Estimating construction waste generation in residential buildings: A fuzzy set theory approach in the Brazilian Amazon

Abstract The estimate of construction waste generation is the key decision-making information for policy-makers, construction managers, and the like to devise informed waste management strategies. However, estimating construction waste generated from projects is particularly onerous, as numerous factors related to design, site, and construction are largely in a fuzzy nature when the estimating job is conducted. Built upon previous studies, this paper seeks to develop a model that can be used to estimate construction waste generation based on fuzzy set theory. It follows a trilogy of methodology, including model development, sensitivity analysis, and model validation. A set of IF-THEN rules are developed based on two independent variables, built area and number of floors. A sensitive analysis was conducted to evaluate the influence of the independent variables on waste generation. The model is further calibrated and verified through a case study of 23 residential buildings constructed in the Brazilian Amazon. The model obtained an accuracy of 64.29% in the development phase and 66.67% in the validation phase, showing that the results are largely acceptable. By using this model, it is possible for a waste manager to draw up a baseline graph to indicate the volume of construction waste generation as his/her building project as it progresses. The research is also of novelty by using fuzzy set theory to deal with the fuzzy nature of waste generation in construction projects. Further studies are recommended to enhance the accuracy level of the model by engaging more factors and more quality data.

[1]  Amnon Katz,et al.  A novel methodology to estimate the evolution of construction waste in construction sites. , 2011, Waste management.

[2]  R. Y. Huang,et al.  Framework development for state-level appraisal indicators of sustainable construction , 2011 .

[3]  Desmond Eseoghene Ighravwe,et al.  A multi-criteria decision-making framework for selecting a suitable maintenance strategy for public buildings using sustainability criteria , 2019, Journal of Building Engineering.

[4]  Yi Lu,et al.  FM-test: a fuzzy-set-theory-based approach to differential gene expression data analysis , 2006, BMC Bioinform..

[5]  J. G. Carvalho,et al.  Identification method for fuzzy forecasting models of time series , 2017, Appl. Soft Comput..

[6]  Mohamed Al-Hussein,et al.  Risk identification and assessment of modular construction utilizing fuzzy analytic hierarchy process (AHP) and simulation , 2013 .

[7]  Bing Chen,et al.  FSILP: fuzzy-stochastic-interval linear programming for supporting municipal solid waste management. , 2011, Journal of environmental management.

[8]  Guiwen Liu,et al.  Quantifying construction and demolition waste: an analytical review. , 2014, Waste management.

[9]  Yi Peng,et al.  Benchmarking construction waste management performance using big data , 2015 .

[10]  César Porras-Amores,et al.  Estimation of construction and demolition waste volume generation in new residential buildings in Spain , 2012, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[11]  Weisheng Lu,et al.  Identifying factors influencing demolition waste generation in Hong Kong , 2017 .

[12]  Guohe Huang,et al.  Municipal Solid Waste Management Under Uncertainty: A Mixed Interval Parameter Fuzzy-Stochastic Robust Programming Approach , 2007 .

[13]  Hongdi Wang,et al.  Analysis of the construction waste management performance in Hong Kong: the public and private sectors compared using big data , 2016 .

[14]  K.M. Cochran,et al.  Estimating construction and demolition debris generation using a materials flow analysis approach. , 2010, Waste management.

[15]  Takeshi Fujiwara,et al.  Construction and demolition waste generation rates for high-rise buildings in Malaysia , 2016, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[16]  Shabbir H Gheewala,et al.  Estimation of construction waste generation and management in Thailand. , 2009, Waste management.

[17]  M. Karacasu,et al.  Investigation of waste ceramic tile additive in hot mix asphalt using fuzzy logic approach , 2017 .

[18]  André Augusto Azevedo Montenegro Duarte,et al.  Construction delays: a case study in the Brazilian Amazon , 2017 .

[19]  Mostafa Khanzadi,et al.  Optimum risk allocation model for construction contracts: fuzzy TOPSIS approach , 2012 .

[20]  Andrew N. Baldwin,et al.  Designing out waste in high-rise residential buildings : analysis of precasting methods and traditional construction , 2009 .

[21]  J.P.A. Hettiaratchi,et al.  Modeling Construction Waste Generation towards Sustainability , 2010 .

[22]  Xiaoling Zhang,et al.  Computational Building Information Modelling for construction waste management: Moving from rhetoric to reality , 2017 .

[23]  Zezhou Wu,et al.  An off-site snapshot methodology for estimating building construction waste composition - a case study of Hong Kong , 2019, Environmental Impact Assessment Review.

[24]  Timothy Townsend,et al.  Estimation of regional building-related C&D debris generation and composition: case study for Florida, US. , 2007, Waste management.

[25]  Sahar N. Kharrufa Reduction of building waste in Baghdad Iraq , 2007 .

[26]  P. Sen,et al.  Large sample methods in statistics , 1993 .

[27]  Mohammad Karamloo,et al.  Customizing well-known sustainability assessment tools for Iranian residential buildings using Fuzzy Analytic Hierarchy Process , 2018 .

[28]  Luciana Paulo Gomes,et al.  Waste generated in high-rise buildings construction: a quantification model based on statistical multiple regression. , 2015 .

[29]  O Ortiz,et al.  Environmental performance of construction waste: Comparing three scenarios from a case study in Catalonia, Spain. , 2010, Waste management.

[30]  Dan Wang,et al.  Modelling risk allocation decision in construction contracts , 2007 .

[31]  Jiayuan Wang,et al.  Prediction of large-scale demolition waste generation during urban renewal: A hybrid trilogy method. , 2019, Waste management.

[32]  Mohammad Masud Kamal. Khan,et al.  Design and development of advanced fuzzy logic controllers in smart buildings for institutional buildings in subtropical Queensland , 2016 .

[33]  Jiayuan Wang,et al.  A model for estimating construction waste generation index for building project in China , 2013 .

[34]  Tarek Zayed,et al.  Simulation-based construction productivity forecast using Neural-Network-Driven Fuzzy Reasoning , 2016 .

[35]  Xiaoling Zhang,et al.  Estimating and calibrating the amount of building-related construction and demolition waste in urban China , 2017 .

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

[37]  C Llatas,et al.  A model for quantifying construction waste in projects according to the European waste list. , 2011, Waste management.

[38]  Madhav Prasad Nepal,et al.  Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projects , 2017, Adv. Eng. Informatics.

[39]  Weng Tat Chan,et al.  A type-2 fuzzy set model for contractor prequalification , 2017 .

[40]  Weisheng Lu,et al.  A framework for understanding waste management studies in construction. , 2011, Waste management.

[41]  J. Fellner,et al.  A method for determining buildings’ material composition prior to demolition , 2016 .

[42]  Xi Chen,et al.  The S-curve for forecasting waste generation in construction projects. , 2016, Waste management.

[43]  Hui Zhang,et al.  Risk Assessment Methodology for a Deep Foundation Pit Construction Project in Shanghai, China , 2011 .

[44]  Yashuai Li,et al.  Web-based construction waste estimation system for building construction projects , 2013 .