## 1 Introduction  
An aging road and rail infrastructure has initiated and sustained a considerable body of research material over recent decades in the pursuit of enhanced certainty of structural condition and safety. Many bridge structures are now subjected to traffic loading conditions far in advance of their original design criteria. This continual increase in operational condition loading accelerates structural fatigue and reduces service-life; so much so that structural fatigue is no longer a single-structure problem, but a national network issue [1]. Despite the magnitude of academic work on bridge damage detection and identification, the vast majority of in-service bridge data is still collected via visual inspections. These methods are considered to be tried and trusted within the bridge-owner community, but possess several limitations in their scope and attain inconsistent results due to the disparity of inspector competency. International inconsistencies are also prevalent with regards to damage parameter measurement, non-destructive testing methods, condition state definitions, and inspection frequencies [2], which has resulted in traditional inspection methods being perceived as overly subjective, resulting in recent measures being taken by the international research community to standardise cross-border practices and definitions [3; 4]. Recent figures estimate Europe's highway bridge count to be circa one million [5], which would infer that periodic visual inspections alone are not a practical option, and this has led to many bridge-owners calling for increased integration of monitoring techniques with standard inspections [6]. Moreover, as maintenance costs dramatically increase when damage is left unattended, it is imperative that bridge-owners detect such changes as soon as possible and conduct the necessary maintenance work in a timely manner to minimise life cycle costs. To this end, extensive research in the areas of bridge maintenance and maintenance scheduling [7; 8; 9], in addition to reliability analysis [10; 11; 12; 13], has been conducted to assist the challenge, although this is also a work in progress. For example, a traditionally popular method of maintenance scheduling is to use deterministic functions to conduct time-dependant reliability analyses to model structural deterioration [14]; however, this method of scheduling has been shown to neglect the time-dependency of uncertainty in resistance, which increases over time [15]. [PERSON] et al. [16] demonstrated that scheduling maintenance actions in this way causes substantial maintenance delays, resulting in increased deterioration and repair costs for more bridges than previously thought. A more efficient option for maintenance scheduling is to use known performance indicators that represent the current structural condition and administer maintenance works once the performance indicators breach defined thresholds, in a method known as Reliability-Based Scheduling [17]. Traditionally, the performance indicators used for monitoring purposes have been modal-based parameters. Many techniques incorporating modal parameters have been proposed, developed, and expanded upon over the years, several of which are detailed in the following review papers [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. However, the accurate determination of modal parameters from in-service bridges has proven to be quite a non-trivial task that faces numerous challenges and limitations. The present review paper differentiates from its predecessors by firstly identifying the most prevalent challenges and limitations faced by modal-based Damage Sensitive Features (DSFs) and by subsequently presenting novel solutions specific to each contentious area. The ultimate aim of such methodologies is to fulfil four stages of assessment, as proposed by [PERSON] [29] (see Table 1).