Range Image-Aided Edge Line Estimation for Dimensional Inspection of Precast Bridge Slab Using Point Cloud Data

Fangxin Li, Julian Pratama Putra Thedja, Sung-Han Sim, Joon-Oh Seo and Min-Koo Kim ()
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Fangxin Li: Business School, Hohai University, Nanjing 211100, China
Julian Pratama Putra Thedja: School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
Sung-Han Sim: School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
Joon-Oh Seo: Department of Building and Real Estate, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Min-Koo Kim: Department of Architectural Engineering, Chungbuk National University, Cheong-ju 28644, Republic of Korea

Sustainability, 2023, vol. 15, issue 16, 1-21

Abstract: The accurate estimation of edge lines in precast bridge slabs based on laser scanning is crucial for a geometrical quality inspection. Normally, the as-designed model of precast slabs is used to match with laser scan data to estimate the edge lines. However, this approach often leads to an inaccurate quality measurement because the actually produced slab can be dimensionally different from the as-designed model or the inexistence of the as-designed model. In order to overcome this limitation, this study proposes a novel algorithm that generates and utilizes range images generated from scan points to enhance accuracy. The proposed algorithm operates as follows: first, the scan points are transformed into a range of images, and the corner points of these range images are extracted using a Harris corner detector. Next, the dimensions of the precast bridge slab are computed based on the extracted corner points. Consequently, the extracted corner points from the range images serve as an input for edge line estimation, thereby eliminating the matching errors that could arise when aligning collected scan points to an as-designed model. To evaluate the feasibility of the proposed edge estimation algorithm, a series of tests were conducted on both lab-scale specimens and field-scale precast slabs. The results showed promising accuracy levels of 1.22 mm for lab-scale specimens and 3.10 mm for field-scale precast bridge slabs, demonstrating more accurate edge line estimation results compared to traditional methods. These findings highlight the feasibility of employing the proposed image-aided geometrical inspection method, demonstrating the great potential for application in both small-scale and full-scale prefabricated construction elements within the construction industry, particularly during the fabrication stage.

Keywords: edge detection algorithm; precast bridge slab; dimensional inspection; range image; point cloud data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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