Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements.

Marco Proverbio,Numa Bertola,Ian Smith

doi:10.3390/s18061702

Marco Proverbio, Numa Bertola + Show 1 more

Open Access

https://doi.org/10.3390/s18061702

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Abstract

The aim of structural identification is to provide accurate knowledge of the behaviour of existing structures. In most situations, finite-element models are updated using behaviour measurements and field observations. Error-domain model falsification (EDMF) is a multi-model approach that compares finite-element model predictions with sensor measurements while taking into account epistemic and stochastic uncertainties—including the systematic bias that is inherent in the assumptions behind structural models. Compared with alternative model-updating strategies such as residual minimization and traditional Bayesian methodologies, EDMF is easy-to-use for practising engineers and does not require precise knowledge of values for uncertainty correlations. However, wrong parameter identification and flawed extrapolation may result when undetected outliers occur in the dataset. Moreover, when datasets consist of a limited number of static measurements rather than continuous monitoring data, the existing signal-processing and statistics-based algorithms provide little support for outlier detection. This paper introduces a new model-population methodology for outlier detection that is based on the expected performance of the as-designed sensor network. Thus, suspicious measurements are identified even when few measurements, collected with a range of sensors, are available. The structural identification of a full-scale bridge in Exeter (UK) is used to demonstrate the applicability of the proposed methodology and to compare its performance with existing algorithms. The results show that outliers, capable of compromising EDMF accuracy, are detected. Moreover, a metric that separates the impact of powerful sensors from the effects of measurement outliers have been included in the framework. Finally, the impact of outlier occurrence on parameter identification and model extrapolation (for example, reserve capacity assessment) is evaluated.

Highlights

Sensing in the built environment has shown the potential to improve asset management by revealing intrinsic resources that can be exploited to extend the service life of infrastructure [1].sensors on infrastructure often provide indirect information since effects, rather than causes, are measured
Sensors 2018, 18, 1702 required when measurements are used to improve the accuracy of model predictions, especially when the same models have been used for design
This paper presents a new outlier-detection framework that is compatible with population approaches such as Error-domain model falsification (EDMF)

Summary

Introduction

Sensing in the built environment has shown the potential to improve asset management by revealing intrinsic resources that can be exploited to extend the service life of infrastructure [1]. Sensors on infrastructure often provide indirect information since effects, rather than causes, are measured. Physics-based models are necessary to convert this information into useful knowledge of as-built structure behaviour. Civil-engineering models involve uncertainties and systematic biases due to their conservative, rather than precise, objectives. Sensors 2018, 18, 1702 required when measurements are used to improve the accuracy of model predictions, especially when the same models have been used for design. In the field of structural identification, measurements are employed to update parameter values affecting structure behaviour. Known as model-calibration, consists of adjusting model parameters to minimize the difference between predicted and measured values

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