construction sector is a significant source of global waste, making accurate and proactive prediction of Construction and Demolition Waste (C&DW) essential for sustainable resource management and circular economy efforts. However, estimating C&DW at the project level remains a major challenge. This paper investigates whether C&DW prediction accuracy can be enhanced by integrating the Random Forest (RF) model with two metaheuristic optimization algorithms: the Archimedes Optimization Algorithm (AOA) and Grey Wolf Optimization (GWO). Based on data from 200 real-world projects in Palestine, the GWO-RF model achieved the highest predictive accuracy using only four input variables: project type, start date, building type, and number of floors. To ensure model transparency, Shapley Additive Explanations (SHAP) analysis confirmed that project type and the number of floors were the most influential parameters. This study thus provides a practical, robust, and highly accurate model to support effective waste management strategies in the construction industry.
dc.identifier.doi10.1016/j.autcon.2025.106732
dc.identifier.doi10.1016/j.autcon.2025.106732
dc.identifier.issn0926-5805
dc.identifier.issn1872-7891
dc.identifier.scopus2-s2.0-105029772224
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.autcon.2025.106732
dc.identifier.urihttps://hdl.handle.net/11411/10426
dc.identifier.volume182
dc.identifier.wosWOS:001648214800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofAutomation in Construction
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectConstruction And Demolition Waste
dc.subjectWaste Generation
dc.subjectArchimedes Optimization Algorithm
dc.subjectGrey Wolf Optimization
dc.subjectRandom Forest
dc.subjectGaza-Palestine
dc.subjectShap Analysis
dc.titleIntegrating metaheuristic optimization algorithms with random forest to predict waste generation in construction and demolition projects
dc.typeArticle

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