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

Lee, Dongoun; Kim, Seung‐Ho; Kim, Sang Yong

doi:10.3390/su8090870

Cited by 19 publications

(9 citation statements)

References 27 publications

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“…Several other techniques, besides regression, have also been used to develop models. Lee et al (2016) developed a hybrid model to estimate the amount and cost of waste at the preliminary stages of a project. The proposed model used an ANN and ant colony optimisation (ACO), using information on 118 multifamily residential buildings in South Korea.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling waste generated during construction of buildings using regression analysis

et al. 2019

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The building industry is responsible for a large amount of waste, and the measurement and modelling of this waste could be used to develop better waste management plans. Several theoretical models explain the relationships between waste and building characteristics, but local practices may result in different behaviours. This study aimed to measure and analyse the waste generated through construction. It was based on the analysis of 18 building sites located in the region of Porto Alegre, Brazil. Waste was measured at these sites, and the results showed an average waste generation rate of 0.151 m3 m-2. A regression analysis of the collected data presented a satisfactory performance in two models. The first model was developed to explain total waste generation, including the effects of certain attributes, with an R2 = 0.81. The changes in waste generated during construction were estimated. The second model considered time schedules and examined the effect of the construction stage on waste generation, and reached an R2 = 0.91. The model with time indicated an S-shaped relationship. The models presented satisfactory statistical parameters and could be used to produce better waste management plans in the preconstruction stage.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling waste generated during construction of buildings using regression analysis

et al. 2019

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“…Wu et al [34] forecasted the construction waste amount using gene expression programming. Lee et al [35] developed a hybrid model which combines artificial neural networks and ant colony optimization for multifamily residential buildings. Au et al [36] used the amount of C&D waste as one of the key factors to propose a system dynamics (SD) model that can help to predict C&D waste disposal charges, and environmental implications, as well as its financial implications.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Renovation Waste Generation Based on Grey System Theory: A Case Study of Shenzhen

et al. 2019

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With the rapid development of urbanization, more and more people are willing to improve their living conditions, thus substantial attention has been paid to residential renovation in China. As a result, large quantities of renovation waste are generated annually which seriously challenge sustainable urban development. To effectively manage renovation waste, accurate prediction of waste generation rates is a prerequisite. However, in the literature, few attempts have been made for predicting renovation waste as renovation activities vary significantly in different cases. This study offers an approach to estimate the amount of renovation waste based on the vacancy rate and renovation waste generation rates at a city level. The grey system theory was applied to predict the amount of renovation waste in Shenzhen. Results showed that the amount of renovation waste would reach 135,620 tons in 2023. The research findings can provide supportive information to relevant stakeholders for developing a renovation waste management framework.

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“…The CBR method shows a knowledge base that supports the cost prediction at the initial step of a construction project. A hybrid model ANN-ACO and ANN model for determining the amount and cost of construction waste in the early stage of construction were developed by Lee et al (2016) [11] using "ant colony optimization" ACO algorithm to optimize the ANN weights and ANN model In 2018, a proposed model for predicting the construction costs of sports areas was presented by Juszczyk et al (2018) [12]. Hybrid DES-PSO model that includes discrete event simulation (DES) and particle swarm optimization (PSO) algorithms were developed by Hegazy et al (1994) [13] construction through a set of iterations in networks utilized, that significantly reduces efforts in search optimization scenarios.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects

Khalaf

Çağlar

et al. 2020

Civ Eng J

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Cost and duration estimation is essential for the success of construction projects. The importance of decision making in cost and duration estimation for building design processes points to a need for an estimation tool for both designers and project managers. Particle swarm optimization (PSO), as the tools of soft computing techniques, offer significant potential in this field. This study presents the proposal of an approach to the estimation of construction costs and duration of construction projects, which is based on PSO approach. The general applicability of PSO in the formulated problem with cost and duration estimation is examined. A series of 60 projects collected from constructed government projects were utilized to build the proposed models. Eight input parameters, such as volume of bricks, the volume of concrete, footing type, elevators number, total floors area, area of the ground floor, floors number, and security status are used in building the proposed model. The results displayed that the PSO models can be an alternative approach to evaluate the cost and-or duration of construction projects. The developed model provides high prediction accuracy, with a low mean (0.97 and 0.99) and CoV (10.87% and 4.94%) values. A comparison of the models’ results indicated that predicting with PSO was importantly more precise.

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

Cited by 19 publications

References 27 publications

Modelling waste generated during construction of buildings using regression analysis

Modelling waste generated during construction of buildings using regression analysis

Predicting Renovation Waste Generation Based on Grey System Theory: A Case Study of Shenzhen

Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects

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