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The visual analysis of surface cracks plays an essential role in tunnel maintenance when assessing the condition of a tunnel. To identify patterns of cracks, which endanger the structural integrity of its concrete surface, analysts need an integrated solution for visual analysis of geometric and multivariate data to decide if issuing a repair project is necessary. The primary contribution of this work is a design study, supporting tunnel crack analysis by tightly integrating geometric and attribute views to allow users a holistic visual analysis of geometric representations and multivariate attributes. Our secondary contribution is Visual Analytics and Rendering, a methodological approach which addresses challenges and recurring design questions in integrated systems. We evaluated the tunnel crack analysis solution in informal feedback sessions with experts from tunnel maintenance and surveying. We substantiated the derived methodology by providing guidelines and linking it to examples from the literature.




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The detection and documentation of cracks in the concrete surface of a tunnel are essential for assessing its condition. These cracks comprise a 3D polyline and several multivariate attribute values, such as length, width, orientation, and moisture. Tasks of analysts are, for instance, to identify patterns which endanger the structural integrity of the tunnel surface or assess the density of cracks along the tunnel and identify critical sections. Accomplishing such tasks and evaluating if a repair project is necessary typically requires the visual analysis of detailed geometric data and multivariate attributes simultaneously.


The historical workflow in tunnel maintenance involves an analyst inspecting the tunnel surface on-site. Meanwhile inspections are mostly performed virtually on detailed, digitally reconstructed 3D models of the tunnel surface. Tunnel cracks are traced in high-resolution images by semi-automatic crack-detection algorithms. However, the analysis of multivariate data is still mostly performed via spreadsheets and static plots. Since geometric and attribute data are evaluated separately, no integrated workflow is supported resulting in tedious work to relate both aspects of the data.


Tory and Möller [33] distinguish between data types where the spatialization is given and where it is chosen. To visualize each facet of our data effectively, we decided to visualize the geometric representation in a 3D real-time rendering view, which we refer to as the geometric view. The attributes of each crack are visualized in views with chosen spatialization, such as scatter plots, parallel coordinates, and histograms, which we denote as attribute views. Coordinating these views by linking & brushing already allows analysts to utilize interactive visual analysis and discover phenomena that may not be apparent in a single view visualization [17].


a 2D scatter plot showing orientation vs. length: linked selection of cracks, which are oriented along the tunnel direction, i.e., \(0^\circ \), and which are longer than 8 m. b The scene rendered from the current camera position showing only some of the selected cracks (red). c Some are partially outside, others are completely outside the view frustum. d Cracks occluded by the first tunnel wall are not visible from the current camera position.


However, the coordination of geometric and attribute views has numerous recurring challenges. Figure 2a shows an approach for enabling analysts to brush certain cracks in a scatter plot. To judge the spatial distribution, they need to identify the corresponding geometric representations in the geometric view. Some of these cracks may fully or partially lie outside (Fig. 2c) the view frustum of the geometric view (Fig. 2b), or they may be fully or partially occluded (Fig. 2d) by other geometric objects in the scene.


Multiple cracks The aggregation plot provides experts with exact distributions of attribute values with respect to tunnel sections. However, many tasks require analysts to judge the spatial distribution of attribute values more accurately and to gain immediate access to the corresponding spatial representations. Therefore, we employ camera transitions to allow users to intuitively investigate multiple cracks from overview and detail viewpoints.


In some scenarios, analysts want to compare entities with respect to a typical pattern of attribute values. For instance, if there is a dominant pattern of long, dry cracks, oriented along the tunnel direction, analysts are interested in cracks that deviate from this. Therefore, we provide a similarity-based analysis, which allows users to specify a point of interest in their data, i.e., the focal point. We quantify similarity by a distance metric and treat the resulting distance as another attribute value for each crack. Color mapping enables the identification of tunnel cracks that are similar to or deviate from the specified focal point, which serves G4.


Staff Sgt. Joshua Paserba, 86th Maintenance Squadron nondestructive inspection technician, performs a step in the penetrant inspection process, July 31, 2013, Ramstein Air Base, Germany. This part in the process uses an emulsifying bath, which removes traces of excess penetrant, allowing crack indications to be more readily visible. (U.S. Air Force photo/Senior Airman Chris Willis)


Staff Sgt. Bernadett Kelley, 86th Maintenance Squadron nondestructive inspection technician, views a part underneath black light during the inspection step of the penetrant process, July 31, 2013, Ramstein Air Base, Germany. Any penetrant that has seeped into cracks is now visible due to the penetrants natural fluorescence under black light. (U.S. Air Force photo/Senior Airman Chris Willis) 350c69d7ab


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