Long-term gage reliability for structural health monitoring of steel bridges

Samaras, Vasileios; Fasl, Jeremiah; Reichenbach, Matt; Helwig, Todd A.; Wood, Sharon L.; Frank, Karl H.

doi:10.1117/12.914841

Cited by 2 publications

(1 citation statement)

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“…Since usual sensor systems have a much shorter life time, two approaches seem to be successful: measuring systems have to be designed for long-term use, i.e., the systems have to be maintainable and adequate measures shall reduce the failure probability of components. In addition, reliable dataacquisition-systems and data analysis as well as the knowledge of the long-term behavior of sensors are essential [9][10][11][12]. It has to be known whether the measured data relate to the monitored infrastructure or are derived from the degradation of the sensors.…”

Section: Introductionmentioning

confidence: 99%

Reliability of long-term monitoring data

Anderegg

Brönnimann

Meier

2013

J Civil Struct Health Monit

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Increasing loads, aging, new construction materials like CFRP (carbon fiber reinforced polymers) and also cost pressure are influences for fitness of traffic infrastructures, especially bridges. Prolonged health monitoring can be a means to compete with these challenges. The lifetime of infrastructure can reach 100 years, while that of sensors is typically much shorter. Therefore, sensors need to be long-term qualified and might have to be replaced by new sensors or even another sensor system during the life cycle of a structure. A reliable system for data acquisition and data analysis will be essential: Do the measurement data really relate to the monitored infrastructure and are not an effect of the degradation of the sensing system? We discuss measurement uncertainty and reliability of data obtained by different sensor types, including resistance strain gages, fiber optical sensors and other sensors systems which are in service up to 17 years. Keywords Long-term Á Health monitoring Á Infrastructure Á Reliability Á Carbon fiber reinforced polymers (CFRP) Á Fiber optical sensor (FOS) Á Fiber bragg grating (FBG) Á Resistance strain gages (RSG) Á Sensor Á Data acquisition

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Section: Introductionmentioning

confidence: 99%

Reliability of long-term monitoring data

Anderegg

Brönnimann

Meier

2013

J Civil Struct Health Monit

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Categories of SHM Deployments: Technologies and Capabilities

2015

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The findings of an extensive literature survey focusing on bridge structural health monitoring (SHM) deployments are presented. Conventional, maturing, and emerging technologies are reviewed as well as deployment considerations for new SHM endeavors. The lack of published calibration studies (and quantification of uncertainty studies) for new sensors is highlighted as a major concern and area for future research. There are currently very few examples of SHM systems that have clearly provided significant value to the owners of monitored structures. The results of the literature survey are used to propose a categorization system to better assess the potential outcomes of bridge SHM deployments. It is shown that SHM studies can be categorized as one (or a combination) of the following: (1) anomaly detection, (2) sensor deployment studies, (3) model validation, (4) threshold check, and (5) damage detection. The new framework aids engineers specifying monitoring systems to determine what should be measured and why, hence allowing them to better evaluate what value may be delivered to the relevant stakeholders for the monitoring investments.

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Long-term gage reliability for structural health monitoring of steel bridges

Cited by 2 publications

References 0 publications

Reliability of long-term monitoring data

Reliability of long-term monitoring data

Categories of SHM Deployments: Technologies and Capabilities

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