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Recent research efforts on improving construction progress monitoring have focused mainly on model-based assessment methods. In these methods, the expected performance typically is modeled with 4D BIM, and the actual performance is sensed through the 3D image-based reconstruction method or laser scanning. Previous research on 4D augmented reality (4D AR) models—which fuse 4D BIM with point clouds generated from daily site photologs—and also laser scan versus BIM, have shown that it is possible to conduct occupancy-based assessments, and as an indicator of progress, detect whether BIM elements are present in the scene. However, to detect deviations beyond typical work breakdown structure (WBS) in 4D BIM, these methods also need to capture operation-level details (e.g., current stage of concrete placement: formwork, rebars, concrete). To overcome current limitations, this paper presents methods for sampling and recognizing construction material from image-based point cloud data and using that information in a statistical form to infer the state of progress. The proposed method is validated using the 4D AR model generated for a building construction site. The preliminary experimental results show that it is feasible to sample and detect construction materials from the images that are registered to a point cloud model and use frequency histograms of the detected materials to infer the actual state of progress for BIM elements.
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Han et al. (2014) studied this question.