Robust localization of shear connectors in accelerated bridge construction with neural radiance field

Abstract

Accelerated bridge construction (ABC) demands precise alignment of prefabricated members to prevent assembly failure. Conventional methods struggle to localize shear connectors from point cloud data (PCD) generated by structure-from-motion due to its sparsity. This paper introduces a robust method for shear connector localization using PCD generated by a neural radiance field and a three-step narrowing-down algorithm. The PCD exhibits densely populated points for small connectors, allowing the algorithm to pinpoint their locations accurately. The method successfully identified all 72 shear connectors in a mock-up prefabricated girder, with an average error of 10 mm, demonstrating its potential for assessing constructability in ABC projects. Future research may integrate deep learning-based segmentation techniques to enhance efficiency and adaptability in complex geometries and non-standard bridge designs.