Cluster computing‐aided model updating for a high‐fidelity finite element model of a long‐span cable‐stayed bridge

Lin, Kaiqi; Xu, You Lin; Lu, Xinzheng; Guan, Zhongguo; Li, Jianzhong

doi:10.1002/eqe.3270

Cited by 28 publications

(39 citation statements)

References 38 publications

(85 reference statements)

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“…Therefore, the earthquake‐induced collapse prognosis of a long‐span cable‐stayed bridge requires (1) an accurate nonlinear FE model that reflects the real‐time structural damage state of the bridge and (2) a feasible and reliable numerical solution for simulating the strong nonlinearity and entire collapse progress of a bridge under strong earthquakes. In this study, the accurate nonlinear FE model is derived using an improved nonlinear FE model updating method based on the authors' previous work 22 …”

Section: Improved Tha‐based Nonlinear Fe Model Updatingmentioning

confidence: 99%

“…The improved THA‐based nonlinear FE model updating method is based on the programme developed by the authors in their previous study 22 . The parallel and cluster computing‐aided techniques are integrated into the programme to accelerate the model updating process and make it suitable for large civil structures, such as the long‐span cable‐stayed bridge discussed in this study.…”

Section: Improved Tha‐based Nonlinear Fe Model Updatingmentioning

confidence: 99%

“…The parallel and cluster computing‐aided techniques are integrated into the programme to accelerate the model updating process and make it suitable for large civil structures, such as the long‐span cable‐stayed bridge discussed in this study. Details about the cluster‐computing‐aided FE model updating programme are elaborated in Lin et al 22 Based on the programme, a response time history‐based objective function is proposed for updating the long‐span cable‐stayed bridge, as shown in Equation .

Objective Function = ((), \sum_{i = 1}^{n} ETHA ((),, {bold-italicX}_{i}, {bold-italicY}_{i})) / n,

where X i and Y i are the response measurement data and simulation results at the i th monitoring point, respectively, n is the number of the monitoring points considered in the objective function and ETHA is the error index for quantifying the difference between the measured and simulated responses of the bridge, which can be determined by Equation as follows:

ETHA ((), bold-italicX, bold-italicY) = 1 ‐ (||, \frac{Ε [(X - {bold-italicμ}_{bold-italicX}) (Y - {bold-italicμ}_{bold-italicY})]}{{bold-italicσ}_{bold-italicX} {bold-italicσ}_{bold-italicY}}),

where μ X and σ X are the average and standard deviation of the measurement data vector X , respectively, μ Y and σ Y correspond to the simulation result vector Y and E[·] denotes the expectation operation.…”

Section: Improved Tha‐based Nonlinear Fe Model Updatingmentioning

confidence: 99%

“…An effective way to reduce the modelling uncertainties of civil structures is to update the numerical model based on the measurement data acquired by the SHM systems. Existing studies have attempted to improve the accuracy of FE models through various model updating algorithms 18‐22 . Hasançebi and Dumlupınar 18 updated a reinforced‐concrete T‐beam bridge using an artificial neural network (ANN)–based method.…”

Section: Introductionmentioning

confidence: 99%

“…Both the global and local responses of the bridge were updated, and the results were in good agreement with the experimental measurement data. Recently, a cluster‐computing‐aided FE model updating framework was proposed by Lin et al 22 for updating the refined FE model of large‐scale civil structures. Although a number of studies have proved that the accuracy of the numerical results can be significantly improved through FE model updating, most of them mainly focus on the reproduction of the measured responses that are simultaneously used as the updating objectives.…”

Section: Introductionmentioning

confidence: 99%

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Collapse prognosis of a long‐span cable‐stayed bridge based on shake table test and nonlinear model updating

