Ensemble Classifiers and Feature-Based Methods for Structural Damage Assessment

Abstract

In this paper, a new structural damage detection framework is proposed based on vibration analysis and pattern recognition, which consists of two stages: (1) signal processing and feature extraction and (2) damage detection by combining the classification result. In the first stage, discriminative features were extracted as a set of proposed descriptors related to the statistical moment of the spectrum and spectral shape properties using five competitive time-frequency techniques including fast S-transform, synchrosqueezed wavelet transform, empirical wavelet transform, wavelet transform, and short-time Fourier transform. Then, forward feature selection was employed to remove the redundant information and select damage features from vibration signals. By applying different classifiers, the capability of the feature sets for damage identification was investigated. In the second stage, ensemble-based classifiers were used to improve the overall performance of damage detection based on individual classifiers and increase the number of detectable damages. The proposed framework was verified by a suite of numerical and full-scale studies (a bridge health monitoring benchmark problem, IASC-ASCE SHM benchmark structure, and a cable-stayed bridge in China). The results showed that the proposed framework was superior to the existing single classifier and could assess the damage with reduced false alarms.

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

“…44,45 From either the magnitude or the power (squared amplitude of the STFT) features describing the statistical properties of the signal and the magnitude of the spectrum can also be computed. 46,47…”

“…44,45 From either the magnitude or the power (squared amplitude of the STFT) features describing the statistical properties of the signal and the magnitude of the spectrum can also be computed. 46,47 The use of cepstrum/cepstral coefficients as damage sensitive features has received a renewed focus in SHM over the last number of years. 37,[47][48][49] The Cepstrum is the inverse Fourier transform of the logarithm of the Fourier spectra magnitude squared.…”

“…46,47 The use of cepstrum/cepstral coefficients as damage sensitive features has received a renewed focus in SHM over the last number of years. 37,[47][48][49] The Cepstrum is the inverse Fourier transform of the logarithm of the Fourier spectra magnitude squared. 50 Cepstrum analysis has shown promise as an efficient measure for detecting changes in a signal that are not easily identified in the spectrum.…”

“…50 Cepstrum analysis has shown promise as an efficient measure for detecting changes in a signal that are not easily identified in the spectrum. 47 In Ref. [37], the link between cepstrum based features and modal properties of a structure are defined with cepstrum features found to be an effective measure of damage detection.…”

“…68 A number of filter, wrapper and embedded feature selection approaches have been applied to the binary scenario where two data sets are available from the Tianjin Yonghe Bridge before and after a damage occurrence. 27,47,69 . Feature selection based on a genetic algorithm (GA) is proposed in Ref.…”