Comparative Analysis of Wavelet Transform for Time-Frequency Analysis and Transient Localization in Structural Health Monitoring

A critical problem facing data collection in structural health monitoring,for instance via sensor networks,is how to extract the main components and useful features for damage detection.A structural dynamic measurement is more often a complex time-varying process and therefore,is prone to dynamic changes in time-frequency contents.To extract the signal components and capture the useful features associated with damage from such nonstationary signals,a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required.Wavelet analyses have proven to be a viable and effective tool in this regard.Wavelet transform(WT)can analyze different signal components and then comparing the characteristics of each signal with a resolution matched to its scale.However,the challenge is the selection of a proper wavelet since various wavelets with varied properties that are to analyze the same data may result in different results.This article presents a study on how to carry out a comparative analysis based on analytic wavelet scalograms,using structural dynamic acceleration responses,to evaluate the effectiveness of various wavelets for damage detection in civil structures.The scalogram’s informative time-frequency regions are examined to analyze the variation of wavelet coefficients and show how the frequency content of a signal changes over time to detect transient events due to damage.Subsequently,damage-induced changes are tracked with time-frequency representations.Towards this aim,energy distribution and sharing information are investigated.The undamaged and damaged simulated comparative results of a structure reveal that the damaged structure were shifted from the undamaged structure.Also,the Bump wavelet shows the best results than the others.

A Fatigue Damage Model for FRP Composite Laminate Systems Based on Stiffness Reduction

This paper introduces a stiffness reduction based model developed by the authors to characterize accumulative fatigue damage in unidirectional plies and(0/θ/0)composite laminates in fiber reinforced polymer(FRP)composite laminates.The proposed damage detection model is developed based on a damage evolution mechanism,including crack initiation and crack damage progress in matrix,matrix-fiber interface and fibers.Research result demonstrates that the corresponding stiffness of unidirectional composite laminates is reduced as the number of loading cycles progresses.First,three common models in literatures are presented and compared.Tensile viscosity,Young’s modulus and ultimate tensile stress of composites are incorporated as key factors in this model and are modified in accordance with temperature.Four types of FRP composite property parameters,including Carbon Fiber Reinforced Polymer(CFRP),Aramid Fiber Reinforced Polymer(AFRP),Glass Fiber Reinforced Polymer(GFRP),and Basalt Fiber Reinforced Polymer(BFRP),are considered in this research,and a comparative parameter study of FRP unidirectional composite laminates with different off-angle plies using control variate method are discussed.It is concluded that the relationship between the drop in stiffness and the number of cycles also shows three different regions,following the mechanism of damage of FRP composites and the matrix is the dominant factor determined by temperature,while fiber strength is the dominant factor that determine the reliability of composite.

A Deep Learning Based Approach for Response Prediction of Beam-like Structures

Beam-like structures arde a class of common but important structures in engineering.Over the past few centuries,extensive research has been carried out to obtain the static and dynamic response of beam-like structures.Although build-ing the finite element model to predict the response of these structures has proven to be effective,it is not always suitable in all the application cases because of high computational time or lack of accuracy.This paper proposes a novel approach to predict the deflection response of beam-like structures based on a deep neural net-work and the governing differential equation of Euler-Bernoulli beam.The Prandtl-Ishlinskii model is introduced as an element of prediction model to simu-late the plasticity of this beam structure.Finally the application of the proposed approach is demonstrated through four numerical examples including linear elastic/ideal plastic beam under concentrated/sinusoidal load and elastic/plastic continues beam under seismic load to demonstrate a proof of concept for the effectiveness of this AI-based approach.