Efficient and Effective Quality Assessment of As-Is Building Information Models and 3D Laser-Scanned Data

Tang, Pingbo; Anil, Engin Burak; Akinci, Burcu; Huber, Daniel

doi:10.1061/41182(416)60

Cited by 39 publications

(23 citation statements)

References 4 publications

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“…analysis or scheduling functionalities, the required LoD of objects' attributes and relations has to be defined, such as durations, dependencies or precedence information. Some LoD in literature are depicted in Table 5, whose definitions differ in geometric accuracy, quality or completeness of semantic information [71,152]. …”

Section: Accuracy and Capabilitymentioning

confidence: 99%

Building Information Modeling (BIM) for existing buildings — Literature review and future needs

Volk

Stengel

Schultmann

2014

Automation in Construction

1,672

1,064

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While BIM processes are established for new buildings, the majority of existing buildings is not maintained, refurbished or deconstructed with BIM yet. Promising benefits of efficient resource management motivate research to overcome uncertainties of building condition and deficient documentation prevalent in existing buildings. Due to rapid developments in BIM research, involved stakeholders demand a state-of-the-art overview of BIM implementation and research in existing buildings. This paper presents a review of over recent 180 publications on the topic. Results show scare BIM implementation in existing buildings yet, due to challenges of (1) high modeling/conversion effort from captured building data into semantic BIM objects, (2) updating of information in BIM and (3) handling of uncertain data, objects and relations in BIM occurring in existing buildings. Despite fast developments and spreading standards, challenging research opportunities arise from process automation and BIM adaption to existing buildings' requirements.

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Section: Accuracy and Capabilitymentioning

confidence: 99%

Building Information Modeling (BIM) for existing buildings — Literature review and future needs

Volk

Stengel

Schultmann

2014

Automation in Construction

1,672

1,064

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“…Recent work has shown that object-based recognition techniques based on Scan-vs-BIM frameworks indeed enable the recognition of 3D BIM objects in TLS data for supporting progress and dimensional quality control (Bosché, 2008;Bosché, 2009;Kim, 2013;Tang, 2011;Turkan, 2011;Turkan, 2013). Similar approaches are also investigated using 3D point clouds generated through photogrammetry instead of TLS (Golparvar-Fard, 2009;Golparvar-Fard, 2013).…”

Section: Scan-to-bim and Scan-vs-bimmentioning

confidence: 99%

“…Three dimensional (3D) Terrestrial Laser Scanning (TLS), also called LADAR (Laser Detection and Ranging), has been considered by many as the best available technology to capture 3D information on a project with accuracy and speed, with a wide range of applications in the AEC/FM industry (Stone, 2001;Tang, 2011;Jacob, 2008). Laser scanning has been proven to be valuable for construction managers to help them for many tasks such as progress monitoring, quality control and facility/infrastructure management (Biddiscombe, 2005;Bosché, 2008;Lee, 2012;Lijing, 2008;Park, 2007;Qui, 2008;Valero, 2013;Xiong, 2013;Yen, 2008).…”

Section: Progress Tracking In the Aec Industrymentioning

confidence: 99%

“…Investigated technologies mainly include Radio Frequency Identification (RFID) (Grau, 2009;Ergen, 2007;Li, 2011;Pradhan, 2009;Razavi, 2010;Razavi, 2012), Ultra-Wide Band (UWB) (Teizer, 2008;Cheng, 2011;Shahi, 2012;Saidi, 2011), Global Positioning System (GPS) (Grau, 2009), Photogrammetry (Golparvar-Fard, 2009Golparvar-Fard, 2013), and Three-dimensional Terrestrial Laser Scanning (TLS) (Bosché and Haas, 2008;Kim, 2013;Stone, 2001;Tang, 2010;Tang, 2011).…”

Section: Progress Tracking In the Aec Industrymentioning

confidence: 99%

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Tracking MEP Installation Works

Bosché¹,

Türkan²,

Haas³

et al. 2013

Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013): Building Th

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Previous research has shown that "Scan-vs-BIM" systems are powerful to provide valuable information for tracking structural works (progress, quality, safety). However, the transferability of this capability to other construction areas such as MEP works has not been assessed so far. Comparatively, the construction of MEP systems, in particular pipes and ducts, tends to be more flexible with respect to the positioning of individual components, so that Scan-vs-BIM systems could be defeated when tracking MEP installation works. This paper presents recent results on the feasibility and performance of using a Scan-vs-BIM system to track MEP works. The approach followed is presented and then tested with two real-life challenging case studies were conducted simultaneously but totally independently in Canada and Italy. The results show that, as expected, pipes and ducts tend to be more loosely positioned than structural elements leading to a poorer performance of the Scan-vs-BIM system. Nonetheless, it appears that the system works well to assess the level of conformance of site installation works, providing valuable information for estimating emerging performance metrics like "percent built as-designed". In addition, the proposed system could also be useful to accelerate and thus reduce the cost of delivering as-built BIM models for in the case of new builds.

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“…These 3D imaging sensors capture existing structural and terrestrial conditions accurately, objectively, and with greater continuity than any manual metrology methods. Despite these benefits, the potential of 3D imaging for applications like automated progress tracking [1] and automated dimensional compliance control [2][3][4][5][6][7][8][9] remains limited because isolating the object of interest from the collected data remains a manual process. Manually extracting information from the raw 3D images and running analysis is painstaking, requires many man-hours and specialized personnel training, and therefore discourages adoption by industry.…”

Section: Introductionmentioning

confidence: 99%

Automated Removal of Planar Clutter from 3D Point Clouds for Improving Industrial Object Recognition

Czerniawski

Nahangi

Walbridge

et al. 2016

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

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The industrial construction industry makes use of prefabrication, preassembly, modularization and offsite fabrication (PPMOF) for project execution because they offer a superior level of control as compared to on-site operations. This control is enabled by systematic and thorough performance feedback loops. Improvement of the feedback systems within these facilities will require a transition away from suboptimal manual data collection to more reliable automated data collection and processing. Laser scanners are an effective tool for automatically gathering dimensional data but extraction of useful information from point clouds remains a challenge. The speed of 3D object recognition methods depends on the size of the search space. Methods for reducing this search space are needed in order to improve the performance of 3D object recognition and subsequent information extraction. Large planar objects (e.g. floors and walls) constitute a large portion of the search space in fabrication facilities, yet are rarely the objects of interest for analysis. In this paper, an automated framework for detecting and removing large planes in point clouds is presented to speed up object recognition. The raw point cloud is first Guassian mapped to normal vector space by calculating normal vectors at each point. The Gaussian sphere is clustered using a density-based clustering algorithm and major parallel planes are segmented from the rest of the point cloud. The major planes are removed and the remaining objects in the scene continue on to 3D object recognition. Results show the algorithm for automatic plane removal can reduce the search space for object recognition by as much as 60% or 70%.

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Efficient and Effective Quality Assessment of As-Is Building Information Models and 3D Laser-Scanned Data

Cited by 39 publications

References 4 publications

Building Information Modeling (BIM) for existing buildings — Literature review and future needs

Building Information Modeling (BIM) for existing buildings — Literature review and future needs

Tracking MEP Installation Works

Automated Removal of Planar Clutter from 3D Point Clouds for Improving Industrial Object Recognition

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