A Framework for Automated Formwork Quality Inspection using Laser Scanning and Augmented Reality

  • Chi, Hung-lin (Department of Building and Real Estate, Hong Kong Polytechnic University) ;
  • Kim, Min-Koo (Department of Architectural Engineering, Chungbuk National University) ;
  • Thedja, Julian (Department of Building and Real Estate, Hong Kong Polytechnic University)
  • Published : 2020.12.07

Abstract

Reinforcement steel fixing is a skilled and manually intensive construction trade. Current practice for the quality assessment of reinforcement steel fixing is normally performed by fabricators and has high potential in having errors due to the tedious nature of the work. In order to overcome the current inspection limitation, this study presents an approach that provides visual assistance and inspection enhancement for inspectors to assess the dimensional layout of reinforcement steel fixing. To this end, this study aims to establish an end-to-end framework for rebar layout quality inspection using laser scanning and Augmented Reality (AR). The proposed framework is composed of three parts: (1) the laser-scanned rebar data processing; (2) the rebar inspection procedure integrating with AR; and (3) the checking and fixing the rebar layout through AR visualization. In order to investigate the feasibility of the proposed framework, a case study assessing the rebar layout of a lab-scaled formwork containing two rebar layers is conducted. The results of the case studies demonstrate that the proposed approach using laser scanning and AR has the potential to produce an intuitive and accurate quality assessment for the rebar layout.

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Acknowledgement

The second author would like to acknowledge that this research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1A6A3A03010355), the Start-up Fund (project no. 1-ZE94) by The Hong Kong Polytechnic University, and the Chinese National Engineering Research Centre for the Hong Kong Branch of the National Rail Transit Electrification and Automation Engineering Technology Research Center (grant no. 1-BBVJ).