Performance-Based Design of Structural Health Monitoring Systems

Tonelli, Daniel; Cappello, Carlo; Zonta, Daniele

doi:10.1007/978-3-030-64908-1_22

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…In addition, 82 PT100 platinum resistance thermometers have been installed in the viaduct as well, and the temperature field measurement is used to compensate the displacement data provided by the topographic network. 28,29 Therefore, the displacements used in the analysis to update the structural model are already thermally compensated and can be compared with the model output.…”

Section: Monitoring Systemmentioning

confidence: 99%

Reliability analysis of complex structures based on Bayesian inference

Tonelli

Beltempo

Cappello

et al. 2023

Structural Health Monitoring

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This paper outlines a logical process based on Bayesian inference to exploit monitoring observations to improve the accuracy in the reliability estimates of civil structures that are retrofitted during their service lives. The proposed approach is particularly effective for complex structures, whose response prediction is affected by structural parameters with significant uncertainty; typical examples are prestressed concrete bridges, heavily influenced by the inherent uncertainties of the material’s creep and shrinkage. The methodology is applied to a real-life complex bridge in Italy, as opposed to academic problems and laboratory tests; the structural model developed for this study includes the original creep law of the Model B3 proposed by Bazant rather than linear approximation or response surfaces. The Bayesian inference is performed using a Markov Chain Monte Carlo (MCMC) method, while the reliability assessment utilises importance sampling (IS) for computational efficiency. The results show the extent to which structural health monitoring (SHM) and Bayesian inference reduce the uncertainty in structural parameters and the prediction of the structural demand. At the same time, they increase structural reliability. Particular attention is given to computational effort, structural modelling and algorithm optimisation.

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Section: Monitoring Systemmentioning

confidence: 99%

Reliability analysis of complex structures based on Bayesian inference

Tonelli

Beltempo

Cappello

et al. 2023

Structural Health Monitoring

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“…Finally, based on the previous aspects, the monitoring system designer must select the proper sensing technology, with its ensuing performance in terms of measurement accuracy, sampling frequency (or rate), transmission rate, maintainability, measurement range, possibility of integration into a complex monitoring system, etc. [70].…”

Section: How Digital Twins?mentioning

confidence: 99%

Digital Twin for Civil Engineering Systems: An Exploratory Review for Distributed Sensing Updating

Bado

Tonelli

Poli

et al. 2022

Sensors

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We live in an environment of ever-growing demand for transport networks, which also have ageing infrastructure. However, it is not feasible to replace all the infrastructural assets that have surpassed their service lives. The commonly established alternative is increasing their durability by means of Structural Health Monitoring (SHM)-based maintenance and serviceability. Amongst the multitude of approaches to SHM, the Digital Twin model is gaining increasing attention. This model is a digital reconstruction (the Digital Twin) of a real-life asset (the Physical Twin) that, in contrast to other digital models, is frequently and automatically updated using data sampled by a sensor network deployed on the latter. This tool can provide infrastructure managers with functionalities to monitor and optimize their asset stock and to make informed and data-based decisions, in the context of day-to-day operative conditions and after extreme events. These data not only include sensor data, but also include regularly revalidated structural reliability indices formulated on the grounds of the frequently updated Digital Twin model. The technology can be even pushed as far as performing structural behavioral predictions and automatically compensating for them. The present exploratory review covers the key Digital Twin aspects—its usefulness, modus operandi, application, etc.—and proves the suitability of Distributed Sensing as its network sensor component.

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“…In order to quantify the difference between the expected and real uncertainty of a model parameter, and to validate the proposed approach, we use the concept of monitoring system effectiveness [3] in the estimation of the model parameter m. The monitoring effectiveness, η, expresses the extent to which the monitoring observations improve the knowledge of the structure behaviour; in other words, the extent to which the parameter uncertainty a posteriori is lower than a priori. We define the expected effectiveness of the monitoring system, η exp , as the ratio between the prior uncertainty σ m and the pre-posterior uncertainty σ m,pp .…”

Section: Expected Vs Real Uncertaintymentioning

confidence: 99%

“…When monitoring a civil structure, the purpose is to identify a number of key parameters-including modal frequencies, long-term strain trends and damage indicators [1,2]-which are representative of its health state. The monitoring is successful if these parameters can be identified with an appropriate level of confidence, keeping their uncertainty below an acceptable target value [3]. Acceptable uncertainty normally depends on the nature of the physical problem that is being investigated and could be required by the user of the monitoring information within the management framework of the infrastructure [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…However, monitoring observations are not yet available when designing a monitoring system; therefore, in order to understand whether a monitoring strategy (the choice of sensors, sensor placement and acquisition protocols) is suitable, it is necessary to estimate an expected uncertainty of the key parameters beforehand. This expected Infrastructures 2022, 7, 5 2 of 24 uncertainty is indeed an a priori estimation (i.e., before acquiring any data) of posterior uncertainty: it is sometimes referred to, in short, as pre-posterior uncertainty, and the estimation process of this value is known as pre-posterior analysis [3].…”

Section: Introductionmentioning

confidence: 99%

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Designing a Structural Health Monitoring System Accounting for Temperature Compensation

et al. 2021

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Structural health monitoring is effective if it allows us to identify the condition state of a structure with an appropriate level of confidence. The estimation of the uncertainty of the condition state is relatively straightforward a posteriori, i.e., when monitoring data are available. However, monitoring observations are not available when designing a monitoring system; therefore, the expected uncertainty must be estimated beforehand. This paper proposes a framework to evaluate the effectiveness of a monitoring system accounting for temperature compensation. This method is applied to the design process of a structural health monitoring system for civil infrastructure. In particular, the focus is on the condition-state parameters representing the structural long-term response trend, e.g., due to creep and shrinkage effects, and the tension losses in prestressed concrete bridges. The result is a simple-to-use equation that estimates the expected uncertainty of a long-term response trend of temperature-compensated response measurements in the design phase. The equation shows that the condition-state uncertainty is affected by the measurement and model uncertainties, the start date and duration of the monitoring activity, and the sampling frequency. We validated our approach on a real-life case study: the Colle Isarco viaduct. We verified whether the pre-posterior estimation of expected uncertainty, performed with the experimented approach, is consistent with the real uncertainty estimated a posteriori based on the monitoring data.

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Performance-Based Design of Structural Health Monitoring Systems

Cited by 9 publications

References 12 publications

Reliability analysis of complex structures based on Bayesian inference

Reliability analysis of complex structures based on Bayesian inference

Digital Twin for Civil Engineering Systems: An Exploratory Review for Distributed Sensing Updating

Designing a Structural Health Monitoring System Accounting for Temperature Compensation

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