Construction Waste Generation Rate (WGR) Revisited: A Big Data Approach

Xi Chen (), Weisheng Lu (), Meng Ye () and Liyin Shen ()
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Xi Chen: University of Hong Kong
Weisheng Lu: University of Hong Kong
Meng Ye: University of Hong Kong
Liyin Shen: Chongqing University

Chapter Chapter 69 in Proceedings of the 19th International Symposium on Advancement of Construction Management and Real Estate, 2015, pp 843-854 from Springer

Abstract: Abstract Benchmarking performance is one of the most efficient ways to improve construction and demolition (C&D) waste management continuously. Waste generation rate (WGR), however it is defined, is often utilized as the key performance indicator (KPI) for this benchmarking purpose. Yet, the WGR cannot be known with any precision, as current studies, for various reasons, can only investigate a relatively small sample of projects hence their results cannot justifiably be generalized to estimate WGRs in other projects. Managers have complained that current WGRs are too divergent to be confidently accepted as KPIs for benchmarking. This research aims at developing a set of convergent KPIs/WGRs, by making use of a large set of data that has become available only recently. By mining the big data of construction waste disposal records accumulated in Hong Kong over 2011, it is found that the WGRis convergent with the increase of data. It thus can be used for benchmarking the performance of C&D waste management. The study provides not only more accurate WGRs in Hong Kong, but also insightful understanding of the usage of WGRs for C&D waste management decision-makers, researchers and the like.

Keywords: Construction and demolition (C&D) waste management; Key performance indicator (KPI); Waste generation rate (WGR); Benchmarking; Big data; Data mining (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-662-46994-1_69

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