Integration of AR and deep learning-based image classification using CNN for construction project monitoring

Area of Science:

• Construction Management
• Computer Vision
• Artificial Intelligence

Background:

• Manual construction project progress monitoring is labor-intensive, time-consuming, and prone to inaccuracies.
• Existing AI-based methods often rely on element counting, failing to capture the true project status.
• There is a need for automated systems that provide precise and real-time construction progress evaluation.

Purpose of the Study:

• To develop an automated system for construction project progress monitoring.
• To accurately identify both the construction category and operational stage using integrated technologies.
• To enhance the precision of progress tracking by moving beyond simple element counting.

Main Methods:

• Integration of Augmented Reality (AR) for real-time data capture.
• Application of deep learning-based image classification utilizing Convolutional Neural Networks (CNNs).
• Systematic identification of construction categories and operational stages based on applied materials.

Main Results:

• The proposed system accurately identifies construction categories and operational stages in real-time.
• Effectiveness demonstrated through a case study of an interior finishing project.
• Precise progress tracking achieved by analyzing materials and operational status.

Conclusions:

• The developed system significantly improves the accuracy of construction progress monitoring.
• Automated identification of construction elements and stages minimizes manual inspection.
• The scheme effectively reduces discrepancies between planned and actual project progress.