Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study

Abstract

Signal processing is one of the essential components in vibration-based approaches and damage detection for structural health monitoring. Since signals in the real world are often nonlinear and non-stationary, especially in extended and complex structures, such as bridges, the Hilbert–Huang transform is used for damage assessment. In recent years, the empirical mode decomposition technique has been gradually used in structural health monitoring and damage detection. In this article, the application of complete ensemble empirical mode decomposition with adaptive noise technique is investigated to identify the presence, location, and severity of damage on a steel truss bridge model. The target is built at laboratory conditions and experimentally subjected to white noise excitations. By employing complete ensemble empirical mode decomposition with adaptive noise technique, four key features extracted from the intrinsic mode functions, including energy, instantaneous amplitude, unwrapped phase, and instantaneous frequency, are assessed to localization, quantification, and detection of damage both quantitatively and qualitatively. In addition, to further explore the sensitivity of the damage detection approach based on the complete ensemble empirical mode decomposition with adaptive noise technique method, several improved damage indices are proposed based on the combinations of two statistical time-history features, including kurtosis and entropy features with the energy and instantaneous amplitude features of the analyzed signal. The experimental results from the damage indices based on the extracted features demonstrate the robustness, superiority, and more sensitivity of the complete ensemble empirical mode decomposition with adaptive noise technique method in addressing the damage location, classifying the severity, and detecting the damage compared to empirical mode decomposition and ensemble empirical mode decomposition techniques.

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

“…The real-time locating systems (RTLS) have been proved to be valid in construction productivity enhancement. However, the implementation is slow because key factors have been overlooked, such as cost and deployment [ 25 , 26 , 27 ]. Besides, laser scanners are effective in construction activities monitoring by laser signals from a rotating laser photon source [ 28 , 29 ].…”

Damage detection and characterization of a scaled model steel truss bridge using combined complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification approach

“…Therefore, since the first IMF gives the most energy value and contains the highest frequency content of the measured signal (RMS amplitude), it is appropriate to be considered as the index IMF in the damage detection procedure of this study. In addition, based on an investigation carried out in the previous study, 41 the capability and advantages of the CEEMDAN in solving the mode mixing problem associated with EMD and EEMD techniques and preserving the original information of the analyzed signal were concluded. Hence, the CEEMDAN can presents a complete decomposition approach with an exact reconstruction of the original signal by summing the generated mode components.…”

“…More detailed information about the superiority assessment of the CEEMDAN compared to traditional EMD-based techniques can be found in the previous study. 41 The performance of several traditional spectral analysis methods including FFT, PSD, and FDD techniques in identifying the presence and severity of damage in the truss is firstly evaluated. To this end, the acceleration responses of low-range frequencies compared to the frequencies of the bridge in a healthy state.…”

“…Therefore, a more powerful technique with the ability to detect and extract the close-mode parameters of a structure with greater accuracy and noise immunity is of interest. The superiority of CEEMDAN technique in identifying the presence, severity, and location of a predefined damage in a laboratory-scale steel truss bridge model was found by Mousavi et al 41 in comparison to EMD and EEMD techniques. In line with this study, Mousavi et al 42 utilized CEEMDAN in combination with an artificial neural network for structural damage localization and quantification of the same steel truss bridge model experimentally exposed to white noise excitations.…”

Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement

“…Generally, artificial intelligence (AI) techniques are able to address some of the previous engineering issues due to their advantages compared to classic methods [31][32][33][34][35][36]. Learning and mocking are two significant points of AI, which make these algorithms favourable for researchers [37][38][39][40][41]. Employing optimisation techniques such as back-propagation algorithms [42], a raw model of artificial neural networks (ANNs) is generally developed.…”