Identification of cracks in an Euler–Bernoulli beam using Bayesian inference and closed-form solution of vibration modes

Proceedings of the Institution of Mechanical Engineers, Part L:

et al. 2022

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A novel dynamic soil-structure interaction model is developed for analysis for Euler–Bernoulli beam rests on a spatially random transversely isotropic viscoelastic foundation subjected to moving and oscillating loads. The dynamic equilibrium equation of beam-soil system is established using the extended Hamilton's principle, and the corresponding partial differential equations describing the displacement of beam and soil and boundary conditions are further obtained by the variational principles. These partial differential equations are discretized in spatial and time domains and solved by the finite difference (FD) method. After the differential equations of beam and soil are discretized in the spatial domain, the implicit iterative scheme is used to solve the equations in the time domain. The solving result shows the FD method is effective and convenient for solving the differential equations of beam-soil system. The spring foundation model adopted the modified Vlasov model, which is a two-parameter model considering the compression and shear of soil. The advantage of the present foundation model is avoided estimating input parameters of the modified Vlasov model using prior knowledge. The present solution is verified by publishing solution and equivalent three-dimensional FE analysis. The present model produced an accurate, faster, and effective displacement response. A few examples are carried out to analyze the parameter variation influence for beam on spatially random transversely isotropic viscoelastic soil under moving loads.

Section: Introductionmentioning

confidence: 99%

Dynamic response analysis of Euler–Bernoulli beam on spatially random transversely isotropic viscoelastic soil

Feng

Proceedings of the Institution of Mechanical Engineers, Part L:

et al. 2022

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“…When such damages remain undetected and unrepaired, they can negatively impact the functionality and integrity of the structure and may even lead to structural failure. Accordingly, structural damage identification plays a crucial role in achieving the maintainability, safety, and reliability of structures [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification

Mohebian

Aval

et al. 2022

Sensors

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Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures.

“…Most structural health monitoring (SHM) strategies in mechanical and civil engineering use dynamic response information via signal processing to design an integrated intelligent monitoring system 1–12 . Wavelet is a time–frequency analysis tool that can play a vital role in a variety of signal processing schemes 13–15 and provides detailed information, which other methods such as Fourier transform and Hilbert–Huang transform are not able to capture 16 .…”

Section: Introductionmentioning

confidence: 99%

Selecting optimum levels of wavelet multi‐resolution analysis for time‐varying signals in structural health monitoring

Struct Control Health Monit

Altabey

Ghiasi

2021

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Summary Although the effectiveness of discrete wavelet transform (DWT) for analyzing time‐varying measurements has been studied for mechanical and civil engineering applications, a few fundamental questions still need to be answered, such as how to choose an optimal decomposition level (DL) for a specific task. Selection of the optimal DL still remains a question that has not been adequately explored and not thoroughly investigated. Vast majority of wavelet based methods in mechanical and civil engineering applications have been associated with nonstationary measurements and do not offer detail information on how DL should be selected or linked to useful features that can identify anomalies in a structure. This study proposes a new detailed framework for choosing the optimal DL to guarantee an effective wavelet analysis for time‐varying structural responses. The approach proposed in this paper uses various wavelets and theoretical levels to decompose the signal and to identify those aspects that interact to affect the DL, such as data characteristics, frequency band features, noise reduction, similarity, sharing information, and reconstruction quality. To show the effectiveness of our proposed method, we considered a comparative study using various mother wavelets on El Centro earthquake and acceleration responses from shaking table results of physical experiment. Experimental results show that the optimal DL for El Centro earthquake is 4 and for the acceleration data is 6. These results obviously demonstrate the stability and the robustness of the proposed method in the analysis of contaminated time‐varying signals.