Towards automated progress assessment of workpackage components in construction projects using computer vision

Ibrahim, Yahaya Makarfi; Lukins, Timothy C.; Zhang, X.; Trucco, Emanuele; Kaka, Ammar Peter

doi:10.1016/j.aei.2008.07.002

Cited by 110 publications

(49 citation statements)

References 24 publications

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“…Rankohi & Waugh (2014) give an overview and comparison of image-based approaches for the monitoring of construction progress. In [11] a single camera approach is used and images taken are compared over a certain period and rasterized. The change between two timeframes is detected through a spatial-temporal derivative filter.…”

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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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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“…An overview and comparison of image-based approaches for construction progress monitoring is given by (Rankohi and Waugh, 2014). (Ibrahim et al, 2009) introduce an approach with a single, fixed camera. They analyze the changes of consecutive images acquired every 15 minutes.…”

Section: Related Workmentioning

confidence: 99%

Validation of Bim Components by Photogrammetric Point Clouds for Construction Site Monitoring

Tuttas¹,

Braun²,

Borrmann³

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Construction progress monitoring is a primarily manual and time consuming process which is usually based on 2D plans and therefore has a need for an increased automation. In this paper an approach is introduced for comparing a planned state of a building (as-planned) derived from a Building Information Model (BIM) to a photogrammetric point cloud (as-built). In order to accomplish the comparison a triangle-based representation of the building model is used. The approach has two main processing steps. First, visibility checks are performed to determine whether or not elements of the building are potentially built. The remaining parts can be either categorized as free areas, which are definitely not built, or as unknown areas, which are not visible. In the second step it is determined if the potentially built parts can be confirmed by the surrounding points. This process begins by splitting each triangle into small raster cells. For each raster cell a measure is calculated using three criteria: the mean distance of the points, their standard deviation and the deviation from a local plane fit. A triangle is confirmed if a sufficient number of raster cells yield a high rating by the measure. The approach is tested based on a real case inner city scenario. Only triangles showing unambiguous results are labeled with their statuses, because it is intended to use these results to infer additional statements based on dependencies modeled in the BIM. It is shown that the label built is reliable and can be used for further analysis. As a drawback this comes with a high percentage of ambiguously classified elements, for which the acquired data is not sufficient (in terms of coverage and/or accuracy) for validation.

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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%

“…The generation of 3D point clouds from fixed cameras would require a very large number of devices to cover the whole construction site. Instead, they can be used for construction site monitoring using image processing and comparison with a projected as-planned model, as done for example by (Lukins and Trucco, 2007, Ibrahim et al, 2009, Kim et al, 2013a. Table 1.…”

Section: Strategiesmentioning

confidence: 99%

Evaluation of Acquisition Strategies for Image-Based Construction Site Monitoring

Tuttas

Braun

Borrmann

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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Towards automated progress assessment of workpackage components in construction projects using computer vision

Cited by 110 publications

References 24 publications

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

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

Validation of Bim Components by Photogrammetric Point Clouds for Construction Site Monitoring

Evaluation of Acquisition Strategies for Image-Based Construction Site Monitoring

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