A Bridge Information Modeling (BrIM) Framework for Inspection and Maintenance Intervention in Reinforced Concrete Bridges

Mohamed, Ahmed Gouda; Khaled, Ahmed; Abotaleb, Ibrahim S.

doi:10.3390/buildings13112798

Cited by 2 publications

(2 citation statements)

References 26 publications

(36 reference statements)

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“…Moreover, it addresses the challenges previously outlined for establishing the lower boundary of the search space. Equation (10) outlines the parameters for the adaptive subsampling method.…”

Section: Neighborhood Scalementioning

confidence: 99%

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Hierarchical SVM for Semantic Segmentation of 3D Point Clouds for Infrastructure Scenes

Mansour,

Martens,

Blankenbach

2024

Infrastructures

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The incorporation of building information modeling (BIM) has brought about significant advancements in civil engineering, enhancing efficiency and sustainability across project life cycles. The utilization of advanced 3D point cloud technologies such as laser scanning extends the application of BIM, particularly in operations and maintenance, prompting the exploration of automated solutions for labor-intensive point cloud modeling. This paper presents a demonstration of supervised machine learning—specifically, a support vector machine—for the analysis and segmentation of 3D point clouds, which is a pivotal step in 3D modeling. The point cloud semantic segmentation workflow is extensively reviewed to encompass critical elements such as neighborhood selection, feature extraction, and feature selection, leading to the development of an optimized methodology for this process. Diverse strategies are implemented at each phase to enhance the overall workflow and ensure resilient results. The methodology is then evaluated using diverse datasets from infrastructure scenes of bridges and compared with state-of-the-art deep learning models. The findings highlight the effectiveness of supervised machine learning techniques at accurately segmenting 3D point clouds, outperforming deep learning models such as PointNet and PointNet++ with smaller training datasets. Through the implementation of advanced segmentation techniques, there is a partial reduction in the time required for 3D modeling of point clouds, thereby further enhancing the efficiency and effectiveness of the BIM process.

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Section: Neighborhood Scalementioning

confidence: 99%

“…BIM offers a holistic approach to bridge management, providing a digital platform for comprehensive data integration, visualization, and analysis throughout the asset lifecycle. This enhances the ability to make data-informed maintenance decisions, mitigate risks, and allocate resources efficiently [10].…”

Section: Introductionmentioning

confidence: 99%