Data Inspecting and Denoising Method for Data‐Driven Stochastic Subspace Identification

Abstract

Data-driven stochastic subspace identification (DATA-SSI) is frequently applied to bridge modal parameter identification because of its high stability and accuracy. However, the existence of abnormal data and noise components may make the identification result of DATA-SSI unreliable. In order to achieve a reliable identification result of the bridge modal parameter, a data inspecting and denoising method based on exploratory data analysis (EDA) and morphological filter (MF) was proposed for DATA-SSI. First, EDA was adopted to inspect the data quality for removing the data measured from malfunctioning sensors. Then, MF along with an automated structural element (SE) size determination technique was adopted to suppress the noise components. At last, DATA-SSI and stabilization diagram were applied to identify and exhibit the bridge modal parameter. A model bridge and a real bridge were used to verify the effectiveness of the proposed method. The comparison of the identification results of the original data and improved data was made. The results show that the identification results obtained with the improved data are more accurate, stable, and reliable.

Operational Modal Analysis on Bridges: A Comprehensive Review

“…Moreover, Tran et al [160] applied Combined Deterministic SSI alongside other methods for large-scale bridge identification. The SSI technique finds widespread use in assessing bridge performance through modal analysis [79,[161][162][163][164][165][166][167][168][169][170][171][172].…”

Nonlinear Spring-Mass-Damper Modeling and Parameter Estimation of Train Frontal Crash Using CLGAN Model

“…)) Update the parameters of the generator through Adam gradient drop algorithm: θ g � Adam(∇θ g (J g ), θ g ) End for ALGORITHM 1: CLGAN algorithm: mini-batch stochastic gradient descent training of generative adversarial net. 8 Shock and Vibration resists deformation at the moment of mid-high-speed crash; after that, the anticlimbing device and coupler buffer devices resist deformation. erefore, we can conclude that the anticlimbing energy absorber performance is best at middle and low speed, whereas a delayed state can be found at high speed by comparing and analyzing the parameters variation.…”

“…Many researchers are currently focusing on the development of new modeling techniques that make use of Machine Learning algorithms [8][9][10], and, among these, we can identify research directions that fall into three main categories: the first category consists of approaches that use FE method combined with Machine Learning. Stoffel et al [11] take into account the strain-rate and high dynamic deformation in nonlinear structural deformations and propose an intelligent finite element, where an Artificial Neural Network (ANN) is used instead of viscoelastic constitutive equations.…”