Automatic identification of dense damage-sensitive features in civil infrastructure using sparse sensor networks

Quqa, Said; Landi, Luca; Diotallevi, Pier Paolo

doi:10.1016/j.autcon.2021.103740

Cited by 29 publications

(11 citation statements)

References 30 publications

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“…The focus of this paper is on vibration-based SHM, which exploits the global dynamic response of structures as a means of damage detection (Quqa et al, 2021). Natural frequencies are used as a damagesensitive feature in the present case due to the ease associated with measuring this parameter and the simplicity of its meaning.…”

Section: Bayesian Updating Of the Probability Of Damage Statesmentioning

confidence: 99%

Quantifying the value of SHM information for bridges under flood-induced scour

Giordano

Prendergast

Limongelli

2022

Structure and Infrastructure Engineering

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Bridge scour is a leading cause of failure for bridges over waterways and is notoriously difficult to detect with accuracy. Dynamic Structural Health Monitoring (SHM) of bridges for scour has gained traction in recent years as monitoring systems have improved and the reliability of measurements increased. Due to the large number of bridges on typical networks and the limited financial resources within asset management agencies, decision-makers must prioritize certain structures when it comes to management in the event of flooding. The decision to install a dynamic SHM system on a bridge must be balanced by the financial benefit of doing so, as limited resources often need to be carefully rationed.In this paper, a methodology is proposed to evaluate such benefit based on the Value of Information (VoI) from Bayesian decision analysis. A case study is presented whereby a dynamic SHM system is considered to be installed on a typical bridge with the aim to support emergency management operations during flooding. The proposed methodology allows computation of the financial benefit of installing a dynamic SHM system over a certain reference period, thus accounting for multiple flood events and scenarios. The various elements of the procedure are discussed in detail.

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Section: Bayesian Updating Of the Probability Of Damage Statesmentioning

confidence: 99%

Quantifying the value of SHM information for bridges under flood-induced scour

Giordano

Prendergast

Limongelli

2022

Structure and Infrastructure Engineering

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“…[1][2][3] SHM methods generally consist of processing structural parameters to extract a damage-sensitive feature alerting about the occurrence of structural anomalies. [4][5][6] Among structural parameters, displacements are widely exploited in SHM applications on existing structures. 7 In the literature, methods to measure structural displacements are generally classified into contact and noncontact techniques.…”

Section: Introductionmentioning

confidence: 99%

Damage detection on a historic iron bridge using satellite DInSAR data

Giordano

Turksezer

Previtali

et al. 2022

Structural Health Monitoring

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Structural Health Monitoring (SHM) allows tracking the structural behavior in time and support decisions regarding, for instance, the need for maintenance and repair activities. Most traditional SHM systems require sensors that are directly applied to the structure to get insights into the structural performance. Satellite technologies can provide an appealing alternative to traditional SHM. They allow to measure displacements at a large scale and to follow their evolution without the need of directly accessing the structure. Further to this, the possibility to monitor large areas opens new avenues for the development of automatic alert systems able to issue an alarm and early-flag damaged structures. However, displacements of civil structures might also be induced by sources other than damage such as thermal or periodic hydrogeological variations. These can hinder the onset and development of damage or lead to false alarms if such displacements are erroneously interpreted as damages. This paper aims to present a new method for damage detection based on DInSAR measurements, that tackles both aspects providing reliable information about the onset of damage under environmentally changing conditions in a period corresponding to about twice the revisit time of the satellite. A case study is presented to demonstrate the applicability of the proposed method, namely the Palatino bridge in Rome, Italy. The satellite data are acquired by COSMO-SkyMed of the Italian Space Agency and consist of displacements of the observed structure recorded during a period spanning between 2011 and 2019.

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“…The missing data significantly hinder the development of data-driven models for structural safety monitoring and decreases the capability of SHM system to recognize anomalies. 3–5…”

Section: Introductionmentioning

confidence: 99%

“…The missing data significantly hinder the development of data-driven models for structural safety monitoring and decreases the capability of SHM system to recognize anomalies. [3][4][5] Complete data are critical for many methods in SHM systems and further applications. 6 In the field of modal identification and damage detection, many effective methods, such as spectrum kurtosis, local mean decomposition, empirical mode decomposition (EMD), intrinsic time scale decomposition, variational mode 1 decomposition, and singular spectrum decomposition, have obtained good results in recognizing different types of operation conditions which need complete dataset as a support.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous structural responses recovery based on multi-modal deep learning

Sun

et al. 2022

Structural Health Monitoring

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For structural health monitoring, a complete dataset is important for further analysis such as modal identification and risk early warning. Unfortunately, the missing data normally exist in current database due to sensor failures, transmission system interruption, and hardware malfunctions. Currently, most of the studies just deleted the dataset containing missing data or using mean values as imputation which could wrongly reflect the characteristics changes of the structure. The present study therefore develops a heterogeneous structural response recovery method based on multi-modal fusion auto-encoder which can consider temporal correlations and spatial correlations and correlations between heterogeneous structural responses simultaneously. Moreover, a parallel optimization method is proposed to optimize the parameters of the deep fusion networks. A dataset containing about 3 months and two input attributes is collected from a bridge and utilized for training and testing the proposed method and some benchmark methods. Statistical scores including root mean square error (RSME), mean absolute error (MAE), and mean relative error (MRE) are applied to evaluate the performance of the implemented models, respectively. Results show that the proposed method achieve the best imputation performance under different missing scenarios. Furthermore, the proposed method can achieve better performance when the missing rate is high. The results suggest that the consideration between heterogeneous structural responses is critical for missing data recovery.

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Automatic identification of dense damage-sensitive features in civil infrastructure using sparse sensor networks

Cited by 29 publications

References 30 publications

Quantifying the value of SHM information for bridges under flood-induced scour

Quantifying the value of SHM information for bridges under flood-induced scour

Damage detection on a historic iron bridge using satellite DInSAR data

Heterogeneous structural responses recovery based on multi-modal deep learning

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