Application of Generative AI for Waste Reduction in Construction - A Systematic Review

10 pages•Published: June 2, 2026

Abstract

The construction industry significantly contributes to material waste and greenhouse gas emissions, making sustainability essential. Artificial intelligence (AI), especially Generative AI (GenAI), offers new opportunities to improve efficiency and reduce waste in the Architecture, Engineering, and Construction (AEC) sector. This study conducts a systematic literature review (SLR) of GenAI applications in construction waste management from 2020–2025. Using PRISMA 2020 guidelines, 29 peer-reviewed studies from databases such as Web of Science, Scopus, Engineering Village, and Google Scholar were analysed. Results show GenAI is advancing in design optimization to prevent waste, robotic sorting of demolition debris, and lean construction workflows. Key benefits include enhanced design accuracy, real-time decision support, and material reuse. However, challenges like data scarcity, computational costs, model hallucination, and lack of regulations persist. The study proposes future research on hybrid AI-human collaboration, model validation, and natural language interfaces for efficient waste management.

Keyphrases: artificial intelligence, generative ai, systematic literature review, waste reduction

In: Wesley Collins, Anthony Perrenoud and John Posillico (editors). Proceedings of Associated Schools of Construction 62nd Annual International Conference, vol 7, pages 435-444.

Links:	https://easychair.org/publications/paper/z2q3
	https://doi.org/10.29007/3xwk

BibTeX entry

@inproceedings{ASC2026:Application_Generative_AI_Waste,
  author    = {Sukumar Bachu and Tolulope Sanni},
  title     = {Application of Generative AI for Waste Reduction in Construction - A Systematic Review},
  booktitle = {Proceedings of Associated Schools of Construction 62nd Annual International Conference},
  editor    = {Wesley Collins and Anthony Perrenoud and John Posillico},
  series    = {EPiC Series in Built Environment},
  volume    = {7},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2632-881X},
  url       = {/publications/paper/z2q3},
  doi       = {10.29007/3xwk},
  pages     = {435-444},
  year      = {2026}}

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