Introduction to sensing for structural performance assessment and health monitoring

Wang, M.L.; Lynch, Jerome P.; Sohn, Hoon

doi:10.1533/9780857099136.1

Cited by 21 publications

(20 citation statements)

References 17 publications

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“…The strategy analysed in the present paper is composed of two stages, as depicted in Figure 2. A preliminary study is first performed to identify: (1) the endogenous factors, namely the sources of vulnerability of the system, its material and geometrical properties, and the aspects associated with its condition state (e.g., material defects, current damages, or evidence of previous damages); (2) the exogenous factors, such as hazards, peculiar operational conditions, loading, and environmental factors.…”

Section: Feature Selection Strategymentioning

confidence: 99%

“…and geometrical properties, and the aspects associated with its condition state (e.g., material defects, current damages, or evidence of previous damages); (2) the exogenous factors such as hazards, peculiar operational conditions, loading, and environmental factors. Engineering judgment plays a relevant role in performing this task, and it should always be supported by the knowledge acquired on similar cases, documentation, surveys, inspections, sampling, experimental tests, and analytical and numerical simulations It is worth noting that these are typical sources for structural assessment and retrofitting as recommended by codes and standards, as the Eurocode 8 part 3 [56], the International Existing Building Code [57], the ASCE/SEI Standard 41-06 [58], or the ISO 13822 [59] among others.…”

Section: Feature Selection Strategymentioning

confidence: 99%

“…and to integrate the information content of each of them, by combining their sensitivity to damage with their precision as global or local indicators. However, the result is a large amount of information, easily up to terabytes of unstructured or semi-structured data per week [1], potentially able to reduce the uncertainty, but extremely more demanding in terms of data management and interrogation [2]. The complexity and the volume of this data flow is comparable to the so-called "Big Data", which refers to datasets that are almost unmanageable and unprocessable with traditional approaches and tools [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques

Barontini

Masciotta

Amado-Mendes

et al. 2021

Sensors

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Data-driven methodologies are among the most effective tools for damage detection of complex existing buildings, such as heritage structures. Indeed, the historical evolution and actual behaviour of these assets are often unknown, no physical models are available, and the assessment must be performed only based on the tracking of a set of damage-sensitive features. Selecting the most representative state indicators to monitor and sampling them with an adequate number of records are therefore essential tasks to guarantee the successful performance of the damage detection strategy. Despite their relevance, these aspects have been frequently taken for granted and little attention has been paid to them by the scientific community working in the field of Structural Health Monitoring. The present paper aims to fill this gap by proposing a multistep strategy to drive the selection of meaningful pairs of correlated features in order to support the damage detection as a one-class classification problem. Numerical methods to reduce the number of necessary acquisitions and estimate the performance of approximation techniques are also provided. The analyses carried out to test and validate the proposed strategy exploit a dense dataset collected during the long-term monitoring of an outstanding heritage structure, i.e., the Church of ‘Santa Maria de Belém’ in Lisbon.

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Section: Feature Selection Strategymentioning

confidence: 99%

Section: Feature Selection Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques

Barontini

Masciotta

Amado-Mendes

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…These activities usually involve gathering information about several components of the structure through which informed decisions about its state can be deduced. Wang et al [1] reported that one of the earliest permanent structural monitoring of high-rise buildings dates back to the 1940s. The need to track and measure the progress of structures continually and as a permanent feature gave birth to the term Structural Health Monitoring.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Framework for Structural Health Monitoring in Nigeria

O.K¹

2020

IJATCSE

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Internet of Things (IoT) is a paradigm that has been explored in various domains, as it facilitates the automation of many tedious processes with more efficiency and robustness. This technology can be applied to effectively mitigate the rising cases of building collapse in Nigeria. Traditional means of monitoring civil structures and determining their health seems inadequate, expensive and out of reach to many, hence a novel means of Structural Health Monitoring (SHM) which IoT provides. IoT facilitates the monitoring and measurement of several structural parameters with a high degree of precision through sensors while enabling these 'things' to communicate effectively through the designed protocol. It has proved to be a cost-effective alternative to traditional SHM while providing the flexibility of alerting users when there is any damage to the structure. In this paper, an IoT platform for structural integrity evaluation and monitoring in civil buildings was developed using piezoelectric transducers (PZT), some microcontrollers, and a SIM800L. These PZTs were deployed at determined nodes to read data continually while some digital signal processing techniques were used to manipulate these data towards determining the health of the structure being monitored. In the end, a system that reads signals from structures, store the signals on the cloud and notify users if any structural damage arises were developed.

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“…Generally speaking, the complexity and success of a structural health monitoring (SHM) system depends on three aspects: detection of damage, accurate localization of damage, and quantification of the damage. 1 This research article focuses on the second aspect, that is, accurate damage localization with a SHM system particularly with high residuals. Acoustic emission (AE) is one of the most important non-destructive technique for passive health monitoring.…”

Section: Introductionmentioning

confidence: 99%

A new non-linear framework for localization of acoustic sources

Das

Lai

Chan

et al. 2018

Structural Health Monitoring

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Source localization is the process of assigning spatial metadata to a set of unlabeled time of arrival for acoustic signals that may be interlaced with extreme outliers caused by detection and material-induced errors. Exploiting the fact that location error is attributed to high residuals, a new framework for non-linear source localization is proposed in this article. The detected time of arrival was employed to calculate the intermediate source and event time. The framework then quarantines sensors with high residual based on the intermediate event time and relative distance from the intermediate source. Then, the time of arrivals for quarantined sensors were reviewed and adjusted. Finally, with new arrival time, the framework iteratively optimizes the spatial location and event time, along with the updating of velocity field through a feedback loop. The new framework was used to localize acoustic emission sources with extreme outliers in an anisotropic medium. The results indicate that the proposed framework can accurately quarantine the bad sensor and localize sources with an accuracy better than existing source localization methods. Finally, the framework was employed to study the progressive damage in a steel fiber–reinforced beam under four-point bending. The source localization result is consistent with that from visual inspection of the member at failure.

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Introduction to sensing for structural performance assessment and health monitoring

Cited by 21 publications

References 17 publications

Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques

Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques

Internet of Things Framework for Structural Health Monitoring in Nigeria

A new non-linear framework for localization of acoustic sources

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