Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations
Stochastic nonlinear dependencies have been reported extensively between different uncertain parameters or in their time or spatial variance. However, the description of dependency is commonly not provided except a linear correlation. The structural reliability incorporating nonlinear dependencies t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/8836330 |
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| author | Liulin Kong Heng Li Bo Zhang Hanbin Luo |
| author_facet | Liulin Kong Heng Li Bo Zhang Hanbin Luo |
| author_sort | Liulin Kong |
| collection | DOAJ |
| description | Stochastic nonlinear dependencies have been reported extensively between different uncertain parameters or in their time or spatial variance. However, the description of dependency is commonly not provided except a linear correlation. The structural reliability incorporating nonlinear dependencies thus needs to be addressed based on the linear correlations. This paper first demonstrates the capture of nonlinear dependency by fitting various bivariate non-Gaussian copulas to limited data samples of structural material properties. The vine copula model is used to enable a flexible modeling of multiple nonlinear dependencies by mapping the linear correlations into the non-Gaussian copula parameters. A sequential search strategy is applied to achieve the estimate of numerous copula parameters, and a simplified algorithm is further designed for reliability involving stationary stochastic processes. The subset simulation is then adopted to efficiently generate random variables from the corresponding distribution for high reliability evaluation. Two examples including a frame structure with different stochastic material properties and a cantilever beam with spatially variable stochastic modulus are investigated to discuss the possible effects of nonlinear dependency on structural reliability. Since the dependency can be determined qualitatively from limited data, the proposed method provides a feasible way for reliability evaluation with prescriptions on correlated stochastic parameters. |
| format | Article |
| id | doaj-art-527605e359df48e4a3686278c9ae4345 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-527605e359df48e4a3686278c9ae43452025-02-03T06:12:48ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/88363308836330Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear CorrelationsLiulin Kong0Heng Li1Bo Zhang2Hanbin Luo3Faculty of Engineering, China University of Geosciences (Wuhan), Wuhan, Hubei 430074, ChinaDepartment of Building and Real Estate, The Hong Kong Polytechnic University, Kowloon, Hong KongDepartment of Building and Real Estate, The Hong Kong Polytechnic University, Kowloon, Hong KongDepartment of Construction Management, School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, ChinaStochastic nonlinear dependencies have been reported extensively between different uncertain parameters or in their time or spatial variance. However, the description of dependency is commonly not provided except a linear correlation. The structural reliability incorporating nonlinear dependencies thus needs to be addressed based on the linear correlations. This paper first demonstrates the capture of nonlinear dependency by fitting various bivariate non-Gaussian copulas to limited data samples of structural material properties. The vine copula model is used to enable a flexible modeling of multiple nonlinear dependencies by mapping the linear correlations into the non-Gaussian copula parameters. A sequential search strategy is applied to achieve the estimate of numerous copula parameters, and a simplified algorithm is further designed for reliability involving stationary stochastic processes. The subset simulation is then adopted to efficiently generate random variables from the corresponding distribution for high reliability evaluation. Two examples including a frame structure with different stochastic material properties and a cantilever beam with spatially variable stochastic modulus are investigated to discuss the possible effects of nonlinear dependency on structural reliability. Since the dependency can be determined qualitatively from limited data, the proposed method provides a feasible way for reliability evaluation with prescriptions on correlated stochastic parameters.http://dx.doi.org/10.1155/2021/8836330 |
| spellingShingle | Liulin Kong Heng Li Bo Zhang Hanbin Luo Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations Advances in Civil Engineering |
| title | Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations |
| title_full | Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations |
| title_fullStr | Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations |
| title_full_unstemmed | Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations |
| title_short | Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations |
| title_sort | estimation of high structural reliability involving nonlinear dependencies based on linear correlations |
| url | http://dx.doi.org/10.1155/2021/8836330 |
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