Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis

Abstract

Construction and demolition waste accounts for a sizable proportion of global waste and is harmful to the environment. Its management is therefore a key challenge in the construction industry. Many researchers have utilized waste generation data for waste management, and more accurate and efficient waste management plans have recently been prepared using artificial intelligence models. Here, we developed a hybrid model to forecast the demolition-waste-generation rate in redevelopment areas in South Korea by combining principal component analysis (PCA) with decision tree, k-nearest neighbors, and linear regression algorithms. Without PCA, the decision tree model exhibited the highest predictive performance (R2 = 0.872) and the k-nearest neighbors (Chebyshev distance) model exhibited the lowest (R2 = 0.627). The hybrid PCA–k-nearest neighbors (Euclidean uniform) model exhibited significantly better predictive performance (R2 = 0.897) than the non-hybrid k-nearest neighbors (Euclidean uniform) model (R2 = 0.664) and the decision tree model. The mean of the observed values, k-nearest neighbors (Euclidean uniform) and PCA–k-nearest neighbors (Euclidean uniform) models were 987.06 (kg·m−2), 993.54 (kg·m−2) and 991.80 (kg·m−2), respectively. Based on these findings, we propose the k-nearest neighbors (Euclidean uniform) model using PCA as a machine-learning model for demolition-waste-generation rate predictions.

Artificial intelligence for waste management in smart cities: a review

“…Convert category variables into continuous variables 2. Predictive performance has been improved The average information on the waste generation rate of the observed values was 1165.04 kg/square meter, and the predicted value was 1161.52 kg/square meter Cha et al ( 2023 ); Cha et al ( 2022 ); Minoglou and Komilis ( 2018 ) Gradient enhancement regression model A gradient-enhanced regression model was developed to predict weekly waste production Presents waste production trends in New York and collects comprehensive data Johnson et al ( 2017 ); Sunayana et al ( 2021 ) …”

High-Level K-Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis

“…KNN considers the similarity factor between new and available data to classify an object into predefined categories. KNN has been widely used in many fields such as industry [7][8][9], machine engineering [10], health [11][12][13], marketing [14], electrical engineering [15], security [16][17][18], manufacturing [19], energy [20][21][22], aerial [23], environment [24], geology [25,26], maritime [27,28], geographical information systems (GIS) [29], and transportation [30].…”

“…In their method, an optimal number of UAVs was obtained with regard to heading synchronization in drone implementation. In the environment field, a predictive model was developed in [24] for the construction and demolition waste rate forecast by means of KNN and principal component analysis (PCA). In the field of geology, the authors [25] reported that the KNN algorithm was utilized for a three-dimensional envision of the stratigraphic structure of porous media related to sedimentary formations.…”

Exploring Analytical Hierarchy Process for Multicriteria Assessment of Reinforced Concrete Slabs

“…Other applications can be found in [29], which develops a prediction model based on machine learning algorithms combined with PCA for demolition waste; of the algorithms combined with PCA, the k-nearest neighbors proved to be the ones with the best results. The authors of [30] studied the assessment of the fire-induced concrete spalling of columns using the k-nearest neighbors, but PCA was only used to compress the number of features.…”