The challenges and future trends in the development of signal processing tools are being widely used for damage identification in bridges. Therefore, it is important to analyse the vibration signals in order to attain effective damage characterization.In this paper, the non-linear and non-stationary dynamic response of bridges under operational loads is studied. First, the signals are decomposed into intrinsic mode functions (IMF) by a novel Improved Completed Ensemble EMD with Adaptive Noise technique (ICEEMDAN). Hilbert-Huang transform is used to obtain their corresponding Hilbert spectra. The marginal Hilbert spectrum (MHS) of each IMF and the instantaneous phase difference (IPD) are proposed as total damage indicators (DI), in the sense that they are able to detect, localize and quantify damage under transient vibration due to traffic. The methodology was tested in two case studies: (i) a numerical model of a two-span steel bridge (ii) a dynamic test conducted on a real steel arch bridge subjected to a series of artificial damages. The experimental and real case results from the damage indices based on the extracted features demonstrate the robustness and more sensitivity of the novel Improved Completed Ensemble EMD with Adaptive Noise technique (ICEEMDAN) in addressing the damage location.

Monitoring structural damage is widely used for sustaining and preserving the service life in civil structures, especially in bridges. The influence of environmental variability like temperature affects the dynamic behavior, which can mask subtler structural changes caused by damage. The direct application of vibration-based damage detection methods to measured responses without a prior treatment of the ambient data may lead to false condition assessments. In this article, the main objective is to separate the structural damage conditions from the changes caused by the environmental effects in a numerical benchmark bridge. The Principal Component Analysis (PCA) is applied to decide if the change in vibration characteristics is due to environmental effects or structural damages. The proposed approach in the use of PCA not only allows to detect the damage without the requirement of the baseline to consist of damage sensitivity features obtained from a wide range of environmental conditions, but also serves as a measure for its quantification. The effectiveness and robustness of the proposed methodology is applied to a benchmark bridge structure generated as part of COST Action TU1402 on quantifying the value of information (VoI) in SHM. The benchmark model consisted of a two-span steel bridge under environmental effects, in which two levels of damage were introduced.

One of the main challenges for bridge damage identification using monitoring data is to acquire sensitive damage features but insensitive to operational and environmental effects as well as noise. Specifically, the temperature as part of environmental variability can mask structural damages in bridges. Inspired by the capabilities of machine learning methods, Principal Component Analysis (PCA) has been applied here as a well-known and robust technique for removing environmental variability. As a first purpose, PCA is used considering only ambient vibrations and the natural frequencies are considered as damage indicators. As a second objective, PCA in conjunction with Hilbert Huang Transform (HHT) and Variational Mode Decomposition (VMD) are applied to eliminate the environmental influence in transient vibrations due to traffic. The combined methodology is applied to the case of a numerical benchmark by using the Instantaneous Phase Difference (IPD) as novel vibration damage feature in the case of non-stationary vibrations. The results show that the proposed strategy to use the non-stationary vibration due to traffic instead of ambient vibration seems a promising tool for damage identification and, therefore, its capabilities in real bridge applications are worth exploring further when experimental data from real bridges will become available.