Abstract
The first-order reliability method (FORM) can provide useful information such as reliability index and design point for a reliability based design of slopes. Thus practical FORM-based slope reliability analysis is in great demand for practicing engineers. One difficulty for FORM-based slope reliability analysis is that the slope problem can be very complicated and some standalone numerical packages which are not designed with probabilistic considerations have to be resorted to. Another difficulty lies in the dealing with correlated non-Gaussian random variables which usually involves mathematical transformation of the random variables to their independent standard normal counterparts (in u-space). In the literature, the first difficulty is usually resolved by the use of a surrogate model to estimate the complicated (usually implicit) limit state function (LSF), and the second is resolved by developing FORM algorithms that can be directly implemented in the original space of random variables (x-space, non-Gaussian type), for example, with the Low and Tang's constrained optimization approach. Then a question is can we have a practical FORM algorithm in x-space that can be easily integrated with standalone numerical packages with no surrogate models? To answer this question, this paper presents a simplified HLRF algorithm for FORM. Naturally, the new algorithm is simply formulated in x-space and requires neither transformation of correlated random variables nor optimization tools. The algorithm is particularly useful for reliability analysis involving correlated nonnormals subjected to implicit LSF. The convergence of the algorithm can be easily improved by adjusting the step length during the iteration. It is first verified using a simple example with closed-form solution. Through perturbation analysis in x-space, it is then illustrated for case studies of earth slope reliability with both limit equilibrium and finite element analyses.
| Original language | English |
|---|---|
| Title of host publication | Geo-Risk 2017 |
| Publisher | American Society of Civil Engineers |
| Pages | 86-98 |
| Number of pages | 13 |
| Edition | GSP 283 |
| DOIs | |
| Publication status | Published - 1 Jan 2017 |
| Event | Geo-Risk 2017 : Reliability-Based Design and Code Developments - Denver, United States of America Duration: 4 Jun 2017 → 7 Jun 2017 |
Publication series
| Name | Geotechnical Special Publication |
|---|---|
| ISSN (Print) | 0895-0563 |
Conference
| Conference | Geo-Risk 2017 |
|---|---|
| Country | United States of America |
| City | Denver |
| Period | 4/06/17 → 7/06/17 |
Cite this
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A Practical HLRF Algorithm for Slope Reliability Analysis. / Ji, Jian; Kodikara, Jayantha.
Geo-Risk 2017. GSP 283. ed. American Society of Civil Engineers, 2017. p. 86-98 (Geotechnical Special Publication).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review
TY - GEN
T1 - A Practical HLRF Algorithm for Slope Reliability Analysis
AU - Ji, Jian
AU - Kodikara, Jayantha
PY - 2017/1/1
Y1 - 2017/1/1
N2 - The first-order reliability method (FORM) can provide useful information such as reliability index and design point for a reliability based design of slopes. Thus practical FORM-based slope reliability analysis is in great demand for practicing engineers. One difficulty for FORM-based slope reliability analysis is that the slope problem can be very complicated and some standalone numerical packages which are not designed with probabilistic considerations have to be resorted to. Another difficulty lies in the dealing with correlated non-Gaussian random variables which usually involves mathematical transformation of the random variables to their independent standard normal counterparts (in u-space). In the literature, the first difficulty is usually resolved by the use of a surrogate model to estimate the complicated (usually implicit) limit state function (LSF), and the second is resolved by developing FORM algorithms that can be directly implemented in the original space of random variables (x-space, non-Gaussian type), for example, with the Low and Tang's constrained optimization approach. Then a question is can we have a practical FORM algorithm in x-space that can be easily integrated with standalone numerical packages with no surrogate models? To answer this question, this paper presents a simplified HLRF algorithm for FORM. Naturally, the new algorithm is simply formulated in x-space and requires neither transformation of correlated random variables nor optimization tools. The algorithm is particularly useful for reliability analysis involving correlated nonnormals subjected to implicit LSF. The convergence of the algorithm can be easily improved by adjusting the step length during the iteration. It is first verified using a simple example with closed-form solution. Through perturbation analysis in x-space, it is then illustrated for case studies of earth slope reliability with both limit equilibrium and finite element analyses.
AB - The first-order reliability method (FORM) can provide useful information such as reliability index and design point for a reliability based design of slopes. Thus practical FORM-based slope reliability analysis is in great demand for practicing engineers. One difficulty for FORM-based slope reliability analysis is that the slope problem can be very complicated and some standalone numerical packages which are not designed with probabilistic considerations have to be resorted to. Another difficulty lies in the dealing with correlated non-Gaussian random variables which usually involves mathematical transformation of the random variables to their independent standard normal counterparts (in u-space). In the literature, the first difficulty is usually resolved by the use of a surrogate model to estimate the complicated (usually implicit) limit state function (LSF), and the second is resolved by developing FORM algorithms that can be directly implemented in the original space of random variables (x-space, non-Gaussian type), for example, with the Low and Tang's constrained optimization approach. Then a question is can we have a practical FORM algorithm in x-space that can be easily integrated with standalone numerical packages with no surrogate models? To answer this question, this paper presents a simplified HLRF algorithm for FORM. Naturally, the new algorithm is simply formulated in x-space and requires neither transformation of correlated random variables nor optimization tools. The algorithm is particularly useful for reliability analysis involving correlated nonnormals subjected to implicit LSF. The convergence of the algorithm can be easily improved by adjusting the step length during the iteration. It is first verified using a simple example with closed-form solution. Through perturbation analysis in x-space, it is then illustrated for case studies of earth slope reliability with both limit equilibrium and finite element analyses.
UR - http://www.scopus.com/inward/record.url?scp=85030466697&partnerID=8YFLogxK
U2 - 10.1061/9780784480700.009
DO - 10.1061/9780784480700.009
M3 - Conference Paper
AN - SCOPUS:85030466697
T3 - Geotechnical Special Publication
SP - 86
EP - 98
BT - Geo-Risk 2017
PB - American Society of Civil Engineers
ER -