Data-based structural health monitoring using small training data sets

Balsamo, Luciana; Betti, Raimondo

doi:10.1002/stc.1744

Cited by 33 publications

(20 citation statements)

References 27 publications

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“…Thus, a vibration-based scheme utilising vibration response data of structures is commonly employed to assess the conditions of the structures. 8,9 In the vibration-based scheme, there are two types of approaches. One is a model-based approach, which is applied to system identification of the structures when response outputs (e.g., displacements and accelerations) and inputs of the structures are available.…”

Section: Introductionmentioning

confidence: 99%

Simple piecewise linearisation in time series for time‐domain inversion to estimate physical parameters of nonlinear structures

Enokida

Kajiwara

2020

Struct Control Health Monit

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Summary This study introduces simple piecewise linearisation in time series (SPLiTS), for time‐domain inversion (TDI) to estimate the physical parameters of nonlinear structures. SPLiTS considers a nonlinear structure to be a set of piecewise linearised structures based on each half‐cycle wave in the displacement response data. For the linearisation, SPLiTS intentionally minimises the effect of the central point shift that damaged structures commonly display. After theoretically introducing SPLiTS to the response data of single‐/multi‐degree‐of‐freedom (SDOF/MDOF) structures, parameter estimations are performed for data obtained from numerical simulations and experiments. The efficiency of consideration of the central‐point shift is demonstrated in parameter estimations for numerical simulations where response data are obtained by a nonlinear SDOF structure having a trilinear hysteretic spring. SPLiTS is then applied to experimental data from shaking table tests, in which a specimen, imitating a one‐storey structure, displayed yielding behaviour. TDI with SPLiTS reasonably estimates physical parameters that show increased damping and decreased stiffness in time series at the inelastic range. Next, SPLiTS is applied to experimental data from shaking table tests conducted for a full‐scale, three‐storey structure at E‐Defense in 2013. The damping coefficient and stiffness of each storey are reasonably estimated for the specimen under different amplitudes. For these experimental data, estimated structural energy absorption calculated by damping in the time series corresponds well with the structural damage of the specimens. Thus, structural energy absorption is proven to be a useful index for indicating structural damage.

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Section: Introductionmentioning

confidence: 99%

Simple piecewise linearisation in time series for time‐domain inversion to estimate physical parameters of nonlinear structures

Enokida

Kajiwara

2020

Struct Control Health Monit

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“…Defining effective damage sensitive features is a challenging task because some of those features are application dependent, some are too sensitive to external (unaccountable) disturbances, etc. To account for these uncertainties, some of the most recent works on pattern recognition in SHM has focused on statistical analysis (Worden et al [3,4] and Balsamo and Betti [5]).…”

Section: Introductionmentioning

confidence: 99%

The inner product vector as an output-only cross-correlation-based feature to structural damage assessment

Morgantini¹,

Betti²

2020

J. vibroeng.

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This paper presents a structural damage assessment method that relies on a crosscorrelation-based damage sensitive feature known as Inner Product Vector (IPV). Such a feature is extracted from the dynamic response measured at different locations within a structure. Previous investigations on the use of the IPV in a damage detection framework were conducted through laboratory tests by manipulating the excitation source in an input-output approach. In this paper, a new output-only approach has been developed to extract the Inner Product Vector only from the structural response time histories without knowledge of the input excitation. This is a more realistic scenario in structural health monitoring applications where it is difficult (in some cases, impossible) to monitor the external forces. The new estimation of the IPV is accomplished by taking the cross-correlations between filtered contributions to the overall structural response. It is shown how the elements of the IPV are affected by changes of the structural properties induced by damage and how effective the proposed approach is in the presence of external disturbances. The proposed approach has been proven to be very effective in dealing with dense sensor networks requiring large computational efforts. Numerical and experimental tests have been performed to address the reliability of the proposed damage index as a damage sensitive feature in an output-only framework.

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“…Structural health monitoring (SHM) has become a popular tool for safety and serviceability assessment of in-service civil structural systems such as buildings and bridges. Significant efforts have been made over the last two decades toward the development of sensing technologies, 1,2 data processing techniques, 3,4 computational modeling, and model updating [5][6][7][8] as well as system identification algorithms [9][10][11] for SHM in both lab experiments and field testing. Recently, monitoring of building structures has gained tremendous attention.…”

Section: Introductionmentioning

confidence: 99%

Computational modeling of a unique tower in Kuwait for structural health monitoring: Numerical investigations

Sun

Al-Qazweeni

Parol

et al. 2019

Struct Control Health Monit

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Summary Computational modeling, in addition to data analytics, plays an important role in structural health monitoring (SHM). The high‐fidelity computational model based on the design and construction information provide important dynamics information of the structure and, more importantly, can be updated against field measurements for SHM purposes such as damage detection, response prediction, and reliability assessment. In this paper, we present a unique skyscraper (Al‐Hamra Tower) located in Kuwait City and its high‐fidelity computational model using ETABS for structural health monitoring applications. The tower is made of cast‐in‐place reinforced concrete with a core of shear walls and two curved shear walls running the height of the building (approximately 413 m with 86 floors in total). Interesting static and dynamic characteristics of the tower are described. System identification, interferometry‐based wave propagation analysis, and wave‐based damage detection are performed using synthetic data. This work mainly presents the phase of numerical investigations, which serves as a basis for correlating the field monitoring data to the model of the building in future work.

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Data-based structural health monitoring using small training data sets

Cited by 33 publications

References 27 publications

Simple piecewise linearisation in time series for time‐domain inversion to estimate physical parameters of nonlinear structures

Simple piecewise linearisation in time series for time‐domain inversion to estimate physical parameters of nonlinear structures

The inner product vector as an output-only cross-correlation-based feature to structural damage assessment

Computational modeling of a unique tower in Kuwait for structural health monitoring: Numerical investigations

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