## 3 Advancements and Alternatives to Modal-Based Damage Identification
### Advancements to Environmental & Operational Challenges
#### 3.1.3 Advancements to Operational Specific Challenges  
An accurate determination of environmental and operational conditions is an important aspect of bridge SHM and can require substantial instrumentation, particularly for operational specific variables. Recently, Fiber Bragg Grated (FBG) sensors have be employed as strain and temperature sensors, as [PERSON] et al. [122] demonstrated on the Fu-Sui Bridge in China, who obtained temperature data and traffic volume in real-time. For temperature measurements, the dynamic strain histories were passed through a smoothing low-pass moving average filter, which produces low frequency strain evolution that is directly correlated with recorded temperatures. For traffic volume measurements, results from previous findings [123; 124] showed that traffic-induced strain fluctuations were shown to be identifiable as peak impulses in dynamic strain histories. Therefore, [PERSON] et al. set out to isolate the traffic-induced strain fluctuations from the FBG sensors by first applying a wavelet decomposition filter to the original strain measurements to enhance the resolution. Then, a simple low-pass moving average filter was applied to smooth out the traffic impulses. Vehicle passages were then identified by extracting the impulses within the residual leftover between the filtered and smoothed strains. The vehicle type was determined based on the thresholds applied to the strain impulses. Over the course of the monitoring campaign, the traffic volume of the Fu-Sui Bridge was deemed to have increased by 55%.  
The utilization of the Hilbert-Huang Transformation's (HHT) [125] ability to decompose non-stationary and non-linear signals into instantaneous frequency and energy has recently seen a rise in popularity, with [PERSON] et al. [126] compiling a comprehensive review on its application in SHM. The HHT is a two-step process that firstly consists of a decomposition stage called Empirical Mode Decomposition (EMD) [125], whereby the raw vibration signal is transformed into multiple vibration signals of equal duration known as Intrinsic Mode Functions (IMFs). EMD employs a \"shifting process\" to separate time series composed of frequencies in decreasing order, with the first IMF generally composed of high frequency noise that can be rejected. The remaining IMFs should portray the dominant frequencies of the system, such as instantaneous modal frequencies. Unlike Fourier-based transformations, EMD's empirical methodology allows the nonlinear characteristics of the raw signal to be maintained, which is beneficial for the assessment of traffic-induced vibration.  
In fact, the utilization of the EMDs alone can yield identification of structural damage based on changes to its intrinsic mode functions' curvature [127]. Additionally, many SHM applications of the HHT have been conducted on data obtained from numerical models [128] and laboratory-based experiments [129]. However, recent research by Moughty & Casas [130] has focused on the potential of using the HHT to detect early stage damage in bridges subjected to operational loading. The premise of the application of HHT in this way stems from the fact that the majority of bridges are designed to behave linear-elastically under operational design loads, which implies that healthy bridges subjected to normal operational conditions should produce a nonlinear, but stationary dynamic response. However, the presence of damage will induce nonlinear-inelastic structural behavior, resulting in signal non-stationarity that should be identifiable in the HHT spectrum. The SHM benefit of this method over other frequency domain transformations that assume stationarity is that earlier damage detection may be possible, as vehicle loading may momentarily induce a modal frequency change (non-stationarity), which would be otherwise masked in Fourier type transforms.