Fatigue reliability assessment of retrofitted steel bridges integrating monitored data

Liu, Ming; Frangopol, Dan M.; Kwon, Kihyon

doi:10.1016/j.strusafe.2009.08.003

Cited by 104 publications

(43 citation statements)

References 14 publications

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“…Structural Health Monitoring (SHM) is expected to contribute to this field of study by enabling automated analysis of data collected on-site by sensing systems [3]. Examples of SHM applications include aftershock rapid assessment [4], optimal scheduling of maintenance activities [5], and effective monitoring of deterioration [6], and fatigue of structural components [7].…”

Section: Introductionmentioning

confidence: 99%

Electromechanical modelling of a new class of nanocomposite cement-based sensors for structural health monitoring

D’Alessandro

Ubertini

Materazzi

et al. 2014

Structural Health Monitoring

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This work focuses on the analysis of a new nanocomposite cement-based sensor (carbon nanotube cementbased sensor), for applications in vibration-based structural health monitoring of civil engineering structures. The sensor is constituted of a cement paste doped with multi-walled carbon nanotubes, so that mechanical deformations produce a measurable change of the electrical resistance. Prior work of some of the authors has addressed the fabrication process, dynamic behaviour and implementation to full-scale structural components. Here, we investigate the effectiveness of a linear lumped-circuit electromechanical model, in which dynamic sensing is associated with a strain-dependent modulation of the internal resistance. Salient circuit parameters are identified from a series of experiments where the distance between the electrodes is parametrically varied. Experimental results indicate that the lumped-circuit model is capable of accurately predicting the step response to a voltage input and its steady-state response to a harmonic uniaxial deformation. Importantly, the model is successful in anticipating the presence of a superharmonic component in sensor's output. KeywordsCarbon nanotubes, electromechanical model, nanotechnology, smart materials, smart sensors, structural health monitoring nanotubes, so that mechanical deformations produce a measurable change of the electrical resistance.Prior work of some of the authors has addressed the fabrication process, dynamic behaviour, and implementation to full-scale structural components. Here, we investigate the effectiveness of a linear lumped-circuit electromechanical model, in which dynamic sensing is associated with a straindependent modulation of the internal resistance. Salient circuit parameters are identified from a series of experiments where the distance between the electrodes is parametrically varied. Experimental results indicate that the lumped-circuit model is capable of accurately predicting the step response to a voltage input and its steady-state response to a harmonic uniaxial deformation. Importantly, the model is successful in anticipating the presence of a superharmonic component in sensor's output.

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Section: Introductionmentioning

confidence: 99%

Electromechanical modelling of a new class of nanocomposite cement-based sensors for structural health monitoring

D’Alessandro

Ubertini

Materazzi

et al. 2014

Structural Health Monitoring

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“…For example, Deng, Li, and Ding (2014), Liu, Frangopol, and Kwon (2010) have considered monitoring of stress ranges and number of cycles. In other cases, the results of fatigue crack inspections have been used in order to assess the remaining life, e.g.…”

Section: Description Of the Detailmentioning

confidence: 99%

Maintenance decision model for steel bridges: a case in the Netherlands

Attema

Acharige

Morales-Nápoles

et al. 2016

Structure and Infrastructure Engineering

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A probabilistic model is developed to investigate the crack growth development in welded details of orthotropic bridge decks. Bridge decks may contain many of these vulnerable details and bridge reliability cannot always be guaranteed upon the attainment of a critical crack. Therefore, insight into the crack growth development is crucial in guaranteeing bridge reliability and scheduling efficient maintenance schemes. The probabilistic nature of the crack growth development model and the dependence of this model on many interdependent random variables result in significant uncertainties regarding model outcome. To reduce some of these uncertainties, the probabilistic model is combined with a monitoring system installed on a part of the bridge. In addition, a Bayesian network is used to determine the dependence structure between the different details (monitored and non-monitored) of the bridge. This dependence structure enables us to make more accurate crack growth predictions for all details of the bridge while monitoring only a limited number of those details and updating the remaining uncertainties.

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“…They can be used to automatically assess the condition of a structural component through the analysis of on-site collected data. This can provide signs of a likely incipient or initial damage, and ultimately lead to condition-based maintenance decisions instead of traditional breakdown-or timebased decisions, resulting in strong economic benefits [24][25][26]. Self-sensing materials also provide the opportunity to transform a structural component into an infinite or continuous set of sensors, therefore enabling distributed sensing in the form of a dense sensor network [27,28].…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study on Static and Dynamic Strain Sensitivity of Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures

Meoni¹,

D’Alessandro²,

Downey³

et al. 2018

Preprint

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Abstract:The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix materials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNTs content. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both static and dynamically varying compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

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Fatigue reliability assessment of retrofitted steel bridges integrating monitored data

Cited by 104 publications

References 14 publications

Electromechanical modelling of a new class of nanocomposite cement-based sensors for structural health monitoring

Electromechanical modelling of a new class of nanocomposite cement-based sensors for structural health monitoring

Maintenance decision model for steel bridges: a case in the Netherlands

An Experimental Study on Static and Dynamic Strain Sensitivity of Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures

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