Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets

Gi-Wook Cha, Hyeun Jun Moon, Young-Min Kim, Won-Hwa Hong, Jung-Ha Hwang, Won-Jun Park and Young-Chan Kim
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Gi-Wook Cha: Department of Architectural Engineering, Dankook University, Yongin 16890, Korea
Hyeun Jun Moon: Department of Architectural Engineering, Dankook University, Yongin 16890, Korea
Young-Min Kim: Department of Applied Statistics, Dankook University, Yongin 16890, Korea
Won-Hwa Hong: School of Architecture, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Korea
Jung-Ha Hwang: School of Architecture, Kyungpook National University, Daegu 41566, Korea
Won-Jun Park: Department of Architectural Engineering, Kangwon National University, Gangwon-do 25913, Korea
Young-Chan Kim: Department of Fire and Disaster Prevention Engineering, Changshin University, Gyeongsangnam-do 51352, Korea

IJERPH, 2020, vol. 17, issue 19, 1-15

Abstract: Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algorithms may not perform as well when applied to categorical data. This article uses machine learning algorithms to predict C&D waste generation from a dataset, as a way to improve the accuracy of waste management in C&D facilities. These datasets include categorical (e.g., region, building structure, building use, wall material, and roofing material), and continuous data (particularly, gloss floor area), and a random forest (RF) algorithm was used. Results indicate that RF is an adequate machine learning algorithm for a small dataset consisting of categorical data, and even with a small dataset, an adequate prediction model can be developed. Despite the small dataset, the predictive performance according to the demolition waste (DW) type was R (Pearson’s correlation coefficient) = 0.691–0.871, R 2 (coefficient of determination) = 0.554–0.800, showing stable prediction performance. High prediction performance was observed using three (for mortar), five (for other DW types), or six (for concrete) input variables. This study is significant because the proposed RF model can predict DW generation using a small amount of data. Additionally, it demonstrates the possibility of applying AI to multi-purpose DW management.

Keywords: demolition waste management; construction waste management; prediction model; random forest; leave-one-out cross-validation; small data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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