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.

中文翻译：

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具有神经辐射场的加速桥梁建设中剪切连接器的稳健定位


加速桥梁施工 （ABC） 要求预制构件精确对齐，以防止装配失败。由于结构的稀疏性，传统方法很难从运动结构生成的点云数据 （PCD） 中定位剪切连接器。本文介绍了一种使用神经辐射场生成的 PCD 和三步缩小算法进行剪切连接器定位的稳健方法。PCD 为小型连接器提供了密集的点，使算法能够准确地确定它们的位置。该方法成功识别了模型预制梁中的所有 72 个抗剪连接件，平均误差为 10 mm，展示了其在 ABC 项目中评估可施工性的潜力。未来的研究可能会整合基于深度学习的分割技术，以提高复杂几何形状和非标准桥梁设计的效率和适应性。