Abstract
The rapid increase in global urbanization, along with the growth of the construction industry, highlights the urgent need for effective management of construction and demolition (C&D) waste. Intelligent technologies offer a viable solution to this critical challenge. However, there remains a significant challenge in integrating these technologies into a cohesive framework. This study conducts a quantitative analysis of 214 papers from 2000 to 2023, highlighting the extensive use of artificial intelligence (AI) and building information modeling (BIM), along with geographic information systems (GIS) and big data (BD). A further qualitative analysis of 73 selected papers investigates the use of seven different intelligent technologies in the context of C&D waste management (CDWM). To overcome current limitations in knowledge, future research should concentrate on (1) the comprehensive integration of technology, (2) inclusive studies throughout all lifecycle phases of CDWM, and (3) the continued examination of new technologies, such as blockchain. Based on these insights, this study suggests a strategic framework for the effective implementation of intelligent technologies in CDWM. This framework aims to assist professionals in merging various technologies, undertaking lifecycle-wide research, and narrowing the divide between existing and new technologies. It also lays a solid foundation for future academic work to examine specific intelligent technologies, conduct comparative studies, and refine strategic decisions. Regular updates on technological developments are essential for stakeholders to consistently enhance CDWM standards.
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This research was supported by National Natural Science Foundation of China (NSFC) (Grant Nos. 72371171, 72001148, and 72101093) and Green Tech Fund (GTF) (Grant No. GTF202110158) Funding Scheme from Hong Kong SAR Government.
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Wu, Z., Pei, T., Bao, Z. et al. Utilizing intelligent technologies in construction and demolition waste management: From a systematic review to an implementation framework. Front. Eng. Manag. (2024). https://doi.org/10.1007/s42524-024-0144-4
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DOI: https://doi.org/10.1007/s42524-024-0144-4