Abstract
Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young’s modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young’s modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort.
Highlights
Introduction censeeMDPI, Basel, Switzerland.In general, civil structures are prone to damage during their service life, leading to the loss of their serviceability and safety
A non-probabilistic method based on WWLS-support vector machine (SVM) algorithm is presented to consider the uncertainties, in the form of noise, in the process of damage detection of structures
An interval analysis is adopted for use with the WWLS-SVM, as an intelligent data analytics scheme, to consider the uncertainties using the interval bounds of the uncertainties in the input parameters of the surrogate model
Summary
The main focus of this research is to facilitate the assessment of uncertainties in SHM. Wavelet packet component energy is an effective method to define and characterize a specific signal phenomenon in the time-frequency domain. Yen and Lin’s [27] demonstrated that the energy stored in a specific frequency band, at a certain level of wavelet packet decomposition, provides more potential for signal feature than the coefficients alone. Sun and Chang [28] utilized sensitivity analysis, to compare the four damage indices of frequency, modal, flexibility, and energy changes of wavelet packets. The relative energy corresponding to the component signals of the structural acceleration response has been used as WWLS-SVM input, the relative energy Ei in i-frequency band can be expressed as: Ei =. The wavelet package relative energy (WPRE) Esp of the signals from sensor s is combined to obtain the fused feature vector [17]:. This fused feature vector will be used as the input of the surrogate model after implementing interval analysis method
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