Multimodel Structural Performance Monitoring

Goulet, James A.; Kripakaran, Prakash; Smith, Ian F. C.

doi:10.1061/(asce)st.1943-541x.0000232

Cited by 88 publications

(67 citation statements)

References 33 publications

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“…Model simplification uncertainty evaluation is based on previous studies [17,19] that estimated the prediction uncertainty related to degrees of freedom (DOF's) (displacements and rotations) to be between 0% and 7% of the averaged predicted value.…”

Section: Uncertaintiesmentioning

confidence: 99%

“…Error-domain model falsification is built upon more than a decade of research [37,39,43]. In a previous study [17], a similar identification approach was used to perform structural identification on bridges based on static measurements. Error-domain identification was proposed to overcome the limitations of other approaches based on statistical inference that relies on the correct evaluation of uncertainties and of their dependencies [5,44].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid probabilities and error-domain structural identification using ambient vibration monitoring

Goulet

Michel²,

Smith

2013

Mechanical Systems and Signal Processing

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For the assessment of structural behavior, many approaches are available to compare model predictions with measurements. However, few approaches include uncertainties along with dependencies associated with models and observations. In this paper, an error-domain structural identification approach is proposed using ambient vibration monitoring (AVM) as the input. This approach is based on the principle that in science, data cannot truly validate an hypothesis, it can only be used to falsity it. Error-domain model falsification generates a space of possible model instances (combination of parameters), obtains predictions for each of them and then, rejects instances that have unlikely differences (residuals) between predictions and measurements. Models are filtered in a two step process. Firstly a comparison of mode shapes based on MAC criterion ensures that the same modes are compared. Secondly, the frequencies from each model instance are compared with the measurements. The instances for which the difference between the predicted and measured value lie outside threshold bounds are discarded. In order to include "uncertainty of uncertainty" in the identification process, a hybrid probability scheme is also presented. The approach is used for the identification of the Langensand Bridge in Switzerland. It is used to falsify the hypothesis that the bridge was behaving as designed when subjected to ambient vibration inputs, before opening to the traffic. Such small amplitudes may be affected by lowlevel bearing-device friction. This inadvertently increased the apparent stiffness of the structure by 17%. This observation supports the premiss that ambient vibration surveys

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Section: Uncertaintiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid probabilities and error-domain structural identification using ambient vibration monitoring

Goulet

Michel²,

Smith

2013

Mechanical Systems and Signal Processing

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“…Model-based methods rely on behavioural models (e.g., finite element (FE) models) that are derived from the fundamental physics of the structure. Their application to interpreting measurements from continuous structural health monitoring is limited due to their larger computational requirements and the difficulty in generating reliable behaviour models that are representative of the structure [10,13]. In contrast to model-based methods, datadriven methods operate directly on measurements and require minimal structural information.…”

Section: Introductionmentioning

confidence: 99%

Predicting thermal response of bridges using regression models derived from measurement histories

Kromanis

Kripakaran

2014

Computers & Structures

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102

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This study investigates the application of novel computational techniques for structural performance monitoring of bridges that enable quantification of temperature-induced response during the measurement interpretation process. The goal is to support evaluation of bridge response to diurnal and seasonal changes in environmental conditions, which have widely been cited to produce significantly large deformations that exceed even the effects of live loads and damage. This paper proposes a regression-based methodology to generate numerical models, which capture the relationships between temperature distributions and structural response, from distributed measurements collected during a reference period. It compares the performance of various regression algorithms such as multiple linear regression (MLR), robust regression (RR) and support vector regression (SVR) for application within the proposed methodology. The methodology is successfully validated on measurements collected from two structures -a laboratory truss and a concrete footbridge. Results show that the methodology is capable of accurately predicting thermal response and can therefore help with interpreting measurements from continuous bridge monitoring.

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“…St-Id approaches based on static measurements are also increasingly finding acceptance among SHM researchers. In particular, displacement and strain measurements collected during load tests have been successfully employed to update numerical models [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…Their generation can also be resource and time-intensive. Furthermore, computed models may not replicate behaviour of real-life structures due to uncertainties and approximations in the modelling process [8]. For example, while the elastic modulus of concrete generally varies with temperature, humidity and age [9], these variations are seldom taken into account in a PB model and in turn, affect the reliability of the St-Id process.…”

Section: Introductionmentioning

confidence: 99%

SHM of bridges: characterising thermal response and detecting anomaly events using a temperature-based measurement interpretation approach

Kromanis

Kripakaran

2016

J Civil Struct Health Monit

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Abstract:A major bottleneck preventing widespread use of Structural Health Monitoring (SHM) systems for bridges is the difficulty in making sense of the collected data. Characterising environmental effects in measured bridge behaviour, and in particular the influence of temperature variations, remains a significant challenge. This paper proposes a novel data-driven approach referred to as Temperature-Based Measurement Interpretation (TB-MI) approach to solve this challenge. The approach is composed of two key steps -(i) characterisation of thermal effects in bridges using a methodology referred to as Regression-Based Thermal Response Prediction (RBTRP) methodology, and (ii) detection of anomaly events by analysing differences between measured and predicted structural behaviour. Measurements from a laboratory truss structure that is setup to simulate a range of structural scenarios are employed to evaluate the performance of the TB-MI approach. The study examines how the predictive capability of the RBTRP methodology is influenced by dimensionality reduction and measurement down-sampling, which are common pre-processing techniques used to deal with high spatial and temporal density in measurements. It also proposes a novel anomaly detection technique referred to as signal subtraction method that detects anomaly events from timeseries of prediction errors, which are computed as the difference between in-situ measurements and predictions obtained using the RBTRP methodology. Results demonstrate that the TB-MI approach has potential for integration within data interpretation frameworks of SHM systems of full-scale bridges. Keywords: structural health monitoring; thermal effects; data-driven methods; anomaly detection; measurement interpretation. Rolands AcknowledgmentsThe authors would like to express their gratitude to Bill Harvey Associates and Pembrokshire County Council for providing access to the measurements of the Cleddau Bridge, and to Elena Barton (National Physical Laboratory) for providing the data from the National Physical Laboratory Footbridge project.

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Multimodel Structural Performance Monitoring

Cited by 88 publications

References 33 publications

Hybrid probabilities and error-domain structural identification using ambient vibration monitoring

Hybrid probabilities and error-domain structural identification using ambient vibration monitoring

Predicting thermal response of bridges using regression models derived from measurement histories

SHM of bridges: characterising thermal response and detecting anomaly events using a temperature-based measurement interpretation approach

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