A review on fatigue monitoring of structures

García-Fernández, N.; Aenlle, Manuel López; Álvarez-Vázquez, Adrián; Muñiz‐Calvente, Miguel; Pelayo, F.

doi:10.1108/ijsi-09-2022-0117

Cited by 8 publications

(5 citation statements)

References 185 publications

(389 reference statements)

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“…Note that in accordance with [9,17], the strain-based approach follows the same modelling pattern, based on compatibility, as that applied to an extended stress-based approach, which includes the LCF, HCF and VHCF domains, as well as the influence of the stress ratio, R, see [18], but even including the influence of the test frequency [12,19]. Consideration of the different fatigue zones is also analysed in [10,20].…”

Section: Different Methods For Working With Aimentioning

confidence: 99%

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Automatic machine learning versus human knowledge-based models, property-based models and the fatigue problem

Castillo,

Fernández Canteli,

Muñiz Calvente

2023

Phil. Trans. R. Soc. A.

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This paper is devoted to emphasizing the importance of human-based knowledge and to present the original property-based models. The main idea is that models are built in terms of equations that reproduce and guarantee their satisfaction, which leads to non-arbitrary parametric models. The methods based on data alone are not sufficient to be applied to fatigue S–N and GRV-N models: first, the required high number of results in machine learning is not attained in fatigue; second, a black box cannot supply a comprehension of the fatigue phenomenon; third, the absence of a supporting model impedes extrapolation beyond the scope of experimentation; and fourth, many other data are required to include the stress ratio, R , while robust models are already available. These models are artificial intelligence mixed models, where its main part is human-based whereas the parameter estimation is solved based on data. The fatigue problem is used to illustrate the methodology and show that its generalization is applicable to other real problems. A detailed analysis of the fatigue model properties is done to show the readers how to extend those properties to other cases. Some future lines of research are suggested, followed by some conclusions. This article is part of the theme issue ‘Physics-informed machine learning and its structural integrity applications (Part 2)’.

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Section: Different Methods For Working With Aimentioning

confidence: 99%

“…In the cumulative damage calculation of structures under real loads, fatigue damage process monitoring is studied, separately, using structural health monitoring and continuous fatigue monitoring whereas a combination of both techniques, with possible application of ML, is still in development, see [10].…”

Section: Introductionmentioning

confidence: 99%

Automatic machine learning versus human knowledge-based models, property-based models and the fatigue problem

Castillo,

Fernández Canteli,

Muñiz Calvente

2023

Phil. Trans. R. Soc. A.

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“…SHM generally refers to any type of damage detection procedure. Generally speaking, SHM is the automation of the condition assessment process of an engineered system [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…These kinds of sensors require meticulous casting process along with other technological processing to develop a suitable sensor for special applications. Last but not least, applications can have an extraordinary impact on industry only considering the constrains of the sensors and modern Machine Learning (ML) [2,5] approach along with latest edge computing technology [6,7], its high scale integration into microcontrollers or cloud computing. Note that ML for SHM has become popular due to technological advances in sensors [8,9], high-speed Internet and cloud computing.…”

Section: Introductionmentioning

confidence: 99%

Embedded Devices and Methods for Development of Special Non-Contact Applications

Zaporojan,

Munteanu,

Larin

et al. 2024

J. Eng. Sci.

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There are applications which require particular approaches for each of the used parts. The non-contact strain sensors based on microwires with positive magnetostriction requires specific technological processes starting from casting until development of the corresponding non-contact sensor device. The developed contactless sensors can be embedded into various industry critical parts which require continuous monitoring and maintenance. Hence, specific embedded devices and specific framework should be developed to provide industry reliable solution. The paper describes the technological process of casting microwires, methods of improvement casting process and stress annealing methodology used to obtain appropriate non-contact sensible elements based on microwires. Also, the paper describes the framework of using these elements in non-contact monitoring applications of the condition of specific engineering objects/structures.

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“…Structural health monitoring (SHM) is another field where correlation techniques play a significant role. Dynamic SHM methods, those that use vibration responses to gather information about changes in a structure's dynamic properties, can be classified as modelbased SHM or data-based SHM [35]. Model-based techniques use a finite element model of the structure to predict the dynamic response of the structure and allow damage to be detected and located.…”

Section: Introductionmentioning

confidence: 99%

Mass and Stiffness Correlation Using a Transformation Matrix

García Fernández,

Fernández Fernandez,

Brincker

et al. 2024

Infrastructures

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Model correlation techniques are methods used to compare two different models, usually a numerical model and an experimental model. According to the structural dynamic modification theory, the experimental mode shapes estimated by modal analysis can be expressed as a linear combination of the numerical mode shapes through a transformation matrix T. In this paper, matrix T is proposed as a novel model correlation technique to detect discrepancies between the numerical and the experimental models in terms of mass. The discrepancies in stiffness can be identified by combining the numerical natural frequencies and the matrix T. This methodology can be applied to correlate the numerical and experimental results of civil (bridges, dams, towers, buildings, etc.), aerospace and mechanical structures and to detect damage when using structural health monitoring techniques. The technique was validated by numerical simulations on a lab-scaled two-span bridge considering different degradation scenarios and experimentally on a lab-scaled structure, which was correlated with two numerical models.

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A review on fatigue monitoring of structures

Cited by 8 publications

References 185 publications

Automatic machine learning versus human knowledge-based models, property-based models and the fatigue problem

Automatic machine learning versus human knowledge-based models, property-based models and the fatigue problem

Embedded Devices and Methods for Development of Special Non-Contact Applications

Mass and Stiffness Correlation Using a Transformation Matrix

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