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A field based methodology for estimating waste generation rates at various stages of construction projects

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  • Bakshan, Amal
  • Srour, Issam
  • Chehab, Ghassan
  • El-Fadel, Mutasem

Abstract

The growth in construction activities over the past two decades has resulted in a parallel increase in the amount of generated construction waste. This growth, coupled with shortages in landfill space particularly in urban areas, has proven to be a challenging stressor to the environment. Management of construction waste has thus become a problem attracting increasing attention worldwide. In this context, the quantification of waste streams generated from various construction stages is the first step for managing construction waste. In this study, a methodology for quantifying waste streams arising at various construction stages is proposed. The methodology is then tested at a field scale to estimate generation rates for major waste streams and the total construction waste generation rate for the purpose of developing a generalized construction waste management plan that can be applied at a city/regional/country level. The results of the study reveal that the total construction waste generation rate falls within the range of 38–43kg/m2, with masonry and concrete constituting more than 60% of the total waste. The study concludes with a set of recommendations addressing the most important issues contributing to a successful implementation of an integrated construction waste management plan.

Suggested Citation

  • Bakshan, Amal & Srour, Issam & Chehab, Ghassan & El-Fadel, Mutasem, 2015. "A field based methodology for estimating waste generation rates at various stages of construction projects," Resources, Conservation & Recycling, Elsevier, vol. 100(C), pages 70-80.
    • Handle: RePEc:eee:recore:v:100:y:2015:i:c:p:70-80
    DOI: 10.1016/j.resconrec.2015.04.002
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    References listed on IDEAS

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    1. Li, Jingru & Ding, Zhikun & Mi, Xuming & Wang, Jiayuan, 2013. "A model for estimating construction waste generation index for building project in China," Resources, Conservation & Recycling, Elsevier, vol. 74(C), pages 20-26.
    2. Ortiz, Oscar & Pasqualino, Jorgelina C. & Díez, Gloria & Castells, Francesc, 2010. "The environmental impact of the construction phase: An application to composite walls from a life cycle perspective," Resources, Conservation & Recycling, Elsevier, vol. 54(11), pages 832-840.
    3. Begum, Rawshan Ara & Siwar, Chamhuri & Pereira, Joy Jacqueline & Jaafar, Abdul Hamid, 2009. "Attitude and behavioral factors in waste management in the construction industry of Malaysia," Resources, Conservation & Recycling, Elsevier, vol. 53(6), pages 321-328.
    4. Håvard Bergsdal & Rolf André Bohne & Helge Brattebø, 2007. "Projection of Construction and Demolition Waste in Norway," Journal of Industrial Ecology, Yale University, vol. 11(3), pages 27-39, July.
    5. Zhao, W. & Leeftink, R.B. & Rotter, V.S., 2010. "Evaluation of the economic feasibility for the recycling of construction and demolition waste in China—The case of Chongqing," Resources, Conservation & Recycling, Elsevier, vol. 54(6), pages 377-389.
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    Cited by:

    1. Li, Yashuai & Zhang, Xueqing & Ding, Guoyu & Feng, Zhouquan, 2016. "Developing a quantitative construction waste estimation model for building construction projects," Resources, Conservation & Recycling, Elsevier, vol. 106(C), pages 9-20.
    2. Casanovas-Rubio, Maria del Mar & Ramos, Gonzalo, 2017. "Decision-making tool for the assessment and selection of construction processes based on environmental criteria: Application to precast and cast-in-situ alternatives," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 107-117.
    3. Bakshan, Amal & Srour, Issam & Chehab, Ghassan & El-Fadel, Mutasem & Karaziwan, Jalal, 2017. "Behavioral determinants towards enhancing construction waste management: A Bayesian Network analysis," Resources, Conservation & Recycling, Elsevier, vol. 117(PB), pages 274-284.
    4. Lu, Weisheng & Chen, Xi & Peng, Yi & Shen, Liyin, 2015. "Benchmarking construction waste management performance using big data," Resources, Conservation & Recycling, Elsevier, vol. 105(PA), pages 49-58.

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