Reliability-based design and sensitivity analysis of rock armors for rubble-mound breakwater

Author(s):  
Mojtaba Karimaei Tabarestani ◽  
Atabak Feizi ◽  
Meysam Bali
1988 ◽  
Vol 1 (21) ◽  
pp. 153
Author(s):  
Masato Yamamoto ◽  
Kazumasa Mizumura ◽  
Taiji Endo ◽  
Naofumi Shiraishi

The object of this present research is to study probabilistic design of armor blocks protecting composite breakwaters and to produce optimum design methodology for S-shaped breakwaters in terms of failure probability and construction cost. Failure probability in the vicinity of the still water level is greatest in the case of uniform sloped breakwaters. Therefore,S-shaped breakwaters of which the slope near the still water level is milder have a reduced risk of damage compared to uniform sloped ones. The optimum design index presents good economics and reliability in rubble-mound breakwater design.


Author(s):  
Masato Yamamoto ◽  
Kazumasa Mizumura ◽  
Taiji Endo ◽  
Naofumi Shiraishi

Author(s):  
Y M Zhang ◽  
X D He ◽  
Q L Liu ◽  
B C Wen ◽  
J X Zheng

Techniques from the perturbation method, the Edgeworth series, the reliability-based design theory, and the sensitivity analysis approach are employed to present a practical and efficient method for the reliability sensitivity of automobile components with arbitrary distribution parameters. On the condition of first four moments of original random variables known, the reliability sensitivity theory and case studies are researched. The respective program can be used to obtain the reliability sensitivity of automobile components with arbitrary distribution parameters accurately and quickly.


2021 ◽  
Vol 11 (10) ◽  
pp. 4708
Author(s):  
Junho Chun

Structural optimization aims to achieve a structural design that provides the best performance while satisfying the given design constraints. When uncertainties in design and conditions are taken into account, reliability-based design optimization (RBDO) is adopted to identify solutions with acceptable failure probabilities. This paper outlines a method for sensitivity analysis, reliability assessment, and RBDO for structures. Complex-step (CS) approximation and the first-order reliability method (FORM) are unified in the sensitivity analysis of a probabilistic constraint, which streamlines the setup of optimization problems and enhances their implementation in RBDO. Complex-step approximation utilizes an imaginary number as a step size to compute the first derivative without subtractive cancellations in the formula, which have been observed to significantly affect the accuracy of calculations in finite difference methods. Thus, the proposed method can select a very small step size for the first derivative to minimize truncation errors, while achieving accuracy within the machine precision. This approach integrates complex-step approximation into the FORM to compute sensitivity and assess reliability. The proposed method of RBDO is tested on structural optimization problems across a range of statistical variations, demonstrating that performance benefits can be achieved while satisfying precise probabilistic constraints.


2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Sangjune Bae ◽  
Nam H. Kim ◽  
Seung-gyo Jang

This paper presents a tradeoff between shifting design and controlling sampling uncertainty in system reliability-based design optimization (RBDO) using the Bayesian network. The sampling uncertainty is caused by a finite number of samples used in calculating the reliability of a component, and it propagates to the system reliability. A conservative failure probability is utilized to consider sampling uncertainty. In this paper, the sensitivity of a conservative system failure probability is derived with respect to the design change and the number of samples in a component using Bayesian network along with global sensitivity analysis (GSA). In the sensitivity analysis, GSA is used for local sensitivity calculation. The numerical results show that sampling uncertainty can significantly affect the conservative system reliability and needs to be controlled to achieve the desired level of system reliability. Numerical examples show that both shifting design and reducing sampling uncertainty are crucial in the system RBDO.


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