Tunnel Reconstruction With Block Level Precision by Combining Data-Driven Segmentation and Model-Driven Assembly

Abstract

Metro subway systems with underground tunnels form the backbone of urban transportations and therefore, accurate monitoring and maintenance of such subway systems are extremely necessary for a hassle-free daily commutation of billions of people. Though 3-D models of tunnels are widely used for the deformation monitoring of such subway tunnels, existing model-based tunnel monitoring systems rely on coarse geometric models and hence fail to capture complete tunnel health information. We present a two-stage algorithm to create high-fidelity geometric models of tunnel lining from Terrestrial Laser Scanning (TLS) point clouds. Tunnel geometry, defined at the detailed block entity level, is constructed through a data-driven block segmentation algorithm and a model-driven assembly technique. In our approach, the 3-D tunnel block segmentation problem has been translated into a bolt and lining joint recognition problem from 2-D images unfolded from the 3-D scans. The segmented 3-D blocks are matched with a set of predefined 3-D templates from a primitive library via a constraint total least squares matching method and the matched 3-D templates are assembled to create the final watertight tunnel model. The proposed tunnel modeling method has been comprehensively evaluated on Changzhou, Nanjing, and Wuhan tunnel data sets in terms of outliers, missing data, point density, topological representation, robustness, and geometric accuracy. The experiments on Nanjing and Changzhou metro tunnels show that the geometric model fitting incurs an error of only 7 mm, which is almost consistent with a mean density of 6 mm of these two data sets. Experimental results validate the advantages and potentials of the proposed tunnel modeling method.