Automated progress controlling and monitoring using daily site images and building information modelling

Mahami, Hadi, Nasirzadeh, Farnad, Hosseininaveh Ahmadabadian, Ali and Nahavandi, Saeid 2019, Automated progress controlling and monitoring using daily site images and building information modelling, Buildings, vol. 9, no. 3, pp. 1-20, doi: 10.3390/buildings9030070.

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Title Automated progress controlling and monitoring using daily site images and building information modelling
Author(s) Mahami, Hadi
Nasirzadeh, FarnadORCID iD for Nasirzadeh, Farnad
Hosseininaveh Ahmadabadian, Ali
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Journal name Buildings
Volume number 9
Issue number 3
Article ID 70
Start page 1
End page 20
Total pages 20
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2019
ISSN 2075-5309
Keyword(s) Construction progress monitoring
Structure from motion
Multi-view stereo
Point cloud
Summary 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.
Language eng
DOI 10.3390/buildings9030070
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, the authors
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