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

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.

[1]  Ann T. W. Yu,et al.  On-site sorting of construction and demolition waste in Hong Kong , 2001 .

[2]  Hongping Yuan.  A dynamic model for assessing the effectiveness of construction and demolition waste management , 2011 .

[3]  Keng Siau,et al.  A review of data mining techniques , 2001, Ind. Manag. Data Syst..

[4]  Kae‐Long Lin,et al.  Feasibility study of using brick made from municipal solid waste incinerator fly ash slag. , 2006, Journal of hazardous materials.

[5]  Hjh Jos Brouwers,et al.  Construction Waste: Quantification and Source Evaluation , 1996 .

[6]  Weisheng Lu,et al.  Exploring critical success factors for waste management in construction projects of China , 2010 .

[7]  K P Soman,et al.  A Simple Approach to Clustering in Excel , 2010 .

[8]  Weisheng Lu,et al.  Investigating waste reduction potential in the upstream processes of offshore prefabrication construction , 2013 .

[9]  Weisheng Lu,et al.  The evolution of construction waste sorting on-site , 2013 .

[10]  Lucio Soibelman,et al.  Material Waste in Building Industry: Main Causes and Prevention , 2002 .

[11]  Weisheng Lu,et al.  Construction waste management policies and their effectiveness in Hong Kong: A longitudinal review , 2013 .

[12]  Weisheng Lu.  Beyond the inert and non-inert dichotomy: towards 'building a zero waste Hong Kong' , 2013 .

[13]  Weisheng Lu,et al.  An empirical investigation of construction and demolition waste generation rates in Shenzhen city, South China. , 2011, Waste management.

[14]  Chi Sun Poon,et al.  Management of construction waste in public housing projects in Hong Kong , 2004 .

[15]  Marija J. Norusis,et al.  IBM SPSS Statistics 19 Guide to Data Analysis , 2011 .

[16]  Pranab Kumar Sen,et al.  Large Sample Methods in Statistics: An Introduction with Applications , 1993 .

[17]  C. Poon.  Reducing construction waste. , 2007, Waste management.

[18]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[19]  Pilar Mercader-Moyano,et al.  Selective classification and quantification model of C&D waste from material resources consumed in residential building construction , 2013, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.