01492nas a2200157   4500000000100000008004100001100001500042700001500057700001700072700001400089700001800103700002600121700001800147245009800165520107100263       2025                            d1 aYashuai Li1 aWubin Wang1 aJichang Zhao1 aYang Yang1 aZhaohui Zhang1 aMirosław Skibniewski1 aJingfeng Yuan00aEstimating demolition waste from residential interior photos: A Large Language Model solution3 a<p>Demolition waste management is a critical challenge during the final stages of construction projects, particularly concerning fine-grained waste at the room level. This paper investigates the predictive potential of visual elements for demolition waste estimation, with an emphasis on interior room design. A framework integrating deep learning models with Large Language Model (LLM) is proposed to automatically classify interior design types and predict demolition waste. By utilizing both residential interior photos and floor plans, the framework can identify design types, segment walls, match rooms, and estimate demolition volumes at the room level. The results show high accuracy in design classification and waste prediction, surpassing traditional methods based on waste generation rates. The framework also provides valuable insights into specific waste materials, enhancing waste management at a detailed level. This research advances micro-level demolition waste prediction, promoting the broader application of LLMs in the construction industry.</p>