In order to solve the problems of information islands, delayed identification of progress deviations, and inefficient resource allocation in the progress management of traditional construction projects, this paper integrates Beidou high-precision positioning, BIM (Building Information Modeling), IoT sensor network and other technologies to build a management system covering the entire chain. This paper deploys Beidou positioning terminals and vibration, temperature and humidity sensors to obtain the trajectory of construction machinery, material transportation status and environmental parameters. At the same time, based on the BIM 5D model, the design drawings and construction plans are automatically associated, and the actual progress data is dynamically updated. The progress prediction model is constructed by using the LSTM (Long Short-Term Memory) algorithm to output the probability of progress deviation in the next 3 days. In addition, the Apriori algorithm is used to identify process conflicts and generate optimization suggestions in advance. This paper also builds a cloud-based collaborative platform that integrates data from multiple parties such as design, construction, and supervision to support lightweight browsing and progress comparison of BIM models. At the same time, a resource scheduling optimization engine is developed to solve multi-objective constraint problems based on a genetic algorithm (GA). Digital construction has shown advantages in construction time, resource utilization efficiency, process conflict warning accuracy, and construction quality. The average construction time of digital construction is shorter than that of traditional construction, the resource idle rate is reduced, the accuracy of process conflict warning is improved, and the construction quality inspection pass rate is as high as 100.0%. This study provides reference and reference for the digital transformation of the construction industry.

Song Jing. Practice of Digital Construction to Improve Construction Project Progress Management. Academic Journal of Engineering and Technology Science(2025), Vol. 8, Issue 2: 36-44. https://doi.org/10.25236/AJETS.2025.080205.

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