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The purpose of this study is to examine how artificial intelligence (AI) can help reduce waste on construction sites. An explorative, mixed-method research design is deployed. Qualitative methods were utilised, including an extensive literature search, 32 interviews, a project visit, and participation in chosen seminars. Additionally, quantitative methods included an analysis of waste quantities in 161 construction projects, selected based on criteria for availability of data, as well as a targeted questionnaire with 21 respondents. Several methods were employed as means of triangulation, to increase the validity and reliability of the data in a complex and rapidly developing field. The study uncovers several possibilities and concludes with 18 proposed measures for waste reduction on a construction site, along with a set of recommendations for practical implementation. The recommended measures include defining appropriate targets for waste production, optimising resources, tracking continuously, reporting and presenting waste quantities, training, conducting inspections, and implementing specific routines for warehousing. The study helps bridge the gap between ambition and practice by highlighting considerations related to the practical implementation of measures for waste management and providing an understanding of which AI-based tools and measures are considered effective for waste reduction in construction projects.
Bang et al. (Sat,) studied this question.