期刊論文
| 學年 | 114 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2026-01-21 |
| 作品名稱 | Integration of AR and deep learning-based image classification using CNN for construction project monitoring. |
| 作品名稱（其他語言） | |
| 著者 | Su-Ling Fan; Bao-Yu Lin; Chun-Tin Wu; Hee Yong Youn; Faustyn Recha; Jakub Świerzawski; Marek Malucha |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Scientific Reports 16, p. 2852 |
| 摘要 | With manual update, progress monitoring of a construction project is notably challenging due to its labor-intensive and time-consuming nature. The issue of manual update has been proposed to be alleviated by automatic update, but most existing AI-based progress tracking approaches are based on counting the elements finished. As a result, they cannot accurately reflect actual progress status to the project management. This research aims to develop a system that updates the progress of the construction project by effectively identifying both the construction category and operational stage. It is achieved by systematically integrating the techniques of Augmented Reality (AR) and deep learning–based image classification with convolutional neural networks (CNNs). The proposed scheme enables real-time data capture, facilitating comprehensive progress evaluation across multiple project locations. Its effectiveness is demonstrated using a case study of an interior finishing project for a building in Tamkang University, Taiwan. It reveals that the proposed system accurately identifies the construction category and operational stage based on the materials applied, enabling precise tracking of the progress. The results confirm that the proposed scheme significantly enhances the accuracy in construction progress monitoring, avoiding manual inspection and minimizing discrepancies between the planned and actual progress. |
| 關鍵字 | Artificial intelligence; Augmented reality; Construction progress monitoring; Image classification; Interior finishing; Machine learning |
| 語言 | en |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | SCI TSSCI |
| 產學合作 | 國外 |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | GBR |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129295 ) |