Simulation-Based Classification; a Model-Order-Reduction Approach for Structural Health Monitoring

Taddei, Tommaso; Penn, James Douglass; Yano, Masayuki; Patera, Anthony T.

doi:10.1007/s11831-016-9185-0

Cited by 35 publications

(19 citation statements)

References 66 publications

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“…In this paper, we have investigated a new strategy for real-time structural health monitoring, treating damage detection and localization as classification tasks [3], and framing the proposed procedure in the family of SBC approaches [4]. We have proposed to employ fully convolutional networks to analyse time series coming from a set of sensors.…”

Section: Discussionmentioning

confidence: 99%

“…SHM applications are often treated as classification problems [3] aiming (i) to distinguish the damage state of a structure from the undamaged state, starting from a set of available recordings of a monitoring sensor system, and (ii) to locate and quantify the current damage. In this framework, we have adopted the so-called simulation-based classification (SBC) approach [4], and we have exploited deep learning (DL) techniques for the sake of automatic classification. In our procedure, data are displacement and/or acceleration recordings of the structural response, and the classification task consists of recognizing which structural state, among a discrete set, could have most probably produced them.…”

Section: Introductionmentioning

confidence: 99%

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Fully convolutional networks for structural health monitoring through multivariate time series classification

Rosafalco

Manzoni

Mariani

et al. 2020

Adv. Model. and Simul. in Eng. Sci.

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We propose a novel approach to structural health monitoring (SHM), aiming at the automatic identification of damage-sensitive features from data acquired through pervasive sensor systems. Damage detection and localization are formulated as classification problems, and tackled through fully convolutional networks (FCNs). A supervised training of the proposed network architecture is performed on data extracted from numerical simulations of a physics-based model (playing the role of digital twin of the structure to be monitored) accounting for different damage scenarios. By relying on this simplified model of the structure, several load conditions are considered during the training phase of the FCN, whose architecture has been designed to deal with time series of different length. The training of the neural network is done before the monitoring system starts operating, thus enabling a real time damage classification. The numerical performances of the proposed strategy are assessed on a numerical benchmark case consisting of an eight-story shear building subjected to two load types, one of which modeling random vibrations due to low-energy seismicity. Measurement noise has been added to the responses of the structure to mimic the outputs of a real monitoring system. Extremely good classification capacities are shown: among the nine possible alternatives (represented by the healthy state and by a damage at any floor), damage is correctly classified in up to $$95 \%$$ 95 % of cases, thus showing the strong potential of the proposed approach in view of the application to real-life cases.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fully convolutional networks for structural health monitoring through multivariate time series classification

Rosafalco

Manzoni

Mariani

et al. 2020

Adv. Model. and Simul. in Eng. Sci.

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“…It has been used for several applications. For instance, [38] applies the PBDW for structural health monitoring; [19] proposes a non-intrusive PBDW with application to urban dispersion modeling frameworks; and [1,2] exploit the generalized empirical interpolation method [27,28,29] in a data interpolation perspective. As a further step towards efficient industrial implementation, [31] develops a PBDW approach based on noisy observations and [32] introduces an adaptive PBDW approach with a user-defined update space.…”

Section: Introductionmentioning

confidence: 99%

Reducing sensors for transient heat transfer problems by means of variational data assimilation

Benaceur¹

2021

The SMAI Journal of Computational Mathematics

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We propose a contribution that combines model reduction with data assimilation. A dedicated Parametrized Background Data-Weak (PBDW) approach has been introduced in the literature so as to combine numerical models with experimental measurements. We extend the approach to a time-dependent framework by means of a POD-greedy reduced basis construction. Since the construction of the basis is performed offline, the algorithm addresses the time dependence of the problem while the time stepping scheme remains unchanged. Moreover, we devise a new algorithm that exploits offline state estimates in order to diminish both the dimension of the online PBDW statement and the number of required sensors collecting data. The idea is to exploit in situ observations in order to update the best-knowledge model, thereby improving the approximation capacity of the background space.

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“…Parts of them develop the data‐driven methods merging of the probability, mechanics, and signal processing to detect the building and infrastructure damage 5–11 . The others propose using the time‐varying characteristics of the short/long term data and their mathematical models to study the behavior and performance of structures under in‐service loads 12–17 . However, the mining of massive data has always been a difficult problem to solve.…”

Section: Introductionmentioning

confidence: 99%

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

Zhao

Wei

et al. 2020

Struct Control Health Monit

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Nonlinear mapping of the fuzzy relation exists between structural inputs and outputs, as well as between structural global and local response. It is difficult for the numerical simulation to introduce the nonlinear effects of the variability of loading effects and the uneven deterioration of structure. The big monitoring data make it feasible to mine these nonlinear effects, and the network of deep learning is a good tool to establish the nonlinear mapping model between the multi-source monitoring data. Based on the temperature, strain, and dynamic displacement data from the structural health monitoring (SHM) system of an in-service bridge, the deep learning regression network of long short-term memory (LSTM) is designed and trained, which is used for modeling the nonlinear mapping between the structural input-output and the structural global-local response. The digital model of the nonlinear mapping of the temperature to temperature-induced strain, dynamic displacement to vehicleinduced strain, and vehicle-induced strain to dynamic displacement is established by the deep learning of LSTM regression network. The established nonlinear mapping model can be used to recover the abnormal and missing data with multi-source data, and it digitally linked the structural input-output and the structural global-local response. Then, the utilization of SHM data analytics will be enhanced, and the digital modeling for the in-service state of the bridge structure will be fast implemented.

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Simulation-Based Classification; a Model-Order-Reduction Approach for Structural Health Monitoring

Cited by 35 publications

References 66 publications

Fully convolutional networks for structural health monitoring through multivariate time series classification

Fully convolutional networks for structural health monitoring through multivariate time series classification

Reducing sensors for transient heat transfer problems by means of variational data assimilation

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

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