Toward Automated Spatial Change Analysis of MEP Components Using 3D Point Clouds and As-Designed BIM Models

Kalasapudi, Vamsi Sai; Türkan, Yelda; Tang, Pingbo

doi:10.1109/3dv.2014.105

Cited by 24 publications

(14 citation statements)

References 10 publications

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“…Malihi et al (2018) identified the flatness of the surface collected by laser scanning, detailed surface can be captured to support the defect detection; Schnabel et al (2007) proposed a method to extract building geometries automatically from unorganized point cloud, which reduced manual work to create as-is BIM. Dimitrov & Golparvar-Fard (2015) solved the problem of complex point cloud segmentation in a single scene, provide a solution for building point cloud segmentation; Wang et al (2015), on the basis of segmentation, further extract the boundary features of the building after the data de-noising and region growing; Zhou et al (2015) and Kalasapudi et al (2014) respectively propose the fitting method after boundary extraction, and the alpha shape algorithm used by Zhou is also applied in this paper in another way. All the researches mentioned above have provided theoretical implementation for feature extraction of building point cloud, and provide references for the process of this article.…”

Section: Point Cloud Contour Extractionmentioning

confidence: 99%

3D Reconstruction and Measurement of Surface Defects in Prefabricated Elements Using Point Clouds

Kang

2020

J. Comput. Civ. Eng.

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Due to higher efficiency and lower cost, prefabricated construction is gradually gaining acceptance within the market. Laser scanning has already been adopted in civil engineering to reconstruct 3D model of structure and monitoring the deformation and so on. This paper seeks to explore a more automated and accurate quality control process focusing on the surface defects in prefabricated elements. Laser scanning is adopted for data collection and the 3D reconstruction of the prefabricated components. Besides, a new point cloud pre-processing, involving the KNN algorithm, reduction of data dimension and data gridding, is developed to improve the efficiency and accuracy of subsequent algorithms. The Delaunay triangle is used to extract the contour of the point cloud, then the contour is fitted to further determine the geometric data. Meanwhile, a comprehensive quality control system of prefabricated components based on relevant specifications is proposed, and the quality of prefabricated components is monitored intuitively by the values of indicators. In order to integrate into the BIM platform and better store the obtained quality information, the production quality information is designed to be extended to the IFC standard. The proposed approach will be applied to analyze the causes of quality problems in the production process and strengthen the quality control. This study designs a more efficient and accurate quality evaluation process, including data collection, data processing, indicator calculation and quality evaluation. Moreover, the results forward can feedback to the cause of the quality issues, and further improve the production quality of prefabricated elements.

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Section: Point Cloud Contour Extractionmentioning

confidence: 99%

3D Reconstruction and Measurement of Surface Defects in Prefabricated Elements Using Point Clouds

Kang

2020

J. Comput. Civ. Eng.

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“…Nahangi and Haas present an approach for automated compliance monitoring of pipe spool fabrication which compares laser-scanned point clouds to as-designed 3D CAD models [16]. The proposed approach, which is also present in many other studies [24][25][26][27][28], consists of preprocessing BIM and 3D sensing, then point cloud registration which aligns the as-designed and as-built point clouds, and lastly condition assessment to evaluate dimensional accuracy of the assemblies.…”

Section: Quantifying Dimensional Qualitymentioning

confidence: 99%

Benchmarking and Improving Dimensional Quality on Modular Construction Projects – A Case Study

Rausch

Edwards

Haas

2020

Int. J. Industrialized Constr.

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Dimensional quality plays a key role in project success for modular construction. While approaches exist for reducing rework associated with dimensional variability in traditional construction (i.e., onsite resolution), more proactive approaches must be employed during offsite production of modules. Unfortunately, the stricter dimensional quality demands in modular construction are not yet completely addressed in existing guidelines or studies. As such, contractors often must resort to use of reactive measures to reduce rework. This paper bridges this gap by demonstrating how to implement continuous benchmarking and improvement of dimensional quality by comparing as-built and nominal 3D geometric data across modular construction projects. A case study is presented for two nearly identical modular construction projects, which are carried out in succession. The first project is used to quantify and benchmark key impacts on overall dimensional quality, while strategic improvements are introduced in the second project to improve quality and reduce rework. The results of this study demonstrate how contractors can achieve adequate dimensional quality and reduce rework on successive modular construction projects.

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“…This research was just an automatic review of temporary structures such as the safety of a stair tower, but did not consider other components to propose an appropriate position and shape for them. JEDT 19,1 Kalasapudi et al (2014) developed a method for systems composed of densely located objects such as MEP (mechanical, electrical and plumbing) components that could reliably associate three-dimensional model and point cloud data, and then detect changes of such objects. They could use relational structures of objects in designed BIM models for combining three-dimensional data and as-designed BIM.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the second phase, a framework to automatically address the mismatches between design and as-built drawings was introduced. Other researchers such as (Lee et al, 2013;Kalasapudi et al, 2014;Zhang et al, 2013;Bosché et al, 2015 andYang andErgan, 2015), have tried to present an automated approach to deal with deviations. However, there are gaps to use new technologies (Zhou et al, 2012).…”

Section: Providing a Model To Address Mismatchesmentioning

confidence: 99%

BIM-based integrated solution for analysis and management of mismatches during construction

Jafari

Sharyatpanahi

Noorzai

2020

JEDT

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Purpose Prompt and effective responses to incompatibilities between as-designed and as-built drawings prevent cost and time overruns and material waste. This paper aims to provide an efficient framework to handle mismatches between these two models with the least negative impact on the whole project. Design/methodology/approach First, 11 most frequent mismatches were identified through questionnaires. Also, the respondents were asked to determine the mismatches’ roots and solutions and the impact of applying solutions on the whole project. Afterward, the process to present the optimum solution to one of these mismatches was modeled. After running the application programming interface developed in Navisworks software, decision-makers access a form, showing mismatches, their causes and solutions, as well as the solutions’ effect. To finalize the optimal solution, a platform was provided on whether to accept the system solution or to propose an alternative. Findings To empirically validate the reliability of the proposed framework, two projects were investigated. Two different approaches to dealing with the same mismatch occurred in these projects were compared in terms of time, cost and material required. The results showed that addressing the mismatches through the proposed framework can efficiently enhance time, cost and material consumption, in comparison with the traditional approach. Originality/value There is currently no building information modeling-based holistic framework for managing mismatches between as-designed and as-built drawings. The results of this research can help contractors to make the best decision, saving project resources, when setting about a mismatch during construction.

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Toward Automated Spatial Change Analysis of MEP Components Using 3D Point Clouds and As-Designed BIM Models

Cited by 24 publications

References 10 publications

3D Reconstruction and Measurement of Surface Defects in Prefabricated Elements Using Point Clouds

3D Reconstruction and Measurement of Surface Defects in Prefabricated Elements Using Point Clouds

Benchmarking and Improving Dimensional Quality on Modular Construction Projects – A Case Study

BIM-based integrated solution for analysis and management of mismatches during construction

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