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

Niluka Domingo, Heshani M. Edirisinghe, Ravindu Kahandawa, Gayan Wedawatta

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number1941
Number of pages14
JournalSustainability
Volume16
Issue number5
Early online date27 Feb 2024
DOIs
Publication statusPublished - Mar 2024

Bibliographical note

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/).

Keywords

  • construction waste; waste prediction; construction waste modelling; waste quantification; waste management; generalised liner regression

Fingerprint

Dive into the research topics of 'Generalised Linear Modelling for Construction Waste Estimation in Residential Projects: Case Study in New Zealand'. Together they form a unique fingerprint.

Cite this