Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018

Wang, Qian; Kim, Minkoo

doi:10.1016/j.aei.2019.02.007

Cited by 293 publications

(159 citation statements)

References 142 publications

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“…This demonstrates the significance of implementing intelligent asset documentation and structural health monitoring (SHM) approaches for existing built assets. Laser scanning has been widely used to document and monitor existing conditions of real-world assets in the form of point clouds [3,4]. A point cloud is an unstructured low-level digital representation, which by itself does not contain any meaningful information of the documented asset.…”

Section: Introductionmentioning

confidence: 99%

Geometric Accuracy of Digital Twins for Structural Health Monitoring

Lu¹,

Rausch²,

Bolpagni³

et al. 2021

Structural Integrity and Failure

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We present an exploratory analysis of the geometric accuracy of digital twins generated for existing infrastructure using point clouds. The Level of Geometric Accuracy is a vital specification to measure the twinning quality of the resulting twins. However, there is a lack of a clear definition of the Level of Geometric Accuracy for twins generated in the operation and maintenance stage, especially for structural health monitoring purposes. We critically review existing industry applications and twinning methods. To highlight the technical challenges with creating high-fidelity digital replicas, we present a case study of twinning a bridge using real-world point clouds. We do not provide conclusive methods or results but envisage potential twinning strategies to achieve the desired geometry accuracy. This chapter aims to inform the future development of a geometric accuracy-based evaluation system for use in twinning and updating processes. Since a major barrier for a fully automated twinning workflow is the lack of rigorous interpretation of 'geometric accuracy' outside design environments, it is imperative to develop comprehensive standards to guide practitioners and researchers in order to achieve model certainty. As such, this chapter also aims to educate all stakeholders in order to minimise risk when drafting contracts and exchanging digital deliverables.

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Section: Introductionmentioning

confidence: 99%

Geometric Accuracy of Digital Twins for Structural Health Monitoring

Lu¹,

Rausch²,

Bolpagni³

et al. 2021

Structural Integrity and Failure

View full text Add to dashboard Cite

show abstract

“…The point cloud only contains geometric information and does not contain any semantics or specific information regarding structural components, which is primarily processed from spatial data (e.g., laser scans and multiview images) (Q. Wang & Kim, 2019). Point clouds are rarely used directly as 3D models in civil engineering.…”

Section: Introductionmentioning

confidence: 99%

Structure‐aware 3D reconstruction for cable‐stayed bridges: A learning‐based method

Zhao

Huang

et al. 2020

Computer aided Civil Eng

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A powerful deep learning-based three-dimensional (3D) reconstruction method for reconstructing structure-aware semantic 3D models of cable-stayed bridges is proposed herein. Typically, conventional bridge semantic 3D model reconstruction methods are not robust when low-quality point clouds are used. Furthermore, they are suited particularly for their respective fields and less generalized for cable-stayed bridges. Hence, a structure-aware learning-based cable-stayed bridge 3D reconstruction framework is proposed. The encoder part of the network uses both multiview images and a photogrammetric point cloud as input, whereas the decoder part uses a recursive binary tree network to model a high-level structural relation graph and lowlevel 3D geometric shapes. Two actual cable-stayed bridges are employed as examples to evaluate the proposed method. Test results demonstrate that the proposed method successfully reconstructs the bridge model with structural components and their relations. Quantitative results indicate that the predicted models achieved an average 1 score of 99.01%, a Chamfer distance of 0.0259, and a mesh-to-cloud distance of 1.78 m. The achieved result is similar to that obtained using the manual reconstruction approach in terms of component-wise accuracy, and it is considerably better than that obtained using the manual approach in terms of spatial accuracy. In addition, the proposed recursive binary tree network is robust to noise and partial scans. The potential applications of the obtained 3D bridge models are discussed.

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“…However, these algorithms are mainly used for RGB images, and the 3-dimensional point cloud is not suitable for directly applying these algorithms due to extradimensional data. The object detection for 3D point cloud datasets has been a vast, growing research area in recent times [16], [17]. Before the emergence of the deep learning method, 3D point cloud-based object detection already had a relatively mature process: point cloud filtering, point cloud clustering, feature extraction and classification [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Detection for Tunnel Shotcrete Spray Using Deep Learning and LiDAR

et al. 2020

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Shotcrete spray is an indispensable process in tunnel construction. At present, the construction of tunnels in China is mainly depend on labor or mobile concrete sprayer, which has lots problems like timeconsuming, low precision, and labor intensive. An intelligent detection method for tunnel shotcrete spraying is proposed in this article. There are two main issues need to be solved, one is the modeling of tunnel in real-time to monitor the thickness of shotcrete and other is the detection of spraying area in the tunnel. The LiDAR can obtain a 3D model of tunnel after performing necessary preprocess on it in real-time. On the other hand, the spraying areas are usually divided by arches in the tunnel, so we can detect the position of arches to determine the spraying areas. Inspired by the YOLO algorithm, we proposed a novel neural network structure to detect the approximate bounding boxes of the arches and a line-detection algorithm is used to determine the final positions of the spraying area in the image. The size of the weight file of our neural network is only 2.57 MB after the use of some deep compression tricks, which means our model is device friendly. After that, the object detection results in the image will be projected to the point cloud data. The experimental results suggest that our method performed well in the detection for tunnel shotcrete spraying, and the mAP for spraying area detection was found to be 91.4%. INDEX TERMS 3D point cloud, deep learning, object detection, tunnel shotcrete.

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Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018

Cited by 293 publications

References 142 publications

Geometric Accuracy of Digital Twins for Structural Health Monitoring

Geometric Accuracy of Digital Twins for Structural Health Monitoring

Structure‐aware 3D reconstruction for cable‐stayed bridges: A learning‐based method

Intelligent Detection for Tunnel Shotcrete Spray Using Deep Learning and LiDAR

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