<![CDATA[Application of Image Processing Technology for Physical Assessment and Progress Monitoring of Cluster Projects in Bsd City. The construction industry often faces challenges in project monitoring due to subjective assessments, inefficiencies, and risks of data manipulation. This study introduces a vision-based inspection system utilizing image processing and Convolutional Neural Networks (CNN) to automate progress evaluation in the Enchante BSD City housing project, specifically on foundation and column works. Field images were collected and processed through augmentation, pre-processing, and feature extraction, then used to train an EfficientNet model. The model achieved outstanding results with 100% accuracy, precision, recall, and F1-score, validated through both testing datasets and real field experiments. A prototype web-based application was also developed to integrate the AI model into a practical monitoring tool. The findings demonstrate that this approach can provide real-time, objective, and reliable progress evaluation, offering a promising solution to improve efficiency, transparency, and accountability in construction project management.]]>
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