Geometrically accurate structural analysis models in BIM-centered software

Hasan, Ahmed M. M.; Torky, Ahmed A.; Rashed, Youssef F.

doi:10.1016/j.autcon.2019.04.022

Cited by 23 publications

(15 citation statements)

References 8 publications

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“…BIM models richly describe buildings through classes of objects that contain their 3D geometry and other characteristics. Quantity can be measured automatically from BIM models by extracting geometric data and semantic properties of each building element [11][12][13][14][15].…”

Section: Manualmentioning

confidence: 99%

Cost Comparison of a Building Project by Manual and BIM

et al. 2020

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This article focuses on comparison between Manual/Traditional and Building Information Modelling (BIM) software based approaches for cost comparison. Centre line method for manual and Revit software for BIM based approaches are used in this research. The principal objectives of this research are to calculate quantities by Manual Centre line method, then to make the cost estimation fast, accurate, efficient, and errorless by using BIM software Rivet, and finally the comparison study of manual based and BIM / Software based estimation. For manual approach, quantities are calculated by multiplying the measurements of length, breadth, and height. Then to get the total quantities the deduction is subtracted from the quantities and final BOQ is prepared for which estimated cost of building is calculated. For BIM based approach, 3D model from 2D floor plan of building is prepared in Revit software, then to estimate the cost of building structure elements, sheets of quantities are generated in the schedule option of the view tab in the software. The Percentage difference between Manual and BIM / Revit Software estimation in brick work, RCC Slab, Plaster Work, PCC for Flooring, Floor Tile Work, Skirting, Paint Work, False Ceiling, Doors and Aluminum Work is 4.57, 2.61, 7.58, 3.27, 1.87, 6.73, 8.03, 1.87, and 0.00% respectively. The total cost difference between manual and BIM based estimation approach comes out to be 4.8%. It is thus concluded that the BIM-assisted estimates have better performance over traditional/manual estimating methods.

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Section: Manualmentioning

confidence: 99%

Cost Comparison of a Building Project by Manual and BIM

et al. 2020

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“…next to the common analysis tools based on Finite Element Method (FEM). Most FEM-based structural analysis tools are not integrated into the BIM working environment (Hasan et al, 2019). Each analysis software has its own way of defining the analysis model, leading to many challenges in implementation of BIM in structural design, such as lack of software interoperability and data exchange problems.…”

Section: Computational Tools For Structural Analysismentioning

confidence: 99%

Framework Proposal for a BIM-Based Digital Platform for Flexible Design and Optimization of Industrial Buildings for Industry 4.0

Reisinger¹,

Kovačić²,

Kaufmann³

et al. 2020

CIB W78 Proceedings

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“…Given the high speed and accuracy of LiDAR, methods for deriving the information of building components (e.g., structural member, non-structural member, and furniture) from the indoor PCD are developed to secure the effectiveness of facility management [6][7][8]. The information includes the location and size of mechanical, electrical, and plumbing [9][10][11], building components [12][13][14], and furniture in 3D space [15][16][17]. In the case of an indoor environment, the 3D scanning method for collecting PCD of the object as many as possible is selected for minimizing the occlusion caused by numerous objects [18,19].…”

Section: Introductionmentioning

confidence: 99%

Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing

Lee

Kim

et al. 2021

Remote Sensing

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With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS.

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Geometrically accurate structural analysis models in BIM-centered software

Cited by 23 publications

References 8 publications

Cost Comparison of a Building Project by Manual and BIM

Cost Comparison of a Building Project by Manual and BIM

Framework Proposal for a BIM-Based Digital Platform for Flexible Design and Optimization of Industrial Buildings for Industry 4.0

Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing

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