Information-theoretical noninvasive damage detection in bridge structures

Ambegedara, Amila Sudu; Sun, Jie; Janoyan, Kerop D.; Bollt, Erik M.

doi:10.1063/1.4967920

Cited by 10 publications

(9 citation statements)

References 31 publications

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“…Ambegedara et al 17 study noninvasive damage detection of highway bridges. The authors consider informationtheoretic measures, including entropy and mutual information which require minimal assumptions regarding the specific location, material, and age of the bridge.…”

Section: Contributions To the Focus Issuementioning

confidence: 99%

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Introduction: Collective dynamics of mechanical oscillators and beyond

Belykh

Porfiri

2016

Chaos: An Interdisciplinary Journal of Nonlinear Science

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This focus issue presents a collection of research papers from a broad spectrum of topics related to the modeling, analysis, and control of mechanical oscillators and beyond. Examples covered in this focus issue range from bridges and mechanical pendula to self-organizing networks of dynamic agents, with application to robotics and animal grouping. This focus issue brings together applied mathematicians, physicists, and engineers to address open questions on various theoretical and experimental aspects of collective dynamics phenomena and their control. Published by AIP Publishing.[http://dx.doi.org/10.1063/1.4967727]Collective behavior in mechanical systems was discovered by the Dutch scientist Christiaan Huygens around 1665. 1 In the Huygens' setup, two pendulum clocks, hanging from a wooden beam, showed an "odd" symmetry and ended up oscillating in perfect anti-phase. [2][3][4] In recent years, collective dynamics has become an important topic with applications in a wide spectrum of biological and technological networks, including multi-robot teams, complex mechanical structures, and pedestrian bridges. This focus issue aims at the largely unexplored area of mathematical analysis and modeling of cooperative networks arising from different applications in mechanics and beyond. This highly interdisciplinary issue presents new research contributions which integrate knowledge from different disciplinary areas in applied mathematics and engineering, including stability theory, information theory, piecewise smooth and stochastic dynamical systems and networks, graph theory, classical mechanics, and bio-mechanics. We hope that this collection will contribute to further igniting interest in collective dynamics of mechanical oscillators and promoting interdisciplinary collaborations.

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Section: Contributions To the Focus Issuementioning

confidence: 99%

“…This problem is touched upon in this focus issue in regard to damage identification in highway bridges. 17 …”

Section: Open Questions and Future Challengesmentioning

confidence: 99%

Introduction: Collective dynamics of mechanical oscillators and beyond

Belykh

Porfiri

2016

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…In particular, contrasting forecasts is the defining concept underlying Granger Causality (G-causality), and it is closely related to the concept of information flow as defined by transfer entropy [7,8], which can be proved as a nonlinear version of Granger's otherwise linear (ARMA) test [9]. In this spirit, we find methods such as Convergent Cross-Mapping method (CCM) [10], and causation entropy (CSE) [11] to disambiguate direct versus indirect influences [11][12][13][14][15][16][17][18]. On the other hand, closely related to information flow are concepts of counter factuals: "what would happen if ..." [19] that are foundational questions for another school leading to the highly successful Pearl "Do-Calculus" built on a specialized variation of Bayesian analysis [20].…”

Section: Introductionmentioning

confidence: 99%

On Geometry of Information Flow for Causal Inference

Surasinghe

Bollt

2020

Entropy

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Causal inference is perhaps one of the most fundamental concepts in science, beginning originally from the works of some of the ancient philosophers, through today, but also weaved strongly in current work from statisticians, machine learning experts, and scientists from many other fields. This paper takes the perspective of information flow, which includes the Nobel prize winning work on Granger-causality, and the recently highly popular transfer entropy, these being probabilistic in nature. Our main contribution will be to develop analysis tools that will allow a geometric interpretation of information flow as a causal inference indicated by positive transfer entropy. We will describe the effective dimensionality of an underlying manifold as projected into the outcome space that summarizes information flow. Therefore, contrasting the probabilistic and geometric perspectives, we will introduce a new measure of causal inference based on the fractal correlation dimension conditionally applied to competing explanations of future forecasts, which we will write G e o C y → x . This avoids some of the boundedness issues that we show exist for the transfer entropy, T y → x . We will highlight our discussions with data developed from synthetic models of successively more complex nature: these include the Hénon map example, and finally a real physiological example relating breathing and heart rate function.

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“…Such techniques are based on algorithms that can be trained to recognize novelty in the response of a system, that is, to identify if its characteristics have changed due to damage [16][17][18] . A less studied area, which still holds great promise is the use of information-theoretic tools in which the presence of damage is associated with a modification of the flow of information within the system, from a source to a receiver 19 . Despite their many advantages, existing methods rely on the collection of large amounts of data, thereby posing challenges at the levels of sensor placement and data transfer 15 .…”

Section: Introductionmentioning

confidence: 99%

The detection matrix as a model-agnostic tool to estimate the number of degrees of freedom in mechanical systems and engineering structures

Celli

Porfiri

2022

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Estimating the number of degrees of freedom of a mechanical system or an engineering structure from the time-series of a small set of sensors is a basic problem in diagnostics, which, however, is often overlooked when monitoring health and integrity. In this work, we demonstrate the applicability of the network-theoretic concept of detection matrix as a tool to solve this problem. From this estimation, we illustrate the possibility to identify damage. The detection matrix, recently introduced by Haehne et al. [Phys. Rev. Lett. 122, 158301 (2019)] in the context of network theory, is assembled from the transient response of a few nodes as a result of non-zero initial conditions: its rank offers an estimate of the number of nodes in the network itself. The use of the detection matrix is completely model-agnostic, whereby it does not require any knowledge of the system dynamics. Here, we show that, with a few modifications, this same principle applies to discrete systems, such as spring-mass lattices and trusses. Moreover, we discuss how damage in one or more members causes the appearance of distinct jumps in the singular values of this matrix, thereby opening the door to structural health monitoring applications, without the need for a complete model reconstruction.

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Information-theoretical noninvasive damage detection in bridge structures

Cited by 10 publications

References 31 publications

Introduction: Collective dynamics of mechanical oscillators and beyond

Introduction: Collective dynamics of mechanical oscillators and beyond

On Geometry of Information Flow for Causal Inference

The detection matrix as a model-agnostic tool to estimate the number of degrees of freedom in mechanical systems and engineering structures

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