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Accurate data collected from on-going construction projects assists project field engineers with tracking the progress of construction work. The comparison of as-planned schedules against the as-built status of construction enables the involved parties to determine project performance. Periodic monitoring of project performance enables timely identification of discrepancies between the schedule baseline and the project schedule. The rapid identification of discrepancies allows necessary measures to be taken to minimize the impact of a delay on the construction workflow. In transportation projects, the traditional means of acquiring as-built data from a construction site prevents involved parties from receiving the required information from the site in a timely manner. The delay in the communication of information ultimately causes subsequent delays in implementing necessary courses of action targeted toward improving the workflow. Therefore, this project develops a technology-supplemented progress monitoring approach. The approach implements as-built data with a high level of detail is obtained from a jobsite within a reasonable period of time using lidar technology while ensuring the safety of the data collector. The proposed framework utilizes point cloud data obtained using mobile lidar technology and 4D design models to identify deviations of the performed work from the planned work. The framework was tested using as-built data acquired from an on-going bridge construction project, and Percentage of Completion (POC) for the as-built bridge elements are calculated and compared with the as-planned POC. The differences between these two POC values for every element, on a particular scan date, are used for assessing the performance of the proposed framework. The obtained POC difference values ranged from −7% to 6% for most elements, which shows that the developed framework enables tracking the completion of individual bridge elements in an accurate and efficient manner. • 3D design models, construction schedule and mobile lidar data were used to report project progress information. • POC for the as-built bridge elements are calculated and compared with the as-planned POC. • Individual bridge elements can be monitored accurately and efficiently using the proposed framework. • The manual processing time for each epoch is approximately 1 h and 15 min for the case study. [ABSTRACT FROM AUTHOR]