Accurate force evaluation in prestressed cable-strut structures: A robust sparse Bayesian learning method with feedback-driven error optimization
Force evaluation is critical to ensuring the safety of cable-strut structures during service. This study employs dynamic testing to assess the internal forces resulting from cable relaxation in prestressed cable-strut structures. A cross-model cross-mode algorithm is utilized to establish a cable fo...
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| Vydané v: | Engineering structures Ročník 330; s. 119878 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.05.2025
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| ISSN: | 0141-0296 |
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| Abstract | Force evaluation is critical to ensuring the safety of cable-strut structures during service. This study employs dynamic testing to assess the internal forces resulting from cable relaxation in prestressed cable-strut structures. A cross-model cross-mode algorithm is utilized to establish a cable force evaluation model. This approach broadens the range of available modes and addresses mismatches between modes before and after cable force loss. To enhance the accuracy and reliability of the force evaluation, a robust sparse Bayesian learning method is proposed. Measurement noise is modeled as a mixture of Gaussian distributions rather than a single Gaussian distribution, enabling a more precise representation of uncertainties in force evaluation. Furthermore, a feedback-driven error optimization process is introduced to minimize residuals through multiple linear iterations. Numerical simulations demonstrate that the proposed method achieves greater evaluation accuracy compared to existing sparse Bayesian approaches. Comparative analyses under varying noise levels reveal that the proposed method is robust and effectively reduces the impact of measurement noise.
•A modal-based method for force evaluation of cable-strut structures is proposed.•A robust SBL method is introduced to enhance the accuracy and reliability.•Measurement noise is modeled as a mixture of Gaussian to capture the uncertainties.•An iterative error optimization is established to mitigate the effects of structural nonlinearity. |
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| AbstractList | Force evaluation is critical to ensuring the safety of cable-strut structures during service. This study employs dynamic testing to assess the internal forces resulting from cable relaxation in prestressed cable-strut structures. A cross-model cross-mode algorithm is utilized to establish a cable force evaluation model. This approach broadens the range of available modes and addresses mismatches between modes before and after cable force loss. To enhance the accuracy and reliability of the force evaluation, a robust sparse Bayesian learning method is proposed. Measurement noise is modeled as a mixture of Gaussian distributions rather than a single Gaussian distribution, enabling a more precise representation of uncertainties in force evaluation. Furthermore, a feedback-driven error optimization process is introduced to minimize residuals through multiple linear iterations. Numerical simulations demonstrate that the proposed method achieves greater evaluation accuracy compared to existing sparse Bayesian approaches. Comparative analyses under varying noise levels reveal that the proposed method is robust and effectively reduces the impact of measurement noise.
•A modal-based method for force evaluation of cable-strut structures is proposed.•A robust SBL method is introduced to enhance the accuracy and reliability.•Measurement noise is modeled as a mixture of Gaussian to capture the uncertainties.•An iterative error optimization is established to mitigate the effects of structural nonlinearity. |
| ArticleNumber | 119878 |
| Author | Zhou, Haodong Xie, Tianyu Sareh, Pooya Chen, Yao Shen, Zhengliang Gao, Jiangjun |
| Author_xml | – sequence: 1 givenname: Yao orcidid: 0000-0003-0924-8945 surname: Chen fullname: Chen, Yao email: chenyao@seu.edu.cn organization: Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, and National Prestress Engineering Research Center, Southeast University, Nanjing 211189, China – sequence: 2 givenname: Haodong surname: Zhou fullname: Zhou, Haodong organization: Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, and National Prestress Engineering Research Center, Southeast University, Nanjing 211189, China – sequence: 3 givenname: Jiangjun surname: Gao fullname: Gao, Jiangjun organization: China Construction Fifth Engineering Bureau Co., Ltd., Changsha 410000, China – sequence: 4 givenname: Zhengliang surname: Shen fullname: Shen, Zhengliang organization: China Construction Fifth Engineering Bureau Co., Ltd., Changsha 410000, China – sequence: 5 givenname: Tianyu surname: Xie fullname: Xie, Tianyu organization: Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, and National Prestress Engineering Research Center, Southeast University, Nanjing 211189, China – sequence: 6 givenname: Pooya surname: Sareh fullname: Sareh, Pooya organization: Creative Design Engineering Lab (Cdel), School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK |
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| Keywords | Uncertainty quantification Force evaluation Expectation–maximization algorithm Sparse Bayesian learning Prestressed cable-strut structure |
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| SubjectTerms | Expectation–maximization algorithm Force evaluation Prestressed cable-strut structure Sparse Bayesian learning Uncertainty quantification |
| Title | Accurate force evaluation in prestressed cable-strut structures: A robust sparse Bayesian learning method with feedback-driven error optimization |
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