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Open AccessArticle

Automated Progress Controlling and Monitoring Using Daily Site Images and Building Information Modelling

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Department of Construction Project Management, Art University of Tehran, Tehran 1136813518, Iran
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School of Architecture and Built Environment, Deakin University, Geelong 3220, Australia
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Department of Photogrammetry and Remote Sensing, Faculty of Geodesy & Geomatics Engineering, K.N.Toosi University of Technology, Tehran 158754416, Iran
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Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, 75 Pigdons Road, Waurn Ponds 3216, Australia
*
Author to whom correspondence should be addressed.
Buildings 2019, 9(3), 70; https://doi.org/10.3390/buildings9030070
Received: 25 January 2019 / Revised: 15 February 2019 / Accepted: 20 February 2019 / Published: 20 March 2019
This research presents a novel method for automated construction progress monitoring. Using the proposed method, an accurate and complete 3D point cloud is generated for automatic outdoor and indoor progress monitoring throughout the project duration. In this method, Structured-from-Motion (SFM) and Multi-View-Stereo (MVS) algorithms coupled with photogrammetric principles for the coded targets’ detection are exploited to generate as-built 3D point clouds. The coded targets are utilized to automatically resolve the scale and increase the accuracy of the point cloud generated using SFM and MVS methods. Having generated the point cloud, the CAD model is generated from the as-built point cloud and compared with the as-planned model. Finally, the quantity of the performed work is determined in two real case study projects. The proposed method is compared to the Structured-from-Motion (SFM)/Clustering Multi-Views Stereo (CMVS)/Patch-based Multi-View Stereo (PMVS) algorithm, as a common method for generating 3D point cloud models. The proposed photogrammetric Multi-View Stereo method reveals an accuracy of around 99 percent and the generated noises are less compared to the SFM/CMVS/PMVS algorithm. It is observed that the proposed method has extensively improved the accuracy of generated points cloud compared to the SFM/CMVS/PMVS algorithm. It is believed that the proposed method may present a novel and robust tool for automated progress monitoring in construction projects. View Full-Text
Keywords: construction progress monitoring; structure from motion; multi-view stereo; point cloud construction progress monitoring; structure from motion; multi-view stereo; point cloud
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Mahami, H.; Nasirzadeh, F.; Hosseininaveh Ahmadabadian, A.; Nahavandi, S. Automated Progress Controlling and Monitoring Using Daily Site Images and Building Information Modelling. Buildings 2019, 9, 70.

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