TY - JOUR

T1 - On the use of Directional Importance Sampling for reliability-based design and optimum design sensitivity of linear stochastic structures

AU - Jerez, Danko J.

AU - Jensen, Héctor A.

AU - Valdebenito, Marcos A.

AU - Misraji, Mauricio A.

AU - Mayorga, Franco

AU - Beer, Michael

N1 - Funding Information: This research is partially supported by ANID (National Agency for Research and Development, Chile) under its program FONDECYT, grant numbers 1200087 and 1180271 . Also, this research has been partially supported by ANID, Chile and DAAD (German Academic Exchange Service, Germany) under CONICYT-PFCHA/Doctorado Acuerdo Bilateral DAAD Becas Chile/ 2018-62180007 . These supports are gratefully acknowledged by the authors.

PY - 2022/10

Y1 - 2022/10

N2 - This contribution focuses on reliability-based design and optimum design sensitivity of linear dynamical structural systems subject to Gaussian excitation. Directional Importance Sampling (DIS) is implemented for reliability assessment, which allows to obtain first-order derivatives of the failure probabilities as a byproduct of the sampling process. Thus, gradient-based solution schemes can be adopted by virtue of this feature. In particular, a class of feasible-direction interior point algorithms are implemented to obtain optimum designs, while a direction-finding approach is considered to obtain optimum design sensitivity measures as a post-processing step of the optimization results. To show the usefulness of the approach, an example involving a building structure is studied. Overall, the reliability sensitivity analysis framework enabled by DIS provides a potentially useful tool to address a practical class of design optimization problems.

AB - This contribution focuses on reliability-based design and optimum design sensitivity of linear dynamical structural systems subject to Gaussian excitation. Directional Importance Sampling (DIS) is implemented for reliability assessment, which allows to obtain first-order derivatives of the failure probabilities as a byproduct of the sampling process. Thus, gradient-based solution schemes can be adopted by virtue of this feature. In particular, a class of feasible-direction interior point algorithms are implemented to obtain optimum designs, while a direction-finding approach is considered to obtain optimum design sensitivity measures as a post-processing step of the optimization results. To show the usefulness of the approach, an example involving a building structure is studied. Overall, the reliability sensitivity analysis framework enabled by DIS provides a potentially useful tool to address a practical class of design optimization problems.

KW - Directional Importance Sampling

KW - First excursion probability

KW - Gaussian loading

KW - Interior point algorithm

KW - Linear structures

KW - Optimum design sensitivity

KW - Structural design

UR - http://www.scopus.com/inward/record.url?scp=85139864510&partnerID=8YFLogxK

U2 - 10.1016/j.probengmech.2022.103368

DO - 10.1016/j.probengmech.2022.103368

M3 - Article

AN - SCOPUS:85139864510

VL - 70

JO - Probabilistic Engineering Mechanics

JF - Probabilistic Engineering Mechanics

SN - 0266-8920

M1 - 103368

ER -