Estimation, Classification, and Prediction of Construction and Demolition Waste Using Machine Learning for Sustainable Waste Management: A Critical Review

The management of construction and demolition waste is a critical concern for sustainable urban development and environmental conservation. In this review, the authors provides an overview of the involvement of machine learning techniques like the support vector machine (SVM), artificial neural networks (ANNs), Random Forest (RF), K-nearest neighbor (KNN), deep convolutional neural networks (DCNNs), etc. in the estimation, classification, and prediction of construction and demolition waste, contributing to the advancement of sustainable waste management practices. The authors observed that the DCNN achieved an outstanding accuracy of 94% in the estimation and classification of construction waste. Based on the authors’ observations, the machine learning models are well suited for the prediction or classification of construction waste and are good for sustainable waste management in the future. This paper provides insights into the promising future of machine learning in revolutionizing waste management practices and future research.