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Reliability analysis of tunnels using a metamodeling technique based on augmented radial basis functions
Graphical abstract Display Omitted
Highlights A reliability analysis method of tunnels using augmented RBFs was developed. Two tunnel examples including closed-form equations and FE analyses were solved. Various sample sizes were tested and failure probabilities were calculated. The proposed reliability analysis method was found to be efficient and accurate. The method provides a useful tool when expensive response simulations are required.
Abstract Metamodeling techniques have been developed and used for years in engineering reliability analysis involving expensive response simulations. In practical tunnel engineering problems where finite element (FE) simulations are required, the limited state/performance functions are in general implicit and nonlinear, and it is difficult to apply traditional gradient-based or sampling-based reliability methods, especially for large-scale problems. There is a need to develop accurate and efficient metamodels for practical tunnel engineering applications. In this paper, a metamodeling technique for reliability analysis of tunnels was studied based on augmented radial basis functions (RBFs). With a relatively small size of samples, the RBFs were used to create accurate approximate functions for different types of responses including linear and higher-order nonlinear functions. With the RBF-based metamodel constructed to express a limit state/performance function, Monte Carlo simulations (MCS) were applied to evaluate failure probability. The failure probability and reliability index obtained using the RBF-based metamodeling method were found to have good accuracy with a reasonable number of sample points. The reliability analyses of two existing tunnel examples showed that the augmented RBF metamodeling approach was efficient and effective for tunnel engineering problems.
Reliability analysis of tunnels using a metamodeling technique based on augmented radial basis functions
Graphical abstract Display Omitted
Highlights A reliability analysis method of tunnels using augmented RBFs was developed. Two tunnel examples including closed-form equations and FE analyses were solved. Various sample sizes were tested and failure probabilities were calculated. The proposed reliability analysis method was found to be efficient and accurate. The method provides a useful tool when expensive response simulations are required.
Abstract Metamodeling techniques have been developed and used for years in engineering reliability analysis involving expensive response simulations. In practical tunnel engineering problems where finite element (FE) simulations are required, the limited state/performance functions are in general implicit and nonlinear, and it is difficult to apply traditional gradient-based or sampling-based reliability methods, especially for large-scale problems. There is a need to develop accurate and efficient metamodels for practical tunnel engineering applications. In this paper, a metamodeling technique for reliability analysis of tunnels was studied based on augmented radial basis functions (RBFs). With a relatively small size of samples, the RBFs were used to create accurate approximate functions for different types of responses including linear and higher-order nonlinear functions. With the RBF-based metamodel constructed to express a limit state/performance function, Monte Carlo simulations (MCS) were applied to evaluate failure probability. The failure probability and reliability index obtained using the RBF-based metamodeling method were found to have good accuracy with a reasonable number of sample points. The reliability analyses of two existing tunnel examples showed that the augmented RBF metamodeling approach was efficient and effective for tunnel engineering problems.
Reliability analysis of tunnels using a metamodeling technique based on augmented radial basis functions
Wang, Qian (author) / Fang, Hongbing (author) / Shen, Lin (author)
Tunnelling and Underground Space Technology ; 56 ; 45-53
2016-02-15
9 pages
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2016
| British Library Online Contents | 2016
| British Library Online Contents | 2016