4.3 Article

Analysis and Integration of Surface and Subsurface Information of Different Bridges

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DOI: 10.1007/s12524-019-01087-2

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Terrestrial laser scanning (TLS); Close-range photogrammetry (CRP); Differential global positioning system (DGPS); Georeferencing; Ground-penetrating radar (GPR); Image classification; Surface and subsurface analysis; Point cloud classification

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Road transportation is one of the major sources of transportation. Bridges are one of the vital engineering structures which have a major impact on the road transportation system. Bridges provide connectivity between two sides of the river banks or untouched paths to ease the travel. The strength of the bridge can get deteriorated due to heavy traffic and aggressive weather conditions. Evaluation of the condition of the bridges traditionally can be more expensive and time-consuming. The other way is by using remote sensing techniques that are nondestructive and advantageous. Terrestrial laser scanning (TLS) and close-range photogrammetry (CRP) are more suitable noninvasive techniques to generate a detailed 3D point cloud model of real objects. Point clouds obtained from TLS and CRP are merged together to produce point cloud dataset (PCD). The PCD can be georeferenced with the help of differential global positioning system points near the structure and total station points on the surface of the structure. The surface analysis for the features like corrosion, vegetation, biological crust, water presence, etc. can be extracted using the PCD and images obtained from digital single-lens reflex camera. Ground-penetrating radar (GPR) can be utilized for generating subsurface 2D and 3D profile scans. Subsurface analysis features like the presence of pier/abutment, water, voids, rebars, crack, asphalt layer, deck layer, etc. can be extracted with the GPR scan profiles. The surface and subsurface information can be visualized together to understand the surface or subsurface features corresponding to each other's location. Accuracy assessments of the classified images and the classified points of the PCD are done in this research, and the accuracies obtained were 79.69% and 92.494%, respectively. The ground-truth validations were done with the help of laser distometer and measuring tape to precisely compare the values.

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