End-to-end point cloud-based segmentation of building members for automating dimensional quality control

Mirzaei, Kaveh; Arashpour, Mehrdad; Asadi, Ehsan; Masoumi, Hossein; Mahdiyar, Amir; González, Vicente A.

doi:10.1016/j.aei.2023.101878

Cited by 19 publications

(8 citation statements)

References 80 publications

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“…Additionally, even after the completion of construction, these technologies can be utilized to monitor the displacement of various structural elements, evaluate the safety and stability of the structure, and facilitate ongoing maintenance of the building (Yin et al, 2023). H. S. Park et al (2007) introduced terrestrial laser scanning (TLS) for structural deformation measurements, and this technology continues to be actively researched by scholars in the construction field (Ge et al, 2023;Mirzaei et al, 2023;S. W. Park et al, 2017;Zhou et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The quality of construction components that make up buildings and infrastructure plays a crucial role in determining the success or failure of construction projects. In particular, the dimensional quality of construction components is critical (Mirzaei et al., 2023), and therefore ensuring its control from the offsite production stage is recommended (Rausch et al., 2020; Q. Wang et al., 2017). However, measuring construction component dimensions is labor‐intensive and time‐consuming, resulting in high costs and the potential for human errors (Li et al., 2018; Mirzazade et al., 2023; Pan & Zhang, 2022; Q. Wang et al., 2017; Yeum & Dyke, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In Mirzaei et al. (2023), the authors developed component dimension measurements using TLS and achieved a measurement accuracy of ± 19 mm or less. Considering that the research results were derived for surrounding components within a building measuring 12.3 m (width) × 23.6 m (length) × 2.5 m (height), the measurement accuracy could be estimated for a measurement distance of approximately 27 m. Q. Wang et al.…”

Section: Introductionmentioning

confidence: 99%

“…However, when applying the measured 3D point cloud data to construction component dimension measurement problems, post-processing is required, which reduces the actual measurement accuracy. In Mirzaei et al (2023), the authors developed component dimension measurements using TLS and achieved a measurement accuracy of ± 19 mm or less. Considering that the research results were derived for surrounding components within a building measuring 12.3 m (width) × 23.6 m (length) × 2.5 m (height), the measurement accuracy could be estimated for a measurement distance of approximately 27 m. Q.…”

Section: Introductionmentioning

confidence: 99%

“…S. Park et al. (2007) introduced terrestrial laser scanning (TLS) for structural deformation measurements, and this technology continues to be actively researched by scholars in the construction field (Ge et al., 2023; Mirzaei et al., 2023; S. W. Park et al., 2017; Q. Wang et al., 2017; Zhou et al., 2022). TLS is a device that employs light detection and ranging technology to measure the three‐dimensional (3D) point cloud data of target objects.…”

Section: Introductionmentioning

confidence: 99%

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Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

Park

2023

Computer aided Civil Eng

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This study presents a novel, deep‐learning‐based model for the automated reconstruction of a cross‐sectional drawing from stereo photographs. Targeted cross‐sections captured in stereo photographs are detected and translated into sectional drawings using faster region‐based convolutional neural networks and Pix2Pix generative adversarial network. To address the challenge of perspective correction in the photographs, a novel camera pose optimization method is introduced and employed. This method eliminates the need for camera calibration and image matching, thereby offering greater flexibility in camera positioning and facilitating the use of telephoto lenses while avoiding image‐matching errors. Moreover, synthetic image datasets are used for training to facilitate the practical implementation of the proposed model in construction industry applications, considering the limited availability of open datasets in this field. The applicability of the proposed model was evaluated through experiments conducted on the cross‐sections of curtain wall components. The results demonstrated superior measurement accuracy, compared with those of current methods of laser scanning or camera‐based measurements for construction components.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

Park

2023

Computer aided Civil Eng

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Post‐earthquake structural damage identification and safety evaluation using point clouds

Yu,

Li,

Liu

et al. 2024

Structural Concrete

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Efficient and accurate post‐earthquake damage assessment of building structures is critical for ensuring the human safety and structural integrity of affected buildings. However, manual inspections and traditional visual damage identification methods are often constrained by the inaccessibility of hard‐to‐reach regions and the subjectivity of human inspectors. To overcome these issues, this paper proposes a novel approach for rapid and precise post‐earthquake damage identification and evaluation using unmanned aerial vehicle (UAV) and point cloud techniques, significantly reducing the time, labor, and errors associated with traditional methods. A comprehensive testing on full‐scale reinforced concrete shear walls was conducted to validate the precision and feasibility of this method. The point cloud models of the shear wall were generated leveraging UAV imagery and laser scanning technology with millimeter‐scale accuracy. The proposed algorithm effectively segmented each target plane of the shear wall, achieving a relatively satisfactory overall Intersection over Union of 99.25%. The relative errors of deformation between the algorithm's identification and gauges measurements were within 5%. This study successfully segmented and quantified structural surface damage, including cracks and spalling. Finally, the structural safety of the shear wall was evaluated according to ATC‐20 guidelines, using indicators such as inclination, story drift ratio, crack width, and damage area. Furthermore, proposed method was also verified in real cases.

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Autonomous damage segmentation of post-fire reinforced concrete structural components

Liu,

Wang,

Wang

et al. 2024

Advanced Engineering Informatics

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End-to-end point cloud-based segmentation of building members for automating dimensional quality control

Cited by 19 publications

References 80 publications

Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

Post‐earthquake structural damage identification and safety evaluation using point clouds

Autonomous damage segmentation of post-fire reinforced concrete structural components

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