Fatigue Assessment of Critical Connections in a Historic Eyebar Suspension Bridge

Liu, Zhongxiang; Hebdon, Matthew H.; Correia, José A.F.O.; Carvalho, Hermes; Vilela, Paula Moura Leite; Jesus, Abílio M.P. De; Calçada, Rui

doi:10.1061/(asce)cf.1943-5509.0001236

Cited by 24 publications

(9 citation statements)

References 27 publications

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“…The elements used in the global FE model are listed in Table . Modeling details and model calibration of the global FE model can refer to the modeling process descripted in Liu et al Boundary conditions of the global FE model were set as follows: the bottoms of the main towers and ends of eye‐bar chains were fully restrained for all degrees of freedom (DOFs); the trusses were coupled with the columns of the main towers for all DOFs except for the rotational DOF around the z‐axis at two ends. Linear elastic material properties, including the density, Young's modulus, and Poisson ratio, were assigned to the elements of original replaced components of the global model, which were separately presented in Figure A,B.…”

Section: Case Study: Ancient Riveted Metallic Bridgementioning

confidence: 99%

“…The global basic model is used to obtain global behaviors of the structure under loading and boundaries for the local refined model, and the local refined model can be utilized to capture localized information under the boundaries to account for global fatigue response. Integrated global beam model and submodel by using shell elements were applied to investigate the fatigue performance and of fatigue lives of rehabilitated riveted bridge joints of an ancient bridge based on code‐based S‐N curves . In this method, the submodel was built in the global model at concerned position rather than in a separated model, and the nonlinearity of material properties was not considered.…”

Section: Introductionmentioning

confidence: 99%

“…Integrated global beam model and submodel by using shell elements were applied to investigate the fatigue performance and of fatigue lives of rehabilitated riveted bridge joints of an ancient bridge based on code-based S-N curves. 22 In this method, the submodel was built in the global model at concerned position rather than in a separated model, and the nonlinearity of material properties was not considered. This means that a single model needs to cover both the global model and multiple submodels when synchronous computation of multiple submodels is conducted, which is time-consuming.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global‐local fatigue assessment of an ancient riveted metallic bridge based on submodelling of the critical detail

Liu

Correia

Carvalho

et al. 2018

Fatigue Fract Eng Mat Struct

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Increasing traffic demands (ie, load intensity and operational life) on ancient riveted metallic bridges and the fact that these bridges were not explicitly designed against fatigue make the fatigue performance assessment and fatigue life prediction of riveted bridges a concern. This paper proposes a global‐local fatigue analysis method that integrates beam‐to‐solid submodeling, elastoplastic of material in local region, and local fatigue life prediction approach. The proposed beam‐to‐solid submodeling can recognize accuracy local stress/strain information accompanying with the global structural effect on the fatigue response of local riveted joints. The fatigue life is predicted based on cumulative damage rule, local strains, and number of cycles with consideration of traffic data, where the relation between the fatigue life and local strain is derived according to the Basquin and Manson‐Coffin law. Besides, the elastoplastic of material is considered. The proposed methodology for fatigue life prediction based on local strain parameter and the Palmgren‐Miner linear damage hypothesis is implemented in a case study of an ancient riveted bridge.

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Section: Case Study: Ancient Riveted Metallic Bridgementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Global‐local fatigue assessment of an ancient riveted metallic bridge based on submodelling of the critical detail

Liu

Correia

Carvalho

et al. 2018

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

show abstract

“…in the Netherlands, [13]). Parallely, works (for example [16,27]) based on numerical simulations show a significant increase of the maximum lifetime traffic load effects with the increase of single truck weights. However, the growth of truck volume and proportion of heavy trucks within the whole traffic cause a relatively moderate increase in load effects.…”

Section: Introductionmentioning

confidence: 99%

Fatigue analysis of a bridge deck using the peaks-over-threshold approach with application to the Millau viaduct

Nesterova¹,

Schmidt²,

Soize³

2020

SN Appl. Sci.

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Usually, to estimate the fatigue life of structural details in existing bridges, fatigue damage assessed with monitoring data is extrapolated linearly in time. In this study, a methodology is proposed for predicting the numbers of fatigue cycles with the peaks-over-threshold approach. On the other side, this POT approach, which is based on extreme values, is, as usually, also used to predict the load effects of extreme amplitude. This provides an innovative method to predict fatigue damage, which considers the initial distribution of numbers of cycles over time. It may account for traffic growth in volume and mass. This paper shows the comparison between reliability indexes assessed with the proposed method and the linear extrapolation.

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“…Corrosion of steel structures causes geometrical changes on the surface of structural members because of corrosion defects [1,2], which in turn lowers their load-bearing capacity. In the field of civil engineering, the corrosion of steel structures majorly contributes to deterioration of structural performance, together with fatigue cracks [3,4]. This issue worsens with the aging and deterioration of steel structures.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Tensile Performance of Steel Members by Analysis of Corroded Steel Surface Using Deep Learning

Chun

Yamane

Izumi

et al. 2019

Metals

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To conduct safety checks of corroded steel structures and formulate appropriate maintenance strategies, the residual strength of steel structural members must be assessed with high accuracy. Finite element method (FEM) analyses that precisely recreate the morphology of corroded surfaces using solid elements are expected to accurately assess the strength; however, the cost of conducting these calculations is extremely high. Therefore, a model that uses mean thickness as the thickness of the shell element is widely used but this method has precision issues, particularly regarding overestimation of risk. Thus, this study proposes a method of structural analysis in which the effective thickness of a shell element is assessed using the convolutional neural network (CNN), a type of deep learning performed on tensile structural members. An FEM model is then built based on the shell element that uses this effective thickness. We cross-validated this method by adding a feature extraction layer that reflects the domain knowledge, together with convolutional and pooling layers that are commonly used for CNN and found that a high level of accuracy could be achieved. Furthermore, regarding corroded steel plates and H-section steel, our method demonstrated results that were extremely close to those of models that used solid elements.

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Fatigue Assessment of Critical Connections in a Historic Eyebar Suspension Bridge

Cited by 24 publications

References 27 publications

Global‐local fatigue assessment of an ancient riveted metallic bridge based on submodelling of the critical detail

Global‐local fatigue assessment of an ancient riveted metallic bridge based on submodelling of the critical detail

Fatigue analysis of a bridge deck using the peaks-over-threshold approach with application to the Millau viaduct

Evaluation of Tensile Performance of Steel Members by Analysis of Corroded Steel Surface Using Deep Learning

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