Development of 3D Visualisation Algorithms for the Effective Interpretation of Tunnel Subsurface Radar Data

Relevant EPSRC Research Areas:
Architectures and operating systems; Artificial intelligence technologies; Digital signal processing; Graphics and visualisation; Ground Engineering; Human-communication interaction; Image and vision computing; Infrastructure and urban systems; Mathematical physics; Numerical Analysis; Operational Research; Programming languages and compilers; Software Engineering; Structural Engineering

Project Synopsis:
Anticipated growth of global rail network demand is set to further increase pressure on aging tunnel infrastructure. The inherent safety risks, severity of structural compromise and ever reducing allocatable resources for targeted maintenance, has prompted development of TRACKSCAN, an infrastructure inspection hardware solution based on Ground-Penetrating Radar (GPR), able to rapidly produce comprehensive 360-degree point-cloud tunnel structure profiles, referred to here on as 360-GPR data. However, this hardware currently lacks a software counterpart that can clearly convey this data to human operatives.

This project proposes the development of bespoke visualisation software to translate raw 360-GPR data from TRACKSCAN into immersive, intuitive, 3D digital survey environments to facilitate non-disruptive off-site inspections of tunnel structural integrity through the use of Virtual Reality (VR) hardware.

The main challenge to overcome is that the GPR data concerned is inherently non-planar, but helical, as TRACKSCAN utilises a unique rotary kinematic antenna array. Subsequently, leading algorithms for GPR data processing and visualisation, developed across numerous independently conducted research projects, cannot function on this data. Ergo, the primary research goal of this project is the formulation, implementation, and unification of novel procedural augmentations to these strategies for the development of algorithms optimised to enrich, segment and triangulate subsurface geometry from pioneering 360-GPR data.

The secondary challenge of this project is to address the prominent lack of interpretive clarity associated with feature identification in conventional GPR data displays, which has made structural information obtained inaccessible to non-radar trained rail industry operatives. Therefore, visualiser development will introduce a deep learning-based object classification scheme to dynamically highlight and qualitatively assess subsurface features (i.e. assets, defects, and medium characteristics) present within 360-GPR data in real time. Extracted information will then be intuitively conveyed to the end-user by design and integration of a dedicated streamlined user interface.

Additionally, this project seeks to improve achievable levels of operative immersion within the survey data, facilitating more ergonomic execution of virtual inspections and permitting unrestricted analysis of structural features onsite teams cannot easily view (e.g. narrow culvert interiors). This will be achieved through the creation of an original rendering engine, capable of translating subsurface geometry interpolated from the 360-GPR data into realistically textured 3D virtual environments. Navigation will utilise a commercial VR hardware unit, placing operatives directly within the digitally reconstructed tunnels, whilst texture realism will stem from the development of new algorithms to fuse 360-GPR data with corresponding near-surface 3D Lidar scans.

Research proposed will therefore centre on the Development of 3D Visualisation Algorithms for the Effective Interpretation of Tunnel Subsurface Radar Data generated by the new Infrastructure Inspection Radar system TRACKSCAN. This work will involve software engineering; practical testing using VR hardware; topics in machine learning, computational geometry, and human computer interaction within the gaming industry; alongside investigation of the physics and mathematics behind 360-GPR data inversion.