TY - JOUR
T1 - Reliability-based topology optimization using a new method for sensitivity approximation - application to ground structures
AU - Liu, Ke
AU - Paulino, Glaucio H.
AU - Gardoni, Paolo
N1 - Funding Information:
We acknowledge support from the US NSF (National Science Foundation) through Grants 1321661 and 1437535. In addition, Ke Liu acknowledges support of the China Scholarship Council (CSC), and Glaucio H. Paulino acknowledges support of the Raymond Allen Jones Chair at the Georgia Institute of Technology. The authors would like to extend their appreciation to Prof. Krister Svanberg for providing a copy of his MMA (Method of Moving Asymptotes) code, and to Dr. Tomas Zegard for providing his 3D plotting subroutine in MATLAB which was used to prepare some of the figures in this paper.
Publisher Copyright:
© 2016, Springer-Verlag Berlin Heidelberg.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - This paper proposes an efficient gradient-based optimization approach for reliability-based topology optimization of structures under uncertainties. Our objective is to find the optimized topology of structures with minimum weight which also satisfy certain reliability requirements. In the literature, those problems are primarily performed with approaches that use a first-order reliability method (FORM) to estimate the gradient of the probability of failure. However, these approaches may lead to deficient or even invalid results because the gradient of probabilistic constraints, calculated by first order approximation, might not be sufficiently accurate. To overcome this issue, a newly developed segmental multi-point linearization (SML) method is employed in the optimization approach for a more accurate estimation of the gradient of failure probability. Meanwhile, this implementation also improves the approximation of the probability evaluation at no extra cost. In general, adoption of the SML method leads to a more accurate and robust approach. Numerical examples show that the new approach, based on the SML method, is numerically stable and usually provides optimized structures that have more of the desired features than conventional FORM-based approaches. The present approach typically does not lead to a fully stressed design, and thus this feature will be verified by numerical examples.
AB - This paper proposes an efficient gradient-based optimization approach for reliability-based topology optimization of structures under uncertainties. Our objective is to find the optimized topology of structures with minimum weight which also satisfy certain reliability requirements. In the literature, those problems are primarily performed with approaches that use a first-order reliability method (FORM) to estimate the gradient of the probability of failure. However, these approaches may lead to deficient or even invalid results because the gradient of probabilistic constraints, calculated by first order approximation, might not be sufficiently accurate. To overcome this issue, a newly developed segmental multi-point linearization (SML) method is employed in the optimization approach for a more accurate estimation of the gradient of failure probability. Meanwhile, this implementation also improves the approximation of the probability evaluation at no extra cost. In general, adoption of the SML method leads to a more accurate and robust approach. Numerical examples show that the new approach, based on the SML method, is numerically stable and usually provides optimized structures that have more of the desired features than conventional FORM-based approaches. The present approach typically does not lead to a fully stressed design, and thus this feature will be verified by numerical examples.
KW - Reliability analysis
KW - Reliability-based topology optimization
KW - Sensitivity analysis
KW - Topology optimization
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U2 - 10.1007/s00158-016-1422-5
DO - 10.1007/s00158-016-1422-5
M3 - Article
AN - SCOPUS:84961999147
SN - 1615-147X
VL - 54
SP - 553
EP - 571
JO - Structural and Multidisciplinary Optimization
JF - Structural and Multidisciplinary Optimization
IS - 3
ER -