Population-based structural identification for reserve-capacity assessment of existing bridges

Proverbio, Marco; Vernay, D.; Smith, Ian F. C.

doi:10.1007/s13349-018-0283-6

Cited by 18 publications

(27 citation statements)

References 38 publications

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“…The serviceability limit state (SLS) of stress control is investigated for the Exeter Bascule bridge by checking that under characteristic design loads, the maximum Von Mises stress in each element is lower than the yield strength (

). A detailed description of the procedure for the reserve-capacity assessment is available in Reference [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements

Proverbio

Bertola

Smith

2018

Sensors

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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.

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). A detailed description of the procedure for the reserve-capacity assessment is available in Reference [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements

Proverbio

Bertola

Smith

2018

Sensors

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“…This confidence level is a tradeoff between accepting incorrect models and falsifying the Sensors-29460-2019 3 correct model. This confidence level is typically fixed at 95% [3], [8], similarly to characteristic values for material properties in civil engineering. If the residual exceeds thresholds at one location, the model instance is falsified.…”

Section: A Model-updating Methodologymentioning

confidence: 99%

“…As critical limit states may involve plastic collapse, data interpretation and prediction are non-trivial. Monitoring has the potential to reveal this untapped reserve capacity, leading to important economies in asset management and smarter civil infrastructure [3]. Field measurements associated with robust data-interpretation methodologies contribute by improving the accuracy of behavior predictions, and this task is called structural identification.…”

Section: Introductionmentioning

confidence: 99%

Strategy to Validate Sensor-Placement Methodologies in the Context of Sparse Measurement in Complex Urban Systems

2020

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The Internet of Things creates opportunities to develop data-driven design methodologies for smart cities. However, effects rather than causes are often measured in complex urban systems, requiring robust data-interpretation methodologies. Additionally, effective monitoring of large urban components, such as civil infrastructure, often involves multiple sensor devices and invasive sensor systems. In these situations, the design of measurement systems is an important task. Usually, this task is carried out by engineers using only qualitative rules of thumb and experience. Recently, researchers have developed quantitative sensor-placement methodologies to maximize the information gain of measurement systems. Nonetheless, these methodologies are only weakly validated using field measurements due to the small amount of data collected and the difficulties comparing the predicted information gain with observations. This paper proposes a validation strategy for sensor-placement methodologies. In this strategy, predictions of both individual sensor and sensor-configuration performances are compared with observations using statistical tests and hypothesis testing. The validation procedure is illustrated through three full-scale-bridge case studies. This strategy helps engineers select an appropriate methodology to design measurement systems in order to optimize data collection using sensors.

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“…Moreover, the four estimations are close (less than 4% different) from the average reserve capacity computed using the entire CMS. A detailed explanation of reserve-capacity assessments for this case study is available in (Proverbio et al 2018b).…”

Section: Decision Makingmentioning

confidence: 99%

“…Models for which residuals are within these bounds at each sensor location are included in the candidate model set (CMS). When candidate models are identified, they are then employed to perform predictions, for example, at unmeasured locations (Pasquier and Smith 2015) and those predictions may be used to assess the reserve capacity of the structure (Pasquier et al 2014;Proverbio et al 2018b). Furthermore, reserve capacity estimations form the basis for well-engineered interventions, such as retrofitting for capacity improvement.…”

Section: Introductionmentioning

confidence: 99%

Sensor Data Interpretation with Clustering for Interactive Asset-Management of Urban Systems

Proverbio

Costa²,

Smith

2018

J. Comput. Civ. Eng.

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In responsive cities, user feedback and information provided by sensors are combined to improve urban design and to support asset managers in performing decision making. Optimal management of infrastructure networks requires accurate knowledge of current asset conditions, in order to avoid unnecessary replacement and expensive interventions when cheaper and more sustainable alternatives are available. Structural model updating is a discipline that focuses on improving behaviour-model accuracy by means of measurements taken from the built environment. Error-domain model falsification (EDMF) is a simple and practice-oriented methodology that employs measurements at sensor locations to identify plausible models among an initial population that is generated according to engineering judgment. However, many plausible models are often identified, making result interpretations difficult for practising engineers. In this paper, a clustering methodology based on bipartite-modularity optimisation (BMO) is employed to clarify identification outputs. Compared with classical clustering methods such as K-means, BMO clustering provides more accurate interpretations and better visualization of the results. Moreover, engineers can actively interact with the clustering framework to obtain the knowledge that is needed at several stages of the decision-making process.

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Population-based structural identification for reserve-capacity assessment of existing bridges

Cited by 18 publications

References 38 publications

Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements

Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements

Strategy to Validate Sensor-Placement Methodologies in the Context of Sparse Measurement in Complex Urban Systems

Sensor Data Interpretation with Clustering for Interactive Asset-Management of Urban Systems

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