| Title |
Revolutionizing construction and demolition waste sorting: Insights from artificial intelligence and robotic applications |
| ID_Doc |
21081 |
| Authors |
Dodampegama, S; Hou, L; Asadi, E; Zhang, GM; Setunge, S |
| Title |
Revolutionizing construction and demolition waste sorting: Insights from artificial intelligence and robotic applications |
| Year |
2024 |
| Published |
|
| DOI |
10.1016/j.resconrec.2023.107375 |
| Abstract |
The growing environmental concerns have emerged the necessity of sustainable waste management of construction and demolition (C&D) wastes. This review explores the advancements in artificial intelligence (AI) and robotics to automate C&D waste sorting. A comprehensive examination of this domain is conducted by structuring the paper around six research questions. Current trends and potential future directions are revealed by performing methodology and data analysis involving bibliometric and scientometric studies. Notably, recent research emphasises circular economy, AI, and robotics, underscoring the importance to enhance AI for precise categorisation. The scarcity of publicly available datasets is a central challenge in the C&D waste domain, that hinders effective AI applications. However, data augmentation, data synthesis, generative AI, and transfer learning have been identified as crucial techniques to enhance dataset quality and categorization accuracy. While AI draws significant attention in the C&D waste domain, this review shows a lack of AI-enabled robotics systems due to the complex nature of waste sorting and collection. In summary, this study's findings highlight the need for new methods and techniques integrating multisensory fusion, unsupervised machine learning and robotics intelligence to continuously learn and adapt to new waste streams and materials, making them highly efficient in sustainable waste management. |
| Author Keywords |
Construction and demolition wastes; Circular economy; Artificial intelligence; Robotic sorting; Deep learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001145045800001 |
| WoS Category |
Engineering, Environmental; Environmental Sciences |
| Research Area |
Engineering; Environmental Sciences & Ecology |
| PDF |
https://doi.org/10.1016/j.resconrec.2023.107375
|