Online structural health monitoring by model order reduction and deep learning algorithms

Rosafalco, Luca; Torzoni, Matteo; Manzoni, Andrea; Mariani, Stefano; Corigliano, Alberto

doi:10.1016/j.compstruc.2021.106604

Cited by 41 publications

(21 citation statements)

References 41 publications

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“…Such description of damage is consistent with the adopted vibration-based SHM approach, and allows the structure to be modeled as a linear system both in the presence and absence of damage. Moreover, as shown in [9], even if the stiffness reduction takes place over domains of different size from that one adopted during the dataset construction, it is still possible to identify the correct position of damage.…”

Section: Discussionmentioning

confidence: 99%

“…Considering data-driven algorithms, damage localization is often addressed by exploiting a DL feature extractor followed by a classification or a regression module, e.g., as done in [9,10,13]. However, due to the need of training in a simulated environment, the risk of losing generalization capabilities on real monitoring data is high.…”

Section: Discussionmentioning

confidence: 99%

“…The HF numerical model results from the semi-discretized form of the elasto-dynamic problem defined over the FE mesh. On the other hand, in order to ease the construction of a large LF dataset, a projection-based model order reduction strategy for parametrized systems is adopted to build the LF model, see, e.g., [9]. To this aim, the reduced basis method [17] relying on the proper orthogonal decomposition (POD)-Galerkin approach is considered.…”

Section: Datasets Populationmentioning

confidence: 99%

“…reduced-order model in Equation ( 2), i.e., the LF model used to construct D LF , has been built performing a POD upon 40,000 snapshots in time, collected while exploring the parametric input space x LF . 14 POD-bases are selected and stored in matrix W, in place of the original 4659 dofs, after having fixed a suitable tolerance on the energy norm of the reconstruction error (tol POD = 10 −3 ); for further details see, e.g., [9,13].…”

Section: Virtual Experimentsmentioning

confidence: 99%

“…This work proposes an output-only approach to the damage localization problem (see for instance [9,10]), leveraging a synergic combination of multi-fidelity (MF) data-driven meta-modeling and Bayesian parameter identification. The probability distribution of the unknown damage parameters is approximated through a Markov chain Monte Carlo (MCMC) sampling algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate

Torzoni

Manzoni

Mariani

2021

The 1st International Electronic Conference on Algorithms

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To meet the need for reliable real-time monitoring of civil structures, safety control and optimization of maintenance operations, this paper presents a computational method for the stochastic estimation of the degradation of the load bearing structural properties. Exploiting a Bayesian framework, the procedure sequentially updates the posterior probability of the damage parameters used to describe the aforementioned degradation, conditioned on noisy sensors observations, by means of Markov chain Monte Carlo (MCMC) sampling algorithms. To enable the analysis to run in real-time or quasi real-time, the numerical model of the structure is replaced with a data-driven surrogate used to evaluate the (conditional) likelihood function. The proposed surrogate model relies on a multi-fidelity (MF) deep neural network (DNN), mapping the damage and operational parameters onto sensor recordings. The MF-DNN is shown to effectively leverage information between multiple datasets, by learning the correlations across models with different fidelities without any prior assumption, ultimately alleviating the computational burden of the supervised training stage. The low fidelity (LF) responses are approximated by relying on proper orthogonal decomposition for the sake of dimensionality reduction, and a fully connected DNN. The high fidelity signals, that feed the MCMC within the outer-loop optimization, are instead generated by enriching the LF approximations through a deep long short-term memory network. Results relevant to a specific case study demonstrate the capability of the proposed procedure to estimate the distribution of damage parameters, and prove the effectiveness of the MF scheme in outperforming a single-fidelity based method.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Datasets Populationmentioning

confidence: 99%

Section: Virtual Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate

Torzoni

Manzoni

Mariani

2021

The 1st International Electronic Conference on Algorithms

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A Bayesian Framework for Simulation‐based Digital Twins of Bridges

Arcones,

Weiser,

Koutsourelakis

et al. 2023

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Simulation‐based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations of physical systems, digital twins can provide a wealth of information that complements traditional inspection and monitoring data. By incorporating virtual sensors and predictive maintenance strategies, they have the potential to improve our understanding of the behavior and performance of bridges over time. However, as bridges age and undergo regular loading and extreme events, their structural characteristics change, often differing from the predictions of their initial design. Digital twins must be continuously adapted to reflect these changes. In this article, we present a Bayesian framework for updating simulation‐based digital twins in the context of bridges. Our approach integrates information from measurements to account for inaccuracies in the simulation model and quantify uncertainties. Through its implementation and assessment, this work demonstrates the potential for digital twins to provide a reliable and up‐to‐date representation of bridge behavior, helping to inform decision‐making for maintenance and management.

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A Self-adaptive Hybrid Model/data-Driven Approach to SHM Based on Model Order Reduction and Deep Learning

Rosafalco

Torzoni

Manzoni

et al. 2021

Structural Integrity

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Online structural health monitoring by model order reduction and deep learning algorithms

Cited by 41 publications

References 41 publications

Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate

Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate

A Bayesian Framework for Simulation‐based Digital Twins of Bridges

A Self-adaptive Hybrid Model/data-Driven Approach to SHM Based on Model Order Reduction and Deep Learning

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