Complex frequency identification using real modal shapes for a structure with proportional damping

Qu, Chunxu; Yi, Ting‐Hua; Yao, Xiaojun; Li, Hong‐Nan

doi:10.1111/mice.12676

Cited by 24 publications

(9 citation statements)

References 33 publications

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“…Structural safety has always been the most concerned issue of civil engineers and scholars. In recent years, thanks to the rapid progress of various data acquisition technologies (Zhao et al., 2022), signal processing methods (Amezquita‐Sanchez & Adeli, 2019; Qu et al., 2021), and artificial intelligence algorithms (Rafiei & Adeli, 2017; Zheng et al., 2022), the technology of structural health monitoring has made considerable progress (Oh et al., 2017), and has also received more and more attention. To accurately identify cable tensions is critical for bridge construction control and in‐service monitoring (Benedettini & Gentile, 2011; Dong & Catbas, 2021; He et al., 2008).…”

Section: Introductionmentioning

confidence: 99%

A decoupled algorithm for cable parameter identification by frequency‐ratio‐offset

Sun

Zheng

Zhang

et al. 2023

Computer aided Civil Eng

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This paper proposes a frequency‐ratio‐offset (FRO)‐based parameter identification algorithm for real bridge cables. By introducing specific time and space scaling factors, the dynamic equation is fully nondimensionalized and reduced to a most concise form, independent of all distributed cable parameters. As a result, the frequency equation has only two variables, which can be easily solved to obtain the universal dimensionless frequency–length curve under specified boundary conditions. Based on these curves, a decoupled identification algorithm is constructed, using the newly defined FRO as a bridge to directly connect the dimensionless beam and its real‐world counterpart. The algorithm enables a simultaneous determination of any two of the three cable parameters using a pair of measured frequencies. Examples involving both laboratory tests and real‐life applications demonstrate the effectiveness and accuracy of the algorithm. Based on a statistical analysis of the fitness of dozens of predicted and measured frequencies, a guideline for frequency selection is suggested, and the influence of measurement error on the identification results is discussed. The proposed algorithm could become a convenient tool for engineers to efficiently identify multiple parameters (e.g., tension and bending stiffness) for a broad class of bridge cables.

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Section: Introductionmentioning

confidence: 99%

A decoupled algorithm for cable parameter identification by frequency‐ratio‐offset

Sun

Zheng

Zhang

et al. 2023

Computer aided Civil Eng

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“…Along with the system identification (SI) scheme based on estimating the dynamic characteristics of a structure using output-only response (Amezquita- Sanchez & Adeli, 2015;Amezquita-Sanchez et al, 2017;Z. Li et al, 2017;Perez-Ramirez et al, 2016a, b;Qarib & Adeli, 2015;Yuen & Huang, 2018;Yuen & Katafygiotis, 2005;Yuen & Mu, 2015;Yuen et al, 2019) and recently developed SI technique (Amezquita- Perez-Ramirez et al, 2019;Qu et al, 2021;Zhao et al, 2022), some studies proposed to simultaneously estimate the input force. Sun et al (2015) presented an algorithm to estimate the input forces through statistical regularization in the process of forwarding analysis, and the dynamic properties for the analytical-and experimentalscaled test model are subsequently updated.…”

Section: Introductionmentioning

confidence: 99%

“…Along with the system identification (SI) scheme based on estimating the dynamic characteristics of a structure using output‐only response (Amezquita‐Sanchez & Adeli, 2015; Amezquita‐Sanchez et al., 2017; Guo & Kareem, 2016; Guo et al., 2016; Z. Li et al., 2017; Perez‐Ramirez et al., 2016a, b; Qarib & Adeli, 2015; Yuen & Huang, 2018; Yuen & Katafygiotis, 2005; Yuen & Mu, 2015; Yuen et al., 2019) and recently developed SI technique (Amezquita‐Sanchez & Adeli, 2019; Perez‐Ramirez et al., 2019; Qu et al., 2021; Zhao et al., 2022), some studies proposed to simultaneously estimate the input force. Sun et al.…”

Section: Introductionmentioning

confidence: 99%

Frequency domain identification of modal characteristics and loads from output‐only measurements

Hwang

Kwon

Kareem

2023

Computer aided Civil Eng

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This paper proposes a frequency domain framework for concurrently estimating the modal structural parameters and modal loads from the measured output response. A mathematical load model is theoretically developed and compared to the load determined by force identification. Unlike conventional modal identification schemes, the proposed approach can effectively accommodate the spectral shape of the external load in the frequency domain, enabling the determination of the modal parameters without explicit white noise assumption. The proposed approach is verified by applying it to a single‐degree‐of‐freedom system system representing a modal response, an aeroelastic model in an atmospheric boundary layer wind tunnel, a multi‐degree‐of‐freedom system, and the measured acceleration response of a 40‐story building. The simulation results demonstrate that the proposed technique can reliably estimate the modal parameters and load model and is robust against the noise present in the response and the order of the load model. Additionally, the proposed technique can be applied to various loading conditions such as vortex‐induced and buffeting effects since the modal parameters and the load model can be identified for any shape of the loading spectrum.

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“…19,20 Thanks to the recent progress in machine learning techniques, they have been frequently employed on SHM data of bridges. [21][22][23][24] The state-of-the-art algorithms include artificial neural networks (ANNs), 25,26 independent component analysis, 27 cluster analysis, 28,29 support vector machines (SVMs), 30,31 transfer learning, 32 and ensemble learning methods, 33 which have been proved to be accurate and efficient. Particularly, Lu et al 34 developed a stochastic fatigue truck-load model for investigating fatigue reliability of welded steel bridge decks.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven prediction and interpretation of fatigue damage in a road‐rail suspension bridge considering multiple loads

Sun

Santos

Caetano

2022

Structural Contr & Hlth

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Summary In long‐span suspension bridges, fatigue is a significant concern for steel members as they are continuously under multiple effects such as wind, temperature, and traffic loads. Therefore, it is important to quantify the fatigue damage at critical details for effective maintenance countermeasures. This paper proposes an ensemble machine‐learning‐based method with physical interpretation, which predicts fatigue damage with monitored parameters of temperature, wind, train, and roadway loads. Interpretation of the machine learning model is achieved regarding the different contributions of the monitored parameters to the fatigue damage. Datasets from the structural health monitoring (SHM) system of a long‐span rail‐road suspension bridge are utilized to verify the proposed method. They are divided into 80% of training datasets and 20% of testing datasets. The ensemble machine learning model is established with Gradient Boosting Regression Tree (GBRT), and the fatigue damage for top chords on the upstream and downstream sides is predicted. The results are compared with other state‐of‐the‐art machine learning algorithms, namely, artificial neural network (ANN), support vector machine (SVM), decision tree (DT), and random forest (RF). It is found that GBRT demonstrates the highest accuracy for fatigue damage prediction with R2 of 0.9016 and 0.9180, respectively. Furthermore, interpretation of the results is carried out with feature importance estimates (FIE) and individual conditional expectation (ICE). FIE results indicate that the train load has the most significant influence on fatigue damage of the suspension bridge, and ICE results can provide insights on better maintenance plans to avoid severe fatigue damage.

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Complex frequency identification using real modal shapes for a structure with proportional damping

Cited by 24 publications

References 33 publications

A decoupled algorithm for cable parameter identification by frequency‐ratio‐offset

A decoupled algorithm for cable parameter identification by frequency‐ratio‐offset

Frequency domain identification of modal characteristics and loads from output‐only measurements

Data‐driven prediction and interpretation of fatigue damage in a road‐rail suspension bridge considering multiple loads

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