Automated Progress Monitoring Based on Photogrammetric Point Clouds and Precedence Relationship Graphs

Braun, Alexander; Tuttas, Sebastian; Borrmann, André; Stilla, Uwe

doi:10.22260/isarc2015/0034

Cited by 35 publications

(40 citation statements)

References 24 publications

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“…For Test Site A results are shown in , Braun et al, 2015a, Braun et al, 2015b, Tuttas et al, 2014a for progress monitoring (as-built asplanned comparison). Scaffold elements are detected in (Xu et al, 2015), also for Test Site A.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Evaluation of Acquisition Strategies for Image-Based Construction Site Monitoring

Tuttas¹,

Braun²,

Borrmann³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission V WG 4 KEY WORDS: photogrammetric point cloud, construction site monitoring, 3D building model, BIM ABSTRACT:Construction site monitoring is an essential task for keeping track of the ongoing construction work and providing up-to-date information for a Building Information Model (BIM). The BIM contains the as-planned states (geometry, schedule, costs, ...) of a construction project. For updating, the as-built state has to be acquired repeatedly and compared to the as-planned state. In the approach presented here, a 3D representation of the as-built state is calculated from photogrammetric images using multi-view stereo reconstruction. On construction sites one has to cope with several difficulties like security aspects, limited accessibility, occlusions or construction activity. Different acquisition strategies and techniques, namely (i) terrestrial acquisition with a hand-held camera, (ii) aerial acquisition using a Unmanned Aerial Vehicle (UAV) and (iii) acquisition using a fixed stereo camera pair at the boom of the crane, are tested on three test sites. They are assessed considering the special needs for the monitoring tasks and limitations on construction sites. The three scenarios are evaluated based on the ability of automation, the required effort for acquisition, the necessary equipment and its maintaining, disturbance of the construction works, and on the accuracy and completeness of the resulting point clouds. Based on the experiences during the test cases the following conclusions can be drawn: Terrestrial acquisition has the lowest requirements on the device setup but lacks on automation and coverage. The crane camera shows the lowest flexibility but the highest grade of automation. The UAV approach can provide the best coverage by combining nadir and oblique views, but can be limited by obstacles and security aspects. The accuracy of the point clouds is evaluated based on plane fitting of selected building parts. The RMS errors of the fitted parts range from 1 to a few cm for the UAV and the hand-held scenario. First results show that the crane camera approach has the potential to reach the same accuracy level.

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Section: Discussion and Outlookmentioning

confidence: 99%

Evaluation of Acquisition Strategies for Image-Based Construction Site Monitoring

Tuttas¹,

Braun²,

Borrmann³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Braun et al proposed a method to facilitate the as-planned versus as-built comparison through image based monitoring. A dense point cloud was reconstructed from the images that is compared to an existing 4D building information model (BIM) [7]. Behnam et al presented a system for automated progress measurement and online progress reporting of repetitive construction tasks in linear infrastructure projects using satellite remote sensing technology [30].…”

Section: Related Workmentioning

confidence: 99%

“…Automation of construction progress monitoring, however, aids to improve the accuracy and reduce the manual effort required. A building information model (BIM) can help to automate this process [7]. BIM is a comprehensive digital representation of a building comprising not only the 3D geometry of all its components but also a semantic description of the component types and their relationships [4,8,9].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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“…They state that the sequence of activity execution and the connectivity between components are vital clues in the detection of the as-built status of an object. Braun et al (Braun et al, 2015a, Braun et al, 2015b) employ a similar detection framework to track the built status of components. They generate a relationship graph from the as-design BIM based on connectivity.…”

Section: Related Workmentioning

confidence: 99%

Percentage of Completion of in-Situ Cast Concrete Walls Using Point Cloud Data and Bim

Bassier

Vincke

Mattheuwsen

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Progress monitoring of construction sites is becoming increasingly popular in the construction industry. Especially with the integration of 4D BIM, the progression and quality of the construction process can be better quantified. A key aspect is the detection of the changes between consecutive epochs of measurements on the site. However, the development of automated procedures is challenging due to noise, occlusions and the associativity between different objects. Additionally, objects are built in stages and thus varying states have to be detected according to the Percentage of Completion.In this work, a framework is presented to derive work progress of construction sites based on point cloud data. More specifically, a methodology is constituted to compute the Percentage of Completion of in-situ cast concrete walls. In the literature study, existing methods are evaluated for their ability to track progress even in highly cluttered environments. In the practical study, we perform an empirical analysis on a set of periodic point clouds to establish the obstacles and feasibility of the methodology. This work leads to a better understanding of the progress monitoring paradigm which is still subject of ongoing research and will serve as the basis for the further development of a set of automated procedures.

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Automated Progress Monitoring Based on Photogrammetric Point Clouds and Precedence Relationship Graphs

Cited by 35 publications

References 24 publications

Evaluation of Acquisition Strategies for Image-Based Construction Site Monitoring

Evaluation of Acquisition Strategies for Image-Based Construction Site Monitoring

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

Percentage of Completion of in-Situ Cast Concrete Walls Using Point Cloud Data and Bim

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