## 4 Summary  
The present paper began by detailing the development and application of a wide array of modal-based DSFs that have become well known in SHM literature. These DSFs are supported by a well-established theoretical base, but their application to real bridge structures have produced inconsistent comparative results and highlighted a susceptibility to noise, particularly for the more damage sensitive higher modes. Additionally, the assumption of linear stationary structural vibration response made by common frequency domain transformations used to obtain modal-based DSFs may not always be suited to real-World applications. The discussed shortcomings of modal-based DSFs in SHM were characterised into three categories as shown below, which formed the subject topics of the remaining sections of this paper. 1. Problematic influence of environmental & operational effects;
2. Inefficient utilization of machine learning algorithms for damage detection;
3. Over-reliance on modal-based damage sensitive features. Much of the problematic influence of environmental variations on modal-based DSFs' performance has been shown to be mitigated through the utilisation of carefully chosen multivariate regression models or the suitable application of pattern recognition algorithms. Simple linear regression models are not appropriate for locations of large temperature fluctuations and humid conditions, as freezing moisture content in bridge decks produces a nonlinear structural change. Multi-linear regression models provide a suitable option for most bridge types, such as [PERSON] et al. 's [98] robust Least Trimmed Square estimator. More complex nonlinear regression models require sufficient environmental data measurements to insure the accuracy and may be prone to over-fitting if not suitably defined. Research in the use of pattern recognition algorithms for the mitigation of environmental and operational effects has produced positive results, without the need to measure such variables. The utilisation of dimensionality reducing methodologies such as PCA and FA produce reliable results in linear investigations ([PERSON] et al. [103] and [PERSON] et al. [109], respectively) and provide a versatile platform for nonlinear applications ([PERSON]'s [110] non-linear FA application on the Z24 bridge, [PERSON] et al. [113]'s KPCA application on the Champangshiehl Bridge). However, the careful selection of data distribution parameters and number of extracted principle components/common factors is required for successful applications. In terms of the operational-induced effects on modal-based DSFs, this paper highlighted the caution of using frequency domain transformations that assume linear stationary structural response, as the vehicle-induced vibration of damaged bridges can be highly non-linear and non-stationary. For this reason, the authors highlighted the work by [PERSON] [130], who investigate the use of HHT for the early stage damage detection in bridges subjected to operational loading. The improvement and efficient utilization of machine learning algorithms for damage detection has been shown to improve the performance of modal-based DSFs. For instance, [PERSON] & [PERSON]'s [77] use of the Mahalanobis-Taguchi System, which combines multiple modal-based DSFs at once to enhance their collective damage sensitivity and performance consistency when applied to a steel truss bridge subjected to vehicle induced excitation. [PERSON] et al. [98; 136] demonstrated the need for robust methods of outlier detection for supervised learning techniques to ensure that training datasets are not contaminated with outliers, by incorporating the Minimum Covariance Determinate and the Minimum Volume Enclosing Ellipsoid into standard outlier detection methods on the Z-24 Bridge. Finally, for the advancement of SHM in bridges, this paper emphasized the necessity to explore and investigate alternative options to modal-based DSFs.