Reliability-based optimization in structural engineering

Enevoldsen, I.; Sørensen, John Dalsgaard

doi:10.1016/0167-4730(94)90039-6

Cited by 484 publications

(143 citation statements)

References 22 publications

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“…The aim of this study is to obtain a free defects part with a good thickness distribution, decrease the risk of potential failures and to let the process insensitive to the input parameters variations. For more detailed description of the RBDO methodology and variety of frameworks the reader can be refer to the following references (Youn et al, 2003;Enevoldsen et al, 1994;El Hami et al, 2011). The RBDO problem can be generally formulated as:…”

Section: Reliability Analysismentioning

confidence: 99%

Hydroforming Process: Identification of the Material’s Characteristics and Reliability Analysis

Hami¹,

Radi²,

Cherouat³

2012

Metal Forming - Process, Tools, Design

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Section: Reliability Analysismentioning

confidence: 99%

Hydroforming Process: Identification of the Material’s Characteristics and Reliability Analysis

Hami¹,

Radi²,

Cherouat³

2012

Metal Forming - Process, Tools, Design

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“…There are multiple ways of formulating an RBDO problem (Enevoldsen and Sorensen 1994, Frangopol 1995, and Tu et al 1999. In its generic form, we seek the optimal vector of design variables…”

Section: Mathematical Formulation Of the Rbdo Problemmentioning

confidence: 99%

Integrated reliability and sizing optimization of a large composite structure

Eamon

Rais‐Rohani

2009

Marine Structures

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Eamon, C. D., and Rais-Rohani, M. (2009). "Integrated reliability and sizing optimization of a large composite structure." Marine Structures, 22(2), 315-334, doi: 10.1016/j.marstruc.2008.03 AbstractIn this paper, we present the application of probabilistic design modeling and reliability-based design optimization (RBDO) methodology to the sizing optimization of a composite advanced submarine sail structure under parametric uncertainty. With the help of probabilistic sensitivity analysis, the influence of individual random variables on each structural failure mode is examined, and the critical modes are treated as probabilistic design constraints under consistent lower bounds on the corresponding reliability indices. Whereas the failure modes are applied to structural components in the solution of the RBDO problem, the overall system reliability is also evaluated as a post-optimization step. The results indicate that in comparison to a deterministic optimum design, the structural mass of the probabilistic optimum design is slightly higher when consistent probabilistic constraints are imposed, and the overall structural stiffness is found to be more critical than individual component laminate ply thicknesses in meeting the specified design constraints. Moreover, the post-optimality analysis shows that the overall system failure probability of the probabilistic optimum design is more than 50% lower than that of the deterministic optimal design with less than 5% penalty in structural mass. ------------------------------

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“…Two level methods (i.e. Enevoldsen and Sorensen (1994), Tu et al (1999) Chen et al (1997), Kuschel and Rackwitz (2000). 3.…”

Section: Formulation Of the Reliability-based Design Optimization Promentioning

confidence: 99%

Reliability-based design optimization of composite stiffened panels in post-buckling regime

López

Bacarreza

Baldomir

et al. 2016

Struct Multidisc Optim

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This paper focuses on Deterministic and Reliability Based Design Optimization (DO and RBDO) of composite stiffened panels considering post-buckling regime and progressive failure analysis. The ultimate load that a post-buckled panel can hold is to be maximised by changing the stacking sequence of both skin and stringers composite layups. The RBDO problem looks for a design that collapses beyond the shortening of failure obtained in the DO phase with a target reliability while considering uncertainty in the elastic properties of the composite material. The RBDO algorithm proposed is decoupled and hence separates the Reliability Analysis (RA) from the deterministic optimization. The main code to drive both the DO and RBDO approaches is written in MATLAB and employs Genetic Algorithms (GA) to solve the DO loops because discrete design variables and highly nonlinear response functions are expected. The code is linked with Abaqus to perform parallel explicit nonlinear finite element analyses in order to obtain the structural responses at each generation. The RA is solved through an inverse Most Probable failure Point (MPP) search algorithm that benefits from a Poly-

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Reliability-based optimization in structural engineering

Cited by 484 publications

References 22 publications

Hydroforming Process: Identification of the Material’s Characteristics and Reliability Analysis

Hydroforming Process: Identification of the Material’s Characteristics and Reliability Analysis

Integrated reliability and sizing optimization of a large composite structure

Reliability-based design optimization of composite stiffened panels in post-buckling regime

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