Automated progress monitoring of construction projects using Machine learning and image processing approach

Project progress tracking and controlling is one of the most critical responsibilities of construction project management. Current practices for forecasting the effectiveness of a construction project include an eval-uation that is still laborious, long-drawn-out, and can prone to errors. Digitalization has a tremendous potential to enhance management practices in the field of construction. The building sector has been reluctant to admit new technology. This points out the importance of an automated method for tracking the progress of projects. The goal of this research is to implement a monitoring system for Construction Progress Tracking (CPT). Machine learning and image processing techniques are used to ensure CPT auto-matically. In particular, masonry activity in construction is focused, though it can be broadened to other construction work. The proposed deep learning model is trained using a set of data of 356 images taken from several worksites and websites. The proposed system is demonstrated with OpenCV Python lan-guage and an automated progress report is generated in Microsoft excel. The study developed a platform for automated building project progress monitoring. It has also built a systematic strategy for gathering the standardized dataset for benchmarking purposes. The following are some of the study's notable find-ings: A supervised CNN classifier was constructed for brickwork recognition using spatial and color infor-mation. The classifier worked well, with accuracy and recall values of 81% and 83%, respectively. A uniform collection of 356 photos was created. This resulted in the creation of a benchmarked dataset for a masonry wall. The achieved results reveal high precision and speed to effectively detect the con-struction project without the interference of human activities, thereby facilitating improved inspection and supervision.Copyright (c) 2022 Elsevier Ltd. All rights reserved.Selection and peer-review under responsibility of the scientific committee of the International Confer-ence on Advances in Construction Materials and Structures.