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Stochastic analysis of railway embankment with uncertain soil parameters using polynomial chaos expansion
This paper focuses on the stochastic response of railway embankments considering the uncertainties in soil cohesion and friction angle. The non-sampling stochastic method concerning generalised polynomial chaos (gPC) expansion was employed for the dynamic numerical simulation. The uncertain parameters, including soil cohesion and friction angle, were defined by the truncated gPC expansions. Furthermore, the system’s response, namely, the displacement and acceleration of different embankment sections, was presented by the gPC expansion with unknown deterministic coefficients. The stochastic Galerkin projection was used to calculate a set of deterministic equations. The unknown gPC coefficients of the system’s response were determined by a non-intrusive solution as a set of collocation points. In addition, the results of these analyses were compared with classical Monte Carlo simulations. It is essential to note that although only a few collocation points have been used, the results are in good agreement with the MC sampling method. One of the main objectives of this study is to demonstrate the accuracy of the results and the time efficiency of the proposed non-sampling method in quantifying the uncertainty of stochastic systems compared to the sampling procedure (e.g. Monte Carlo simulation).
Stochastic analysis of railway embankment with uncertain soil parameters using polynomial chaos expansion
This paper focuses on the stochastic response of railway embankments considering the uncertainties in soil cohesion and friction angle. The non-sampling stochastic method concerning generalised polynomial chaos (gPC) expansion was employed for the dynamic numerical simulation. The uncertain parameters, including soil cohesion and friction angle, were defined by the truncated gPC expansions. Furthermore, the system’s response, namely, the displacement and acceleration of different embankment sections, was presented by the gPC expansion with unknown deterministic coefficients. The stochastic Galerkin projection was used to calculate a set of deterministic equations. The unknown gPC coefficients of the system’s response were determined by a non-intrusive solution as a set of collocation points. In addition, the results of these analyses were compared with classical Monte Carlo simulations. It is essential to note that although only a few collocation points have been used, the results are in good agreement with the MC sampling method. One of the main objectives of this study is to demonstrate the accuracy of the results and the time efficiency of the proposed non-sampling method in quantifying the uncertainty of stochastic systems compared to the sampling procedure (e.g. Monte Carlo simulation).
Stochastic analysis of railway embankment with uncertain soil parameters using polynomial chaos expansion
Mohammadi, Mohammadreza (author) / Mosleh, Araliya (author) / Razzaghi, Mehran (author) / Alves Costa, Pedro (author) / Calçada, Rui (author)
Structure and Infrastructure Engineering ; 19 ; 1425-1444
2023-10-03
20 pages
Article (Journal)
Electronic Resource
Unknown
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