Measuring and modelling the thermal performance of the Tamar Suspension Bridge using a wireless sensor network

Battista, Nicholas de; Brownjohn, James; Tan, Hwee-Pink; Koo, Ki-Young

doi:10.1080/15732479.2013.862727

Cited by 50 publications

(16 citation statements)

References 34 publications

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“…Case studies on SHM [4][5][6][7], specially focused on damage identification and structural safety maintenance, for application on large infrastructures as well as on residential and commercial buildings, have become increasingly narrated in the literature. Examples of the decentralized systems and advances on the development of sensors are frequently introduced in the structural monitoring area [8][9][10].…”

Section: Importance Of Shm For Structural Safety Maintenancementioning

confidence: 99%

Global overview on advances in structural health monitoring platforms

Mesquita

Antunes

Coelho

et al. 2016

J Civil Struct Health Monit

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Advances in the development of sensors, data processing systems, and numerical models have motivated the implementation of structural health monitoring (SHM) specially focused on the assessment of structural safety. Thus, this work presents a literature review about SHM platforms, especially from 1993 to 2015. In this way, a short history review about the recent advances on SHM, mainly related with dynamic monitoring, was summarized, and a benchmark and the main guidelines related with SHM platforms were also included in this review. Some case studies are also described here. Special attention was given to SHM platforms, and a method for their classification (an extension of Rytter's method) is presented. In addition, experiences related with heritage constructions, specially focused on maintenance, were included in this work. In the final section, some observations are made about the new prospects for SHM. The recent advances on SHM platforms contributed to the development of adaptive systems and to the cost reduction of the monitoring systems implementation, allowing the increase of its application in real structures. However, the monitoring systems should be implemented, optimizing all the available sensing technologies.

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Section: Importance Of Shm For Structural Safety Maintenancementioning

confidence: 99%

Global overview on advances in structural health monitoring platforms

Mesquita

Antunes

Coelho

et al. 2016

J Civil Struct Health Monit

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“…Another point stems from measurements of temperature and extension data in the bearing arrangement, obtained in July 2010, which revealed that the gap extension against temperature is perfectly adjusted to a linear relation, and therefore, does not indicate any relevant frictional force (see Fig. 15 of Battista et al [14] for clarification). The bearings and the string gauge sensor used for such assessment are shown in Fig.…”

Section: The Tamar Bridge Reference and Current State Datasetsmentioning

confidence: 99%

Modular Bayesian damage detection for complex civil infrastructure

Jesus

Brommer

Westgate

et al. 2019

J Civil Struct Health Monit

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We address the problem of damage identification in complex civil infrastructure with an integrative modular Bayesian framework. The proposed approach uses multiple response Gaussian processes to build an informative yet computationally affordable probabilistic model, which detects damage through inverse updating. Performance of structural components associated with parameters of the developed model was quantified with a damage metric. Particular emphasis is given to environmental and operational effects, parametric uncertainty and model discrepancy. Additional difficulties due to usage of costly physics-based models and noisy observations are also taken into account. The framework has been used to identify a reduction of a simulated cantilever beam elastic modulus, and anomalous features in main/stay cables and bearings of the Tamar bridge. In the latter case study, displacements, natural frequencies, temperature and traffic monitored throughout one year were used to form a reference baseline, which was compared against a current state, based on one month worth of data. Results suggest that the proposed approach can identify damage with a small error margin, even under the presence of model discrepancy. However, if parameters are sensitive to environmental/operational effects, as observed for the Tamar bridge stay cables, false alarms might occur. Validation with monitored data is also highlighted and supports the feasibility of the proposed approach.

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“…The major part of the cumulative sliding displacement is obtained from the vehicle‐induced dynamic displacement. In general, the displacement of bridge bearings is monitored by the displacement sensors with a frequency as small as 1 Hz or smaller . The low‐frequency displacement sensors cannot record the high‐frequency bearing displacement information.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the displacement of bridge bearings is monitored by the displacement sensors with a frequency as small as 1 Hz or smaller. [16][17][18][19][20] The low-frequency displacement sensors cannot record the high-frequency bearing displacement information. Additionally, the main girder will vibrate with various modes, including relatively high-frequency modes, for practical bridges subject to vehicle/wind loads.…”

Section: Introductionmentioning

confidence: 99%

Sliding life prediction of sliding bearings using dynamic monitoring data of bridges

Yang

et al. 2020

Struct Control Health Monit

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Summary Sliding bearings of a bridge are important sliding elements that accommodate the bridge movement caused by temperature action and vehicle/wind load. The sliding life of sliding bearings is determined by the wear of the sliding materials in the bearing. However, the wear of sliding bearings primarily results from a large amount of cumulative sliding displacement experienced by the sliding bearing, even if the movement of the bridge under the vehicle/wind load is far less than the allowable design displacement of the sliding bearing. In general, bearing displacement is monitored using displacement gauges with a low sampling frequency, which cannot accurately capture the relatively high‐frequency displacement component caused by vehicle/wind loads. For predicting the sliding life of sliding bearings, this paper provides a mode rotation angle superposition approach to estimate cumulative sliding displacement. The estimation approach incorporates the combination of the rotation angles of bridge ends and the modal parameters of bridges. The feasibility and advantage of the estimation approach are illustrated by a numerical example; then, the influence factors of the error are analysed. Finally, sliding life prediction of sliding bearings is applied to an actual bridge using the estimation approach of cumulative sliding displacement.

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Measuring and modelling the thermal performance of the Tamar Suspension Bridge using a wireless sensor network

Cited by 50 publications

References 34 publications

Global overview on advances in structural health monitoring platforms

Global overview on advances in structural health monitoring platforms

Modular Bayesian damage detection for complex civil infrastructure

Sliding life prediction of sliding bearings using dynamic monitoring data of bridges

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