The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components

Bosché, Frédéric; Ahmed, Mahmoud; Türkan, Yelda; Haas, Carl T.; Haas, Ralph

doi:10.1016/j.autcon.2014.05.014

Cited by 421 publications

(202 citation statements)

References 43 publications

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“…1 The laser scanner which has been used in this experiment was the Riegl VZ-400, provided by Apeks company, placed in Gdansk, Poland. According to the producer brochure about the equipment, Riegl VZ-400 is able to collect the data with 3 mm precision and 5 mm accuracy [5]. The one more problem to investigate in post-processing the data is the level of noise, which has to be filtered before the data processing.…”

Section: Mission Planningmentioning

confidence: 99%

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As- built inventory of the office building with the use of terrestrial laser scanning

Przyborski

Tysiąc

2018

E3S Web Conf.

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Abstract. Terrestrial Laser Scanning (TLS) is an efficient tool for building inventories. Based on the redlaser beam technology it is possible to provide the high accuracy data with complete spatial information about a scanned object. In this article, authors present the solution of use a TLS in as-built inventory of the office building. Based on the provided data, it is possible to evaluate the correctness of built details of a building and provide information for further construction works, for example an area needed for Styrofoam installation. The biggest problem in this research is that an error which equals over 1cm could generate costs, which could be a problem to cover by a constructor. Based on a complicated place of the construction works (centre of a city) it was a challenge to maintain the accuracy.

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Section: Mission Planningmentioning

confidence: 99%

“…Main differences between them are accuracy of the provided data and land they could covered in one measurement mission. For single construction, the best is to use the most accurateterrestrial laser scanning [5].…”

Section: Introductionmentioning

confidence: 99%

As- built inventory of the office building with the use of terrestrial laser scanning

Przyborski

Tysiąc

2018

E3S Web Conf.

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“…For verification, the authors use the percentage of simulated points that can be verified by the real laser scan. In [6], [7], [8] this system is extended for progress tracking using schedule information, for estimating the progress in terms of earned value and for detecting secondary objects. In [9] specific component types are detected using a supervised classification method based on Lalonde features derived from the as-built point cloud.…”

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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“…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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The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components

Cited by 421 publications

References 43 publications

As- built inventory of the office building with the use of terrestrial laser scanning

As- built inventory of the office building with the use of terrestrial laser scanning

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

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

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