Abstract. New transport infrastructure construction can stimulate the growth of economy as well as improving the public citizen welfare. However, with the rising number of mega infrastructure projects, the low project performance, such as project delay and cost escalation, are challenging the traditional Architecture, Engineering, and Construction (AEC) industry. Traditional Construction progress monitoring methods rely on manual data collecting and paperwork reporting which can be labor-intensive, time-consuming and error-prone. Therefore, it is necessary for the involved stakeholders to introduce advanced technologies which facilitates assessing the construction performance automatically and ensures the projects to be delivered on time. The application of building information modeling (BIM) provides involved parties an accurate understandable single source of truth that can improve the interoperability of project information. Nevertheless, current ‘Scan-to-BIM’ workflow cannot support the demand for real-time data analysis and status reporting. This paper presents a semi-automatic construction progress monitoring framework that evaluating the project performance of the infrastructure in real-time. It introduces Hausdorff distance which transmits the 3-D geometrical information contained by as-built point cloud to virtual point cloud directly, to avoid the drawbacks of space partitioning algorithms. The Poisson surface reconstruction utilizing volume as criterion to improve the robustness of progress determination. In addition, the application of 2D polygon fitting provides a potentially feasible method to identify the installation of pre-cast components of the bridge construction. The results indicate that the proposed framework can effectively monitor the geometric increment of road bridge construction project.