title: The S-curve for forecasting waste generation in construction projects.
abstract: Forecasting construction waste generation is the yardstick of any effort by policy-makers, researchers, practitioners and the like to manage construction and demolition (C&D) waste. This paper develops and tests an S-curve model to indicate accumulative waste generation as a project progresses. Using 37,148 disposal records generated from 138 building projects in Hong Kong in four consecutive years from January 2011 to June 2015, a wide range of potential S-curve models are examined, and as a result, the formula that best fits the historical data set is found. The S-curve model is then further linked to project characteristics using artificial neural networks (ANNs) so that it can be used to forecast waste generation in future construction projects. It was found that, among the S-curve models, cumulative logistic distribution is the best formula to fit the historical data. Meanwhile, contract sum, location, public-private nature, and duration can be used to forecast construction waste generation. The study provides contractors with not only an S-curve model to forecast overall waste generation before a project commences, but also with a detailed baseline to benchmark and manage waste during the course of construction. The major contribution of this paper is to the body of knowledge in the field of construction waste generation forecasting. By examining it with an S-curve model, the study elevates construction waste management to a level equivalent to project cost management where the model has already been readily accepted as a standard tool.
authors.affiliation: Department of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong, Hong Kong. Electronic address: wilsonlu@hku.hk.
authors.forename: Weisheng
authors.initials: W
authors.lastname: Lu
authors.affiliation: School of Public Administration, Zhejiang University of Finance & Economics, Hangzhou, PR China. Electronic address: pengyihz@gmail.com.
authors.forename: Yi
authors.initials: Y
authors.lastname: Peng
authors.affiliation: Department of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong, Hong Kong. Electronic address: chenx90@hku.hk.
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Construction and Project Management, Faculty of Science and Engineering, Queensland University of Technology, Brisbane, Australia. Electronic address: rm.skitmore@qut.edu.au.
authors.forename: Martin
authors.initials: M
authors.lastname: Skitmore
authors.affiliation: Department of Public Policy, City University of Hong Kong, Hong Kong. Electronic address: xiaoling.zhang@cityu.edu.hk.
authors.forename: Xiaoling
authors.initials: X
authors.lastname: Zhang
mesh_terms: D060389:Construction Industry; D005544:Forecasting; D006723:Hong Kong; D007220:Industrial Waste / Q000032:analysis*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D018505:Waste Management / Q000379:methods*
chemical_list: D007220:Industrial Waste
keywords: Construction waste management; Curve fitting; Forecast; S-curve; Waste generation quantification