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Development of Hybrid Model for Estimating Construction Waste for Multifamily Residential Buildings Using Artificial Neural Networks and Ant Colony Optimization

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  • Dongoun Lee

    (Department of Architectural Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu 47011, Busan, Korea)

  • Seungho Kim

    (School of Architecture, Yeungnam University, 280 Daehak-ro, Gyeongsan-si 38541, Gyeongbuk, Korea)

  • Sangyong Kim

    (School of Architecture, Yeungnam University, 280 Daehak-ro, Gyeongsan-si 38541, Gyeongbuk, Korea)

Abstract

Due to the increasing costs of construction waste disposal, an accurate estimation of the amount of construction waste is a key factor in a project’s success. Korea has been burdened by increasing construction waste as a consequence of the growing number of construction projects and a lack of construction waste management (CWM) strategies. One of the problems associated with predicting the amount of waste is that there are no suitable estimation strategies currently available. Therefore, we developed a hybrid estimation model to predict the quantity and cost of waste in the early stage of construction. The proposed approach can be used to address cost overruns and improve CWM in the subsequent stages of construction. The proposed hybrid model uses artificial neural networks (ANNs) and ant colony optimization (ACO). It is expected to provide an accurate waste estimate by applying historical data from multifamily residential buildings.

Suggested Citation

  • Dongoun Lee & Seungho Kim & Sangyong Kim, 2016. "Development of Hybrid Model for Estimating Construction Waste for Multifamily Residential Buildings Using Artificial Neural Networks and Ant Colony Optimization," Sustainability, MDPI, vol. 8(9), pages 1-14, September.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:9:p:870-:d:77217
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    References listed on IDEAS

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    1. C. S. Poon & Ann Yu & L. Jaillon, 2004. "Reducing building waste at construction sites in Hong Kong," Construction Management and Economics, Taylor & Francis Journals, vol. 22(5), pages 461-470.
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    Cited by:

    1. Liang Qiao & Doudou Liu & Xueliang Yuan & Qingsong Wang & Qiao Ma, 2020. "Generation and Prediction of Construction and Demolition Waste Using Exponential Smoothing Method: A Case Study of Shandong Province, China," Sustainability, MDPI, vol. 12(12), pages 1-12, June.
    2. Murat Gunduz & Ayman Fahmi Naser, 2017. "Cost Based Value Stream Mapping as a Sustainable Construction Tool for Underground Pipeline Construction Projects," Sustainability, MDPI, vol. 9(12), pages 1-20, November.
    3. Zhijia You & Chen Wu & Lianqiong Zheng & Lingjun Feng, 2020. "An Informatization Scheme for Construction and Demolition Waste Supervision and Management in China," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    4. Niluka Domingo & Heshani M. Edirisinghe & Ravindu Kahandawa & Gayan Wedawatta, 2024. "Generalised Linear Modelling for Construction Waste Estimation in Residential Projects: Case Study in New Zealand," Sustainability, MDPI, vol. 16(5), pages 1-14, February.
    5. Xiaochuan Shi & Lei Wu & Xiaoliang Meng, 2017. "A New Optimization Model for the Sustainable Development: Quadratic Knapsack Problem with Conflict Graphs," Sustainability, MDPI, vol. 9(2), pages 1-10, February.
    6. Zhikun Ding & Mengjie Shi & Chen Lu & Zezhou Wu & Dan Chong & Wenyan Gong, 2019. "Predicting Renovation Waste Generation Based on Grey System Theory: A Case Study of Shenzhen," Sustainability, MDPI, vol. 11(16), pages 1-13, August.

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