Extraction of pipes and flanges from point clouds for automated verification of pre-fabricated modules in oil and gas refinery projects

Maalek, Reza; Lichti, Derek D.; Walker, R. H.; Bhavnani, Adam; Ruwanpura, Janaka Y.

doi:10.1016/j.autcon.2019.03.013

Cited by 54 publications

(83 citation statements)

References 46 publications

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“…Comprehensive reviews of the recent developments in processing of point clouds acquired from construction sites and indoor environments can be found in [8][9][10][11][12][13][14]. Since the focus of this manuscript is the automated semantic extraction of structural components in regular rectangular concrete construction, the review of previous work is restricted to that addressing the specific problem of automated semantic labeling of objects with predominantly planar and linear facades.…”

Section: State Of the Art In Semantic Extraction Of Structural Componmentioning

confidence: 99%

“…First, synthetic as-planned point clouds are generated by decomposing the planned BIM into points with the same spatial resolution of the point cloud. The as-planned and as-built point clouds are then registered through an iterative closest point (ICP) method, and corresponding points are matched by satisfying some spatial similarity criteria [8]. Once matched, the as-built point cloud is labeled as the element representing the as-planned point cloud.…”

Section: Scan Vs Bimmentioning

confidence: 99%

“…An accurate and reliable as-built 3D/4D building information model (BIM) is not only beneficial during construction, but also during facility operations for maintenance work [6] as well as sustainability and waste management [7].As-built documentation using traditional surveying methods, such as total station and measuring tape, is, however, labor-intensive, costly, and error-prone, particularly when performed frequently. In addition, only a portion of the site elements can be monitored for practicality as traditional instruments can only provide spot measurements [8]. To this end, the application of terrestrial laser scanners (TLS) for 4D as-built BIM documentation during construction is growing markedly, especially with the recent advancements in the speed and quality of data capture as well as the reduction in instrument cost.…”

mentioning

confidence: 99%

“…TLS point clouds overcome the shortcoming associated with traditional single-point measurement instruments. However, due to the large amount of data, manual extraction of different structural components to generate a semantically rich BIM from the acquired point clouds is impractical, subjective, and error-prone [8]. Therefore, reliable and automated processing and semantic object extraction from TLS point clouds is essential to enabling its utilization in the construction industry for frequent and reliable as-built BIM documentation.…”

mentioning

confidence: 99%

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Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

2019

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This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.

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Section: State Of the Art In Semantic Extraction Of Structural Componmentioning

confidence: 99%

Section: Scan Vs Bimmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

2019

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“…Robust PCA is able to detect more outliers compared to RANSAC [52]. The effectiveness of robust PCA for outlier detection is well-established in planar and linear surface estimation [53], as well as in improving the cylindrical axis estimation [54].…”

Section: Robust Pcamentioning

confidence: 99%

Concrete Preliminary Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds through Systematic Threshold Definition

Hadavandsiri

Lichti

Jahraus

et al. 2019

IJGI

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This paper presents a novel approach for automatic, preliminary detection of damage in concrete structures using ground-based terrestrial laser scanners. The method is based on computation of defect-sensitive features such as the surface curvature, since the surface roughness changes strongly if an area is affected by damage. A robust version of principal component analysis (PCA) classification is proposed to distinguish between structural damage and outliers present in the laser scanning data. Numerical simulations were conducted to develop a systematic point-wise defect classifier that automatically diagnoses the location of superficial damage on the investigated region. The method provides a complete picture of the surface health of concrete structures. It has been tested on two real datasets: a concrete heritage aqueduct in Brooks, Alberta, Canada; and a civil pedestrian concrete structure. The experiment results demonstrate the validity and accuracy of the proposed systematic framework for detecting and localizing areas of damage as small as 1 cm or less.

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Modelling As-Built MEP Facilities in a BIM Environment

García

Diaz-Severiano

Jáuregui

et al. 2020

Lecture Notes in Mechanical Engineering

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Extraction of pipes and flanges from point clouds for automated verification of pre-fabricated modules in oil and gas refinery projects

Cited by 54 publications

References 46 publications

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

Concrete Preliminary Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds through Systematic Threshold Definition

Modelling As-Built MEP Facilities in a BIM Environment

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