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Sustainability 2016, 8(9), 870; doi:10.3390/su8090870

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

1
Department of Architectural Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu 47011, Busan, Korea
2
School of Architecture, Yeungnam University, 280 Daehak-ro, Gyeongsan-si 38541, Gyeongbuk, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Dimitrios Komilis
Received: 4 July 2016 / Revised: 21 August 2016 / Accepted: 23 August 2016 / Published: 1 September 2016
(This article belongs to the Special Issue Sustainability in Solid Waste Management)
View Full-Text   |   Download PDF [1001 KB, uploaded 1 September 2016]   |  

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. View Full-Text
Keywords: ant colony optimization; artificial neural network; construction waste; multifamily house; multifamily building ant colony optimization; artificial neural network; construction waste; multifamily house; multifamily building
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Lee, D.; Kim, S.; Kim, S. Development of Hybrid Model for Estimating Construction Waste for Multifamily Residential Buildings Using Artificial Neural Networks and Ant Colony Optimization. Sustainability 2016, 8, 870.

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