## 7 Discussion  
Method-centred subdivision of state of the art literature, spanning both data acquisition approaches and conveyance schemes (circa 2015-2021) reveals considerable recent advances in the capabilities of RTSSI-linked visualisation frameworks. Our review addresses two key knowledge gaps and presents three promising considerations for future research. Appendix A summarises our deconstruction of leading NDI techniques for RTSSI, establishing that a multitude of valid comparative grounds exist between methods. Their variable importance subject to survey context undermines heuristic direct comparisons of method performance, suitability or efficacy by balancing advantages against disadvantages, resulting in ambiguity and inconclusiveness. This justifies need for a robust definition of an 'effective' RTSSI visualisation framework to address the knowledge gap. Our formulation of explicit criteria from five key research gaps was based on common shortcomings identified between considered methods. Respective criterions consider the (i) completeness and (ii) duration of survey data acquisition; alongside the (iii) interpretive clarity, (iv) faithfulness and (v) interactivity of information conveyance. We overall find that despite recent innovations, a fully effective RTSSI visualisation framework has yet to be developed. Creation of our CCM facilitated inference of eight key avenues for future research. Grading connected developments by relevance drew distinction between emerging and established trends within the literature. Initial thoughts towards achieving complete, time-efficient RTSSI surveys highlights pioneering analysis of 360 GPR datasets from a novel rotary, air-launched GPR antenna. Already meeting three criteria, we anticipate development of a suitable information conveyance scheme will present a prime research opportunity, with feasible scope for creating the first fully effective RTSSI visualisation framework within the next decade. This would establish 360 GPR as a mainstream and potentially preferential technique amongst current RTSSI methods. We note over 1/3 of research categories concerning information conveyance are aligned to automated target detection and defect severity ranking. However, their practical application on 3D datasets containing realistic quantities, varieties and complexities of subsurface features remains largely unexplored. We believe successful trails on authentic RTSSI datasets present an upcoming milestone for future research efforts to achieve if devised methods are to eventually aid real surveys. Thoughts on detection scheme training for recognition of subsurface features in masonry tunnels flagged a further knowledge gap. Namely, this review was unable to find a combined nor comprehensive list of common masonry-related assets and defects within recent literature. Focus primarily centred on modern concrete tunnels. To address this, we presented our own bespoke consolidated summary for masonry tunnels. Our exploration of emerging trends in dynamic interaction with current visualisation frameworks found studies addressing dataset interfacing with XR hardware featured 63% more prevalently than BIM/DTT system design. Most notable interaction potential was demonstrated by a VR tunnel surface survey platform presented in [218], which justified by end-user trails, quantifiably evidences the high levels of intuitiveness both 3D rendered environments and contextual dashboard modules can achieve. As with most encountered schemes, optimisation is for surface surveys only and no preview facility is provided during data acquisition. Arguably, this could also be beneficial; encouraging off site analysis of survey data in safer environments reduces crew risk. Nonetheless, based on this review we believe VR presents the most versatile and intuitive tunnel survey interaction medium presently available, therefore would provide an ideal basis for future RTSSI visualisation framework developments.