Physics guided wavelet convolutional neural network for wind-induced vibration modeling with application to structural dynamic reliability analysis

Xu, Zidong; Wang, Hao; Xing, Chenxi; Tao, Tianyou; Mao, Jianxiao; Liu, Yun

doi:10.1016/j.engstruct.2023.117027

Cited by 28 publications

(8 citation statements)

References 53 publications

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“…Zong et al [14] used the central composite experimental design method (CCD) and second-order response surface model to update the finite element model based on the health monitoring of a large-span continuous rigid frame bridge, proving that finite element model updating based on a second-order response surface has a high accuracy [15][16][17]. In recent years, finite element modeling and correction technology based on artificial intelligence has attracted more and more attention; for instance, wavelet convolutional neural networks and deep-learning neural networks have been used for wind-induced vibration modeling and stress distribution prediction [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

FE Model Updating of Continuous Beam Bridge Based on Response Surface Method

Dong,

Shi,

Zhong

et al. 2024

Buildings

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In this paper, A high-order response surface method is proposed for finite element model updating of continuous beam bridges. Firstly, based on visual inspection and environmental vibration testing, the peak picking (PP) method and random subspace identification (SSI) method are used to identify the dynamic characteristic parameters of the structure. Then, the finite element model of the continuous beam bridge is updated based on the third-order response surface method. It can be concluded that the results of the updated finite element model are in good agreement with the test results, and the maximum error between the calculated and measured frequency is less than 3%, with MAC values greater than 85%. Moreover, the updated finite element model can reflect the current situation of real bridges and serve as the basis for bridge health monitoring, damage detection, and safety assessment.

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Section: Introductionmentioning

confidence: 99%

FE Model Updating of Continuous Beam Bridge Based on Response Surface Method

Dong,

Shi,

Zhong

et al. 2024

Buildings

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“…The evaluation metrics used in this paper are the time-response assurance criterion (TRAC), which can be found in [43], and the relative root mean square error (RRMSE) in the time domain, as written in (32). They are used to quantify the quality of the predictions vs. the measured signal.…”

Section: Modal Expansionmentioning

confidence: 99%

“…In addition to conventional structural analysis methods, neural networks can predict structural responses nowadays. The emerging trends can be found in [32,33].…”

Section: Introductionmentioning

confidence: 99%

Fatigue Analysis of a Jacket-Supported Offshore Wind Turbine at Block Island Wind Farm

Partovi-Mehr,

DeFrancisci,

Minaeijavid

et al. 2024

Sensors

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Offshore wind-turbine (OWT) support structures are subjected to cyclic dynamic loads with variations in loadings from wind and waves as well as the rotation of blades throughout their lifetime. The magnitude and extent of the cyclic loading can create a fatigue limit state controlling the design of support structures. In this paper, the remaining fatigue life of the support structure for a GE Haliade 6 MW fixed-bottom jacket offshore wind turbine within the Block Island Wind Farm (BIWF) is assessed. The fatigue damage to the tower and the jacket support structure using stress time histories at instrumented and non-instrumented locations are processed. Two validated finite-element models are utilized for assessing the stress cycles. The modal expansion method and a simplified approach using static calculations of the responses are employed to estimate the stress at the non-instrumented locations—known as virtual sensors. It is found that the hotspots at the base of the tower have longer service lives than the jacket. The fatigue damage to the jacket leg joints is less than 20% and 40% of its fatigue capacity during the 25-year design lifetime of the BIWF OWT, using the modal expansion method and the simplified static approach, respectively.

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“…Their condition can be evaluated using structural reliability theory to gauge their reliability and assess their service life (Deng et al 2019, Sun et al 2016. However, bridge hangers are regularly exposed to cyclic loads like temperature (Pagani et al 2014), wind (Ding et al 2023a,2023b, Xu et al 2023, and vehicle loads (Zhou et al 2015) during operation, leading to fatigue issues that can compromise the structure's durability. It is paramount to accurately determine the fatigue life of bridge hangers and ascertain their fatigue reliability (Di et al 2020, Guo et al 2020.…”

Section: Introductionmentioning

confidence: 99%

Reliability assessment of bridge hangers based on fault tree with response surface

Lan,

Chu,

et al. 2024

Eng. Res. Express

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Bridge hangers play a crucial role in transmitting forces and supporting bridge structures. The condition of these hangers directly impacts the overall safety of the bridge. However, they are susceptible to fatigue failure due to cyclic loads like wind and vehicle traffic. Therefore, evaluating the fatigue performance of bridge hangers is vital. Typically made up of multiple parallel steel wires, the failure of even one wire can affect the fatigue performance of the entire hanger. This underscores the importance of considering the individual components’ impact on the system as a whole. This study presents a time-dependent reliability assessment model for bridge hangers based on the S-N curve of steel wire. Additionally, a system-level reliability assessment model is developed using fault tree analysis, taking into account the influence of broken wires and stress redistribution on the hanger system. The model is solved using response surface methodology, and through five numerical simulations, the study examines the effect of key parameters, including the number of broken wires, equivalent structural stress, and total cycles on the system's reliability.

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Physics guided wavelet convolutional neural network for wind-induced vibration modeling with application to structural dynamic reliability analysis

Cited by 28 publications

References 53 publications

FE Model Updating of Continuous Beam Bridge Based on Response Surface Method

FE Model Updating of Continuous Beam Bridge Based on Response Surface Method

Fatigue Analysis of a Jacket-Supported Offshore Wind Turbine at Block Island Wind Farm

Reliability assessment of bridge hangers based on fault tree with response surface

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