EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure

Abstract

Truss-type designs are widely used in civil structures. Despite the fact that they are robust and reliable structures, different kinds of damage can appear. In order to avoid human and economic losses, the development and application of damage-detection methodologies are paramount. In this work, a methodology based on the empirical mode decomposition (EMD) method and the Shannon Entropy Index (SEI) to detect incipient damages associated with corrosion in a 3D 9-bay truss-type bridge is presented. As different EMD methods are presented in literature, the most representative methods are investigated in order to evaluate their performance for this task. To this end, the vibration signals generated in the truss-type bridge at different conditions are analyzed. For the damage condition, four severity levels of simulated corrosion (1 mm, 3 mm, 5 mm, and 8 mm of diameter reduction) generated into the elements of truss-type bridge are considered. Results demonstrate the effectiveness of the proposal in terms of detecting corrosion in its very early stage (1 mm of reduction in the element).

Output-Only Parameters Identification of Earthquake-Excited Building Structures with Least Squares and Input Modification Process

“…To date, a number of vibration-based system identification and damage detection methods have been developed, and review articles with different emphases can be also found [2][3][4]. In general, structural parameters identification can be performed in three different paradigms: (i) time domain (e.g., [5][6][7][8][9][10]), (ii) frequency domain (e.g., [11][12][13][14]), and (iii) time-frequency domain (e.g., [15][16][17]).…”

A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy

“…On the other hand, their application areas are expanding and deepening gradually. EMD has been widely used in different fields, such as short-term wind speed forecasting combined with hybrid linear and nonlinear models [8], the detection and location of pipeline leakage [9], the detection of incipient damages for truss structures [10], denoising for grain flow signal [11], biomedical photoacoustic imaging optimization [12] and heart rate variability analysis [13]. Many scholars have also applied EEMD to their research fields, such as wind speed forecasting combined with the cuckoo search algorithm [14], machine feature extraction combined with a kernel-independent component [15], feature extraction for motor bearing combined with multi-scale fuzzy entropy [16], a bearing fault diagnosis combined with correlation coefficient analysis [17], a partial discharge feature extraction combined with sample entropy [18] and monthly streamflow forecasting combined with multi-scale predictors selection [19].…”

An Improved Objective Function for Modal-Based Damage Identification Using Substructural Virtual Distortion Method

“…Structural damage identification plays an important role in SHM, which provides a reliable theoretical foundation for monitoring, early warning and safety assessment of large-scale structures [1]. In recent years, many effective methods for SHM [2][3][4][5][6][7] and structural damage identification [8][9][10][11][12][13] have been presented. However, it is still difficult to identify the extent of structural damage accurately, and one of the reasons is a limited number of measurement points in large-scale structures.…”