Automated construction progress measurement using a 4D building information model and 3D data

Kim, Changmin; Son, Hyojoo

doi:10.1016/j.autcon.2012.11.041

Cited by 266 publications

(145 citation statements)

References 22 publications

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“…The asbuilt information is provided by laser scanning point clouds (Bosché, 2010, Turkan et al, 2012, Kim et al, 2013b, Maalek et al, 2015, images of fixed cameras (Lukins and Trucco, 2007, Ibrahim et al, 2009, Kim et al, 2013a and photogrammetric point clouds (Golparvar-Fard et al, 2012, Golparvar-Fard et al, 2011. There are several additional works related to the approach of (GolparvarFard et al, 2012) (e.g., (Karsch et al, 2014, Ham et al, 2016), but to the knowledge of the authors this is the only group working on construction site monitoring using photogrammetric point clouds.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…Attempts at incorporating time as additional information to evaluate and analyse different kinds of project sequences comes in the form of 4D Computer Aided Design (CAD) [12]. These earlier researches are the fundamentals of 4D BIM towards construction progress measurement [13]. Time is logically prioritized over cost as 4D in BIM, due to the fact that the cost may fluctuate over time.…”

Section: Time As 4d In Building Information Modellingmentioning

confidence: 99%

5D Building Information Modelling – A Practicability Review

2016

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Abstract. Quality, time and cost are the three most important elements in any construction project. Building information that comes timely and accurately in multiple dimensions will facilitate a refined decision making process which can improve the construction quality, time and cost. 5 dimensional Building Information Modelling or 5D BIM is an emerging trend in the construction industry that integrates all the major information starting from the initial design to the final construction stage. After that, the integrated information is arranged and communicated through Virtual Design and Construction (VDC). This research is to gauge the practicability of 5D BIM with an action research type pilot study by the means of hands-on modelling of a conceptual bungalow design based on one of the most popular BIM tools. A bungalow is selected as a study subject to simulate the major stages of 5D BIM digital workflow. The whole process starts with developing drawings (2D) into digital model (3D), and is followed by the incorporation of time (4D) and cost (5D). Observations are focused on the major factors that will affect the practicability of 5D BIM, including the modelling effort, inter-operability, information output and limitations. This research concludes that 5D BIM certainly has high level practicability which further differentiates BIM from Computer Aided Design (CAD). The integration of information not only enhanced the efficiency and accuracy of process in all stages, but also enabled decision makers to have a sophisticated interpretation of information which is almost impossible with the conventional 2D CAD workflow. Although it is possible to incorporate more than 5 dimensions of information, it is foreseeable that excessive information may escalate the complexity unfavourably for BIM implementation. 5D BIM has achieved a significant level of practicability; further research should be conducted to streamline implementation. Once 5D BIM is matured and widely accepted, it is foreseeable that additional BIM dimensions of information will be incorporated into sophisticated digital building model to achieve specific project outcomes.

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“…This approach is not directly bound to the geometry of a BIM and therefore cannot identify additional construction elements on site. In [12] a fixed camera and image processing techniques are used for the detection of new construction elements and the update of the construction schedule. Since many fixed cameras would be necessary to cover a whole construction site, more approaches rely on images from hand-held cameras covering the whole construction site as in our and the approaches mentioned in the following.…”

Section: Monitoringmentioning

confidence: 99%

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

Braun¹,

Tuttas²,

Borrmann³

et al. 2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Construction progress monitoring is an essential but time-consuming work on all construction sites. This research introduces a method to facilitate the asplanned versus as-built comparison through image based monitoring. A dense point cloud is reconstructed from the images that is compared to an existing 4D building information model (BIM). However, due to the numerous obstructions found on a construction site, only a minority of building elements can be detected directly. In this paper, we discuss how the detection results are significantly refined and enriched by using additional spatial and temporal information gained from the 4D BIM. In this regard, a precedence relationship graph is derived which helps to identify occluded elements and enhance the detection algorithm.

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Automated construction progress measurement using a 4D building information model and 3D data

Cited by 266 publications

References 22 publications

Evaluation of Acquisition Strategies for Image-Based Construction Site Monitoring

Evaluation of Acquisition Strategies for Image-Based Construction Site Monitoring

5D Building Information Modelling – A Practicability Review

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

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