Overtime, the structural condition of bridges tends to decline due to a number of degradation processes, such as; creep, corrosion and cyclic loading, among others. Considerable research has been conducted over the years to assess and monitor the rate of such degradation with the aim of reducing structural uncertainty. Traditionally, vibration-based damage detection techniques in bridges have focused on monitoring changes to modal parameters and subsequently comparing them to numerical models. These traditional techniques are generally time consuming and can often mistake changing environmental and operational conditions as structural damage. Recent research has seen the emergence of more advanced computational techniques that not only allow the assessment of noisier and more complex data, but also allow research to veer away from monitoring changes in modal parameters alone. This paper presents a review of the current state-of-the-art developments in vibration based damage detection in small to medium span bridges with particular focus on the utilisation of advanced computational methods, such as machine learning, pattern recognition and advanced data normalisation algorithms.