Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements

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.ISSN:1424-822

Optimal multi-type sensor placement for structural identification by static-load testing

Assessing ageing infrastructure is a critical challenge for civil engineers due to the difficulty in the estimation and integration of uncertainties in structural models. Field measurements are increasingly used to improve knowledge of the real behavior of a structure; this activity is called structural identification. Error-domain model falsification (EDMF) is an easy-to-use model-based structural-identification methodology which robustly accommodates systematic uncertainties originating from sources such as boundary conditions, numerical modelling and model fidelity, as well as aleatory uncertainties from sources such as measurement error and material parameter-value estimations. In most practical applications of structural identification, sensors are placed using engineering judgment and experience. However, since sensor placement is fundamental to the success of structural identification, a more rational and systematic method is justified. This study presents a measurement system design methodology to identify the best sensor locations and sensor types using information from static-load tests. More specifically, three static-load tests were studied for the sensor system design using three types of sensors for a performance evaluation of a full-scale bridge in Singapore. Several sensor placement strategies are compared using joint entropy as an information-gain metric. A modified version of the hierarchical algorithm for sensor placement is proposed to take into account mutual information between load tests. It is shown that a carefully-configured measurement strategy that includes multiple sensor types and several load tests maximizes information gain

Measurement system design for civil infrastructure using expected utility

For system identification, most sensor-placement strategies are based on the minimization of the model-parameter uncertainty. However, reducing the uncertainty in remaining-life prognosis of structures is often more relevant. This paper proposes an optimization strategy using utility theory and probabilistic behavior prognoses based on model falsification to support decisions related to monitoring interventions. This approach, illustrated by the full-scale case study of a bridge, allows quantification of the expected utility of measurement systems while also indicating the profitability of monitoring actions. In addition, this approach is able to determine when the expected performance of monitoring configurations is reduced due to over-instrumentation. The use of model falsification for system identification allows for explicit inclusion of engineering heuristics in this knowledge intensive task while also offering robustness to effects of systematic modeling errors that are associated with idealization of complex civil structures

Model-Form Uncertainty Quantification in Prognosis and Fleet Management with Physics-Informed Neural Networks

Prognosis and health management play an important role in the control of costs associated with the operation of large industrial equipment. By properly comprehending hardware degradation and accurately predicting the remaining useful life of such equipment, we can significantly lower operational costs by reducing asset downtime and maintenance interventions. However, complex interactions between operational conditions and component capability make accurately modeling damage accumulation for large fleets a daunting task. Unforeseen factors such as aggressive missions introduced by operators, exposure to harsh environments, manufacturing issues, amongst many others, can lead to large discrepancies between predicted and observed useful life. Motivated by the growing availability of data and computational power as well as the advances in hybrid modeling frameworks, capable of merging elements of physics, machine learning, and statistical learning, in this dissertation, we focus on the development of novel approaches to minimize the impact of unforeseen factors in fleet management. In this dissertation, we focus on the challenges of accounting for the impacts of such unforeseen factors on two specific stages of a component service life; early-file and end-life. Two numerical case studies are derived to emulate two common issues in fleet life management; manufacturing issues leading to an infant mortality problem, and unexpected exposure to harsher environments by operators, accelerating wear-out and significantly reducing component\u27s useful life. In the first analysis, two key aspects in a prognosis and health management perspective are addressed; detecting the emerging issue (i.e., the infant mortality problem), and the evaluation of risk mitigation procedures to minimize/mitigate its effects on the overall fleet reliability. Bayesian networks implementing physics-based models are used to model the fleet unreliability and assist in the quantification of the infant mortality impact on the fleet useful life. Additionally, steps to adapted the derived Bayesian networks to assist in the evaluation of possible mitigation approaches to minimize the impacts of fleet-wide early life problems are presented. Concerning the wear-out analysis, a civil aviation case study is derived, in which an aircraft fleet mainly operates in coastal routes, significantly increasing its exposure to saline corrosion. These conditions lead to accelerated degradation of the aircraft wing panels due to the combined effects of corrosion and mechanical fatigue. Such corrosive conditions are not accounted for by the fleet prognosis model generating a significant epistemic uncertainty (i.e., a missing physics issue). To address this issue, we proposed hybrid recurrent neural network modules to compensate for the model-form uncertainty. In the formulated neural network cell, well-understood aspects of the degradation mechanism are addressed by a physics-based model, while data-driven models are trained to account for the missing physics effects. After proper training, the hybrid neural network can compensate for the unaccounted effects in the model damage forecast and generates accurate predictions to assist in the fleet prognosis analysis. Obtained results illustrate the capabilities of the proposed frameworks in compensating for the considered unforeseen factors impacts in fleet management. Additionally, the obtained results have prominently shown the significance and importance of properly account for such factors on fleet prognosis and how these factors can drastically hinder engineers\u27 ability to properly perform prognosis and health management analysis