Generalised Linear Modelling for Construction Waste Estimation in Residential Projects: Case Study in New Zealand

Abstract

Construction waste is a global problem, including in New Zealand where it makes up 40–50% of landfill waste. Accurately measuring construction waste is crucial to reduce its impact on New Zealand’s landfills and meet carbon targets. Waste can be effectively managed if predicted correctly from the start of a project. Waste generation depends on factors such as geography, society, technology, and construction methods. This study focuses on developing a model specific to New Zealand to predict waste generation in residential buildings. By analysing data from 213 residential projects, the study identifies the design features that have the greatest influence on construction waste generation. A generalized linear model is constructed to correlate these design features with waste generation. The findings are valuable for construction stakeholders seeking to implement waste reduction strategies based on predicted waste quantities. This research serves as a starting point, and further investigation in this area is necessary.

Publication DOI: https://doi.org/10.3390/su16051941
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
College of Engineering & Physical Sciences
Funding Information: This research was funded by Auckland Council Waste Management and Innovation Fund, grant number WMIF2002-026.
Additional Information: Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: construction waste; waste prediction; construction waste modelling; waste quantification; waste management; generalised liner regression
Publication ISSN: 2071-1050
Last Modified: 09 Dec 2024 09:11
Date Deposited: 11 Mar 2024 18:19
Full Text Link:
Related URLs: https://www.mdp ... -1050/16/5/1941 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-03
Published Online Date: 2024-02-27
Accepted Date: 2024-02-21
Authors: Domingo, Niluka
Edirisinghe, Heshani M.
Kahandawa, Ravindu
Wedawatta, Gayan (ORCID Profile 0000-0002-8600-5077)

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