Monitoring, Modeling, and Assessment of a Self-Sensing Railway Bridge during Construction

Butler, Lynda L.; Lin, Weiwei; Xu, Jinlong; Gibbons, Niamh; Elshafie, Mohammed; Middleton, Campbell

doi:10.1061/(asce)be.1943-5592.0001288

Cited by 15 publications

(16 citation statements)

References 14 publications

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“…This recently constructed bridge was instrumented with an innovative fibre optic based sensor (FOS) network which was designed to provide reliable and long-term measurements of the bridge's operational performance 31 . As depicted in Figure 3, FBG sensors were installed on the main structural elements.…”

Section: Sensor Instrumentationmentioning

confidence: 99%

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Digital twinning of self-sensing structures using the statistical finite element method

Febrianto¹,

Butler²,

Cirak³

2021

Preprint

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The monitoring of infrastructure assets using sensor networks is becoming increasingly prevalent. A digital twin in the form of a finite element model, as used in design and construction, can help in making sense of the copious amount of collected sensor data. This study demonstrates the application of the statistical finite element method (statFEM), which provides a consistent and principled means for synthesising data and physics-based models, in developing a digital twin of an instrumented railway bridge. The considered structure is a steel skewed half-through bridge of 27.34 m length located along the West Coast Mainline near Staffordshire in the UK. Using strain data captured from fibre Bragg grating (FBG) sensors at 108 locations along the bridge superstructure, statFEM can predict the 'true' system response while taking into account the uncertainties in sensor readings, applied loading and finite element model misspecification errors. Longitudinal strain distributions along the two main I-beams are both measured and modelled during the passage of a passenger train. The digital twin, because of its physics-based component, is able to generate reasonable strain distribution predictions at locations where no measurement data is available, including at several points along the main I-beams and on structural elements on which sensors are not even installed. The implications for long-term structural health monitoring and assessment include optimization of sensor placement, and performing more reliable what-if analyses at locations and under loading scenarios for which no measurement data is available.

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Section: Sensor Instrumentationmentioning

confidence: 99%

“…The newly constructed skewed half-through bridge has previously been instrumented with a network of fibre optic based sensors. The instrumentation of the bridge using FBG strain sensors is described in Butler et al 31 . The sensor measurements have since been compared with a deterministic FE model of the bridge in Lin et al 32 .…”

Section: Introductionmentioning

confidence: 99%

Digital twinning of self-sensing structures using the statistical finite element method

Febrianto¹,

Butler²,

Cirak³

2021

Preprint

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“…The performance of IB5, both during construction and in operation, was investigated through numerical (finite element) modeling which was validated using sensor data [5], [6]. A 3D FE model was constructed incorporating solid, shell and rebar elements, with the consideration of time-dependent concrete properties, staged construction and torsional effects due to the skewed bridge geometry.…”

Section: Physics-based Approach: Finite Element Modelingmentioning

confidence: 99%

“…The case study considered two railway bridges in Staffordshire, UK which have pervasive sensor networks installed at the time of construction. The study involves an interdisciplinary collaboration between the Cambridge Centre for Smart Infrastructure and Construction (CSIC) and the Alan Turing Institute (ATI), combining the expertise of bridge monitoring [4], finite element modeling [5], [6], building information modeling (BIM) [7], [8] and statistical modeling [9], [10], with the end objective of creating a working digital twin for the instrumented Staffordshire bridges.…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin of Bridges for Structural Health Monitoring

Cong¹,

Butler²,

Calka³

et al. 2019

Structural Health Monitoring 2019

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“…However, the future trend in SHM consists of integrating such sensors at the beginning of the construction phase of the structures, in order to gather their whole load history (i.e. including the construction and the pre-service phases) [21][22][23]. Mostly, the information about the early-age behavior of composite civil engineering structures is of great interest as it permits to bring an immediate awareness about their specific issues, and gives guidance to the experts and the manufac-turers to optimize their construction procedures [24].…”

Section: Introductionmentioning

confidence: 99%

Optical fiber sensors implementation for monitoring the early-age behavior of full-scale Timber-Concrete Composite slabs

Teguedy

Joly-Lapalice

Sorelli

2019

Construction and Building Materials

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h i g h l i g h t s• Implementation of DOFs to monitor the short-term behavior of two TCC slabs. • Monitoring of the strain/temperature during concrete curing. • Full characterization of the strain evolution along the CLT slabs using DOFs.• Neutral axis and curvature evolution used to analyse the composite action in TCC. • Hygrometric variations generated considerable structural changes in TCC at early age.

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Monitoring, Modeling, and Assessment of a Self-Sensing Railway Bridge during Construction

Cited by 15 publications

References 14 publications

Digital twinning of self-sensing structures using the statistical finite element method

Digital twinning of self-sensing structures using the statistical finite element method

A Digital Twin of Bridges for Structural Health Monitoring

Optical fiber sensors implementation for monitoring the early-age behavior of full-scale Timber-Concrete Composite slabs

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