Machine learning in construction and demolition waste management: Progress, challenges, and future directions

Abstract

The application of machine learning contributes to intelligent and efficient management of construction and demolition waste, leading to a reduction in waste generation and an increased emphasis on recycling. This research conducts a comprehensive analysis of 98 journals related to the application of machine learning in construction waste management from 2012 to 2023 to identify current hot topics and emerging trends. The results reveal that machine learning is applied in four main areas and 15 subfields, specifically focusing on construction and demolition waste generation, on-site handling, transportation, and disposal. Various models, such as artificial neural networks, deep learning, convolutional neural networks, and support vector machines, demonstrate their effectiveness in different processes of construction and demolition waste management. The findings of this research will aid researchers in gaining a comprehensive understanding of the current state and future directions of machine learning in construction waste management.