title: Waste generated in high-rise buildings construction: a quantification model based on statistical multiple regression.
abstract: Reducing construction waste is becoming a key environmental issue in the construction industry. The quantification of waste generation rates in the construction sector is an invaluable management tool in supporting mitigation actions. However, the quantification of waste can be a difficult process because of the specific characteristics and the wide range of materials used in different construction projects. Large variations are observed in the methods used to predict the amount of waste generated because of the range of variables involved in construction processes and the different contexts in which these methods are employed. This paper proposes a statistical model to determine the amount of waste generated in the construction of high-rise buildings by assessing the influence of design process and production system, often mentioned as the major culprits behind the generation of waste in construction. Multiple regression was used to conduct a case study based on multiple sources of data of eighteen residential buildings. The resulting statistical model produced dependent (i.e. amount of waste generated) and independent variables associated with the design and the production system used. The best regression model obtained from the sample data resulted in an adjusted R(2) value of 0.694, which means that it predicts approximately 69% of the factors involved in the generation of waste in similar constructions. Most independent variables showed a low determination coefficient when assessed in isolation, which emphasizes the importance of assessing their joint influence on the response (dependent) variable.
authors.affiliation: Universidade do Vale do Rio dos Sinos, Civil Engineering Post-Graduation Program, Av Unisinos, 950 Bairro Cristo Rei, São Leopoldo 93 022-000, Rio Grande do Sul, Brazil. Electronic address: apkern@unisinos.br.
authors.forename: Andrea
authors.initials: A
authors.lastname: Parisi Kern
authors.affiliation: Universidade do Vale do Rio dos Sinos, Civil Engineering Post-Graduation Program, Av Unisinos, 950 Bairro Cristo Rei, São Leopoldo 93 022-000, Rio Grande do Sul, Brazil. Electronic address: michelefdias@yahoo.com.br.
authors.forename: Michele
authors.initials: M
authors.lastname: Ferreira Dias
authors.affiliation: Universidade do Vale do Rio dos Sinos, Civil Engineering Post-Graduation Program, Av Unisinos, 950 Bairro Cristo Rei, São Leopoldo 93 022-000, Rio Grande do Sul, Brazil. Electronic address: marlovak@unisinos.br.
authors.forename: Marlova
authors.initials: M
authors.lastname: Piva Kulakowski
authors.affiliation: Universidade do Vale do Rio dos Sinos, Civil Engineering Post-Graduation Program, Av Unisinos, 950 Bairro Cristo Rei, São Leopoldo 93 022-000, Rio Grande do Sul, Brazil. Electronic address: lugomes@unsinos.br.
authors.forename: Luciana
authors.initials: L
authors.lastname: Paulo Gomes
mesh_terms: D060389:Construction Industry; D003252:Construction Materials / Q000032:analysis*; D004784:Environmental Monitoring; D006798:Housing; D007220:Industrial Waste / Q000032:analysis*; D008962:Models, Theoretical; D015999:Multivariate Analysis; D012044:Regression Analysis; D018505:Waste Management / Q000379:methods*
chemical_list: D007220:Industrial Waste
keywords: Construction waste; Quantification of construction waste; Reduction of construction waste