## 6 Steps towards Criteria Fulfilment
### Automated Feature Detection and Evaluation  
If an end-user cannot clearly interpret subsurface data, the inspection yields little useful insight into tunnel structural health or targeted maintenance. Detecting degradation indicators is critical for localising damage, whilst characteristic evaluation (e.g., location, extent, maximal depth, etc.) informs repair urgency. Searching RTSSI visuals manually is impractical: tunnel datasets are cumbersome; defect types are wide-ranging through a tunnel's operational lifespan; human cognition speeds are slow and our evaluation is subjective. Unintuitive data visualisations only compound the issue (e.g., radargrams), explaining why research tackling Automated Feature Detection/Evaluation (AFD/E) accounts for over 1/3 of CCM-featured research connections and encompasses Convolutional Neural Network (CNN) [213, 214] and Deep Learning (DL) [215, 216, 49] detectors, alongside severity ranking schemes [217, 218]. Scope of feature variety and complexity current AFD can simultaneously identify with accuracy drew our attention. Restricting our consideration to subsurface studies, a DL image grid workflow flags four distinct features [96] (manhole, cavity, pipe, heterogeneous soil background), yielding widest feature detection scope, albeit not concurrently. Studies successfully achieving simultaneous detection of realistically complex defect configurations [219, 220] likewise favoured 2D GPR imagery but discriminated two types maximum [81]. However, with exception to [96], test environments featured only assets or defects. This implies GPR-based research is currently leading developments in AFD and seemingly the upper limits of DL feature detector capability have yet to be fully explored. Thus, any study classifying over four mixed type (assets, defects), variety (shaft, void, pipes) or complexity of features would mark a significant advance in RTSSI-AFD.  
Figure 11: Unlike conventional fixed direction antenna (**a**) which typically only image the railbed; rotating GPR antenna capture 360-degree subsurface profiles including tunnel sidewalls, haunch and crown (**b**).  
AFE, namely defect severity ranking, proves less researched. Contrary to our initial expectations of exclusively dictionary-based schemes, of studies found, most now adopt contextual evaluation via fuzzy logic devices [221; 222] and probabilistic analysis [33; 223]. We infer more complex evaluation grounds are being considered in parallel when grading repair urgency, if not yet for RTSSI. For example, many nearby cracks in close proximity can be of greater concern than one occurring in isolation. Collectively, this suggests development of a robust contextual severity ranking scheme for RTSSI would be of worthwhile pursuit in future research.  
ADF/E is clearly transitioning from proof-of-concept simulations to practical deployment tests. In RTSSI, schemes will need to discriminate tunnel assets from more hazardous defects, therefore training demands programmer knowledge of common features. For already aged masonry tunnels, we found no consolidated summary, concerning given a forecast 30-50% increase in rail-traffic demand by 2050 [224]. We therefore now present our own bespoke consolidated summary.