Shape recognition with point clouds in rebars

Ishida, Kosei; Kano, Naruo; Kimoto, Kenji

doi:10.4017/gt.2012.11.02.344.00

Cited by 4 publications

(6 citation statements)

References 3 publications

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“…On the other hand, factors of the category of “S” have been ranked as “object (rebar) distance from scanner”, “rebar cage shape”, “occlusion” and “rebar spacing” for RC structures, which have been identified as effective parameters for automated monitoring via scanning technologies. All these parameters have been supported by studies performed by Ishida et al (2012), Hajian and Brandow (2012), Turkan et al (2013), Wang et al (2017), Maalek et al (2019), Kim et al (2020) and Kim et al (2021). The effectiveness and accuracy of scanned data are very much dependent on scan distance (Kim et al , 2016) and scanning object shape (Bueno et al , 2017).…”

Section: Discussionmentioning

confidence: 80%

“…Rebar is the main element of reinforced concrete (RC), and rebar monitoring is a technical, meticulous and time-consuming procedure (Ishida et al , 2012). Rebar monitoring includes abnormalities inspection related to rebar spacing, rebar diameter and evaluation of the rebar assembly or cage as per the designed model for dimensional compliance throughout the construction phase.…”

Section: Introductionmentioning

confidence: 99%

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Factors affecting the implementation of automated progress monitoring of rebar using vision-based technologies

Qureshi

Alaloul²,

Wing³

et al. 2022

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Purpose Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders. Design/methodology/approach A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index. Findings The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”. Originality/value The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.

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Section: Discussionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Factors affecting the implementation of automated progress monitoring of rebar using vision-based technologies

Qureshi

Alaloul²,

Wing³

et al. 2022

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“…Studies using 3D scanning in building construction [6,14,15,16,17,18] have been conducted continuously and variously in recent years, and especially the technology to compare the construction progress in conjunction with BIM is in the spotlight. Similarly, this case attempts to apply 3D scanning technology to rebar placing work, which is a process that has a high importance as well as relatively high dependence on manpower among the items that inspect the quality of frame construction, to check whether the rebar placing work was performed according to the architectural structure drawings.…”

Section: Methodsmentioning

confidence: 99%

Case Study of Applying 3D Laser Scanning Technology to Spacing Inspection of Rebar Placing Work

Kim

Jeong

Kim

et al. 2023

KEM

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The technology of the 4th industrial revolution is providing opportunities to improve productivity in various fields including the construction industry. The level of activation of domestic smart construction technology is on the rise, and especially the technology linking 3D scanning and BIM improves the efficiency of management in the construction and maintenance stages. In this study, we are to increase the timeliness of quality inspection by applying 3D scanning technology to frame work with high dependence on manpower during construction work. In this study used case of applying technology. The precision of rebar construction by converting the subject of the study into data that can be analyzed through a three-step 3D scanning process and comparing it with a 3D BIM model based on design drawings was identified by case study. The result of the study showed that it was possible to compare the rebar spacing in the design drawing and the rebar spacing of the actual rebar. When 3D scanning technology was applied based on the research results, it was found that it is a highly efficient method compared to the existing method in the operation of the work type. Therefore, it is thought that it will be possible to contribute to the improvement of the competitiveness of the construction industry by securing examples of applying the 4th industrial revolution technology to various types of work and using it for future research.

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“…lajes, pilares, vigas), eliminando posteriormente os restantes pontos que, por exclusão, são considerados ruído [16]. Esta abordagem é aceite pela maioria dos autores como de fácil implementação e menor exigência em termos computacionais [16,17], quando comparada com o processo inverso (ver [18]). Relativamente à segunda parte, a generalidade da investigação foca-se na geração de regiões através de aprendizagem computacional não supervisionada (treino com dados sem categorização prévia), tipicamente por algoritmos de densidade [16].…”

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