Automated Detection for the Reserved Rebars of Bridge Pile Caps Based on Point Cloud Data and BIM

Limei Chen (), Shenghan Li () and Yi Tan ()
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Limei Chen: Shenzhen University
Shenghan Li: Shenzhen University
Yi Tan: Shenzhen University

A chapter in Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate, 2023, pp 1147-1162 from Springer

Abstract: Abstract In the process of prefabricated bridge construction, it is very important to quickly and accurately lift and place prefabricated columns. Column assembly mainly controls the butt joint between the column sleeve and the reserved rebar of the cap. Before assembling, to test the length and distance of rebars reserved for each cap is of necessity and the deviation should be controlled within 5 mm. Whereas the work is generally conducted manually, which is low-efficiency and error-prone. Therefore, this paper presents a method for bridge cap rebar to automatically detect the position, spacing and length of rebar using raw scanned point cloud data. The BIM model of bridge cap rebar is transformed into point cloud, the average length and the diameter and distance of rebar are then automatically calculated. A method combines multi-plane segmentation and pass-through filtering based on the parameter from BIM model is developed to remove members that are not rebar. Furthermore, a minimum 3D bounding box method is used to extract the length and center of the rebar, through which the distance of the rebar can be calculated. Experiments on a prefabricated bridge pile cap are carried out. The comparison results show that the root mean square error of the length and spacing of the steel bar between the method and the manual detection is 2.460 mm and 1.214 mm, respectively. The results show that the proposed method can accurately and effectively estimate the length, position and distance of rebars.

Keywords: Automated detection; Building information model (BIM); Bridge pile caps; Point cloud; Rebar (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-99-3626-7_88

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