Lin

et al. 2020

Earthq Engng Struct Dyn

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The collapse of long-span cable-stayed bridges under strong earthquakes will not only result in severe casualties and loss of property but also significantly delay the rehabilitation of the affected area. It is therefore essential to study the failure progress and potential collapse mechanisms of these bridges under strong earthquakes for assisting their inspection and maintenance and helping them survive an earthquake. Collapse simulations in existing studies are commonly conducted with design-document-based finite element (FE) models, which usually neglect the actual damage state of the bridges. The influences of modelling uncertainties on the simulated responses cannot be addressed. This study proposes a nonlinear FE model updating-based collapse prognosis method for long-span cable-stayed bridges, which includes (1) a correlatedupdating-parameter-based nonlinear FE model updating algorithm, (2) a restarted time history analysis-based seismic response prediction method and (3) an elemental deactivation-based collapse simulation algorithm. The shake table test of a scaled Sutong cable-stayed bridge in China is adopted to illustrate the proposed earthquake-induced collapse prognosis method. A refined FE model of the bridge is first established and nonlinearly updated using the measurement data. The collapse prognosis of the bridge under a subsequent strong ground motion is then performed based on the updated model. The predicted structural responses and final failure mechanisms are compared with the measured responses and experimental observations with good agreement, indicating that the proposed method is feasible and accurate for evaluating the seismic performance and failure mechanisms of long-span cable-stayed bridges. K E Y W O R D S collapse prognosis, long-span cable-stayed bridge, nonlinear FE model updating, restarted time history analysis, shake table test 1 | INTRODUCTION With the rapid development of global economy, many long-span bridges have been constructed worldwide over the last few decades. These long-span bridges usually serve as critical infrastructures in urban transportation systems, and many

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Section: Improved Tha‐based Nonlinear Fe Model Updatingmentioning

confidence: 99%

Section: Improved Tha‐based Nonlinear Fe Model Updatingmentioning

confidence: 99%

Objective Function = ((), \sum_{i = 1}^{n} ETHA ((),, {bold-italicX}_{i}, {bold-italicY}_{i})) / n,

ETHA ((), bold-italicX, bold-italicY) = 1 ‐ (||, \frac{Ε [(X - {bold-italicμ}_{bold-italicX}) (Y - {bold-italicμ}_{bold-italicY})]}{{bold-italicσ}_{bold-italicX} {bold-italicσ}_{bold-italicY}}),

Section: Improved Tha‐based Nonlinear Fe Model Updatingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Collapse prognosis of a long‐span cable‐stayed bridge based on shake table test and nonlinear model updating

Lin

et al. 2020

Earthq Engng Struct Dyn

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Finite element model updating method for continuous girder bridges using monitoring responses and traffic videos

Luo

Xia

Wang

et al. 2022

Structural Contr & Hlth

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Accurate finite element (FE) models play an essential role in the health monitoring of operational bridges. Static structural deflections caused by vehicles, which are used in traditional finite element model updating (FEMU) methods, are often procured from field tests, interrupting the traffic and limiting the test loading scenarios. This study proposes a FEMU method that directly applies the massive, multi-source structural and traffic data in the operation phase to update the FE model, effectively solving the defects above. We use the computer vision-based vehicle load identification technique to accurately locate and weigh vehicle loads and carry out static simulations in the FE model based on the identified vehicle loads. The proposed FEMU objective function is established using indices including dynamic structural characteristics and vehicle-induced static structural responses. The static error index in the objective function integrates the curve shape and extrema difference of theoretic and measured static responses. Finally, we deploy a parallel particle swarm optimization (PSO) algorithm to find the global optimal updated FE model. A continuous scale bridge model is employed in the experimental studies with four typical scenarios. Compared to the results from the initial FE model, the updated FE model provides significantly better results both in dynamic and static aspects in all scenarios, and the static error indexes reduce by 75% on average. The proposed method offers a practical approach to deploying the monitoring data for real-time FEMU, which considers dynamic and static features and provides a basis for damage detection, performance assessment, and management.

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Digital twin-based life-cycle seismic performance assessment of a long-span cable-stayed bridge

Lin

et al. 2022

Bull Earthquake Eng

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Long-span cable-stayed bridges serve as critical infrastructures in highway transportation systems. If they are located in earthquake-prone zones, seismic action becomes a dominant hazard that threatens their functionality and safety. Long-span cable-stayed bridges are often designed to have a service life of more than a hundred years. They may thus experience multiple seismic events in their service life, and the earthquake shocks may

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Cluster computing‐aided model updating for a high‐fidelity finite element model of a long‐span cable‐stayed bridge

Cited by 28 publications

References 38 publications

Collapse prognosis of a long‐span cable‐stayed bridge based on shake table test and nonlinear model updating

Collapse prognosis of a long‐span cable‐stayed bridge based on shake table test and nonlinear model updating

Finite element model updating method for continuous girder bridges using monitoring responses and traffic videos

Digital twin-based life-cycle seismic performance assessment of a long-span cable-stayed bridge

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