Reliability-based optimal design software for earthquake engineering applications

2007 ◽  
Vol 34 (7) ◽  
pp. 856-869 ◽  
Author(s):  
Hong Liang ◽  
Terje Haukaas ◽  
Johannes O Royset
Keyword(s):  
Finite Element ◽  
Design Optimization ◽  
Seismic Design ◽  
Earthquake Engineering ◽  
Structural Response ◽  
Object Oriented ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Nonlinear Structural ◽  
Nonlinear Structural Response

This paper describes a functional tool for engineers to make rational design decisions by balancing cost and safety. Focus is on seismic design, in which nonlinear structural response must be considered. For this purpose, we implement and apply a state-of-the-art algorithm for reliability-based design optimization. The work extends the OpenSees software, which is rapidly gaining users in the earthquake engineering community. Consequently, design optimization with sophisticated nonlinear finite element models of real structures is possible. An object-oriented software architecture is employed that focuses on maintainability and extensibility of the software. This approach also offers flexibility in the choice of optimization and reliability methods for each specific problem, supported by the decoupled nature of the optimization algorithm. Our work utilizes and extends the existing tools for structural reliability analysis in OpenSees. In particular, we employ response sensitivities that are computed within the finite element code by direct differentiation. The implementation is tested through case studies with nonlinear structural response. Discontinuous response gradients are overcome by use of fibre cross sections and smoothed material models. The numerical examples include the seismic design optimization of a six-storey, three-bay, reinforced concrete building. Key words: reliability-based design optimization, nonlinear finite elements, earthquake engineering, object-oriented software development, OpenSees.

An Efficient Re-Analysis Methodology for Probabilistic Vibration of Large-Scale Structures

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
Geng Zhang ◽  
Efstratios Nikolaidis ◽  
Zissimos P. Mourelatos
Keyword(s):  
Finite Element ◽  
Design Optimization ◽  
Reliability Analysis ◽  
Probabilistic Analysis ◽  
Large Scale ◽  
Analysis Method ◽  
Reliability Based Design Optimization ◽  
Large Scale Structures ◽  
Reliability Based Design ◽  
Scale Structures

Probabilistic analysis and design of large-scale structures requires repeated finite-element analyses of large models, and each analysis is expensive. This paper presents a methodology for probabilistic analysis and reliability-based design optimization of large-scale structures that consists of two re-analysis methods, one for estimating the deterministic vibratory response and another for estimating the probability of the response exceeding a certain level. The deterministic re-analysis method can analyze efficiently large-scale finite-element models consisting of tens or hundreds of thousand degrees of freedom and design variables that vary in a wide range. The probabilistic re-analysis method calculates very efficiently the system reliability for different probability distributions of the random variables by performing a single Monte Carlo simulation of one design. The methodology is demonstrated on probabilistic vibration analysis and reliability-based design optimization of a realistic vehicle model. It is shown that the computational cost of the proposed re-analysis method for a single reliability analysis is about 1/20 of the cost of the same analysis using MSC/NASTRAN. Moreover, the probabilistic re-analysis approach enables a designer to perform reliability-based design optimization of the vehicle at a cost almost equal to that of a single reliability analysis. Without using the probabilistic re-analysis approach, it would be impractical to perform reliability-based design optimization of the vehicle.

An Efficient Re-Analysis Methodology for Probabilistic Vibration of Large-Scale Structures

2008 ◽  
Author(s):  
Geng Zhang ◽  
Efstratios Nikolaidis ◽  
Zissimos P. Mourelatos
Keyword(s):  
Finite Element ◽  
Design Optimization ◽  
Reliability Analysis ◽  
Probabilistic Analysis ◽  
Large Scale ◽  
Reliability Based Design Optimization ◽  
Large Scale Structures ◽  
Reliability Based Design ◽  
Design Variables ◽  
Scale Structures

It is challenging to perform probabilistic analysis and design of large-scale structures because it requires repeated finite-element analyses of large models and each analysis is expensive. This paper presents a methodology for probabilistic analysis and reliability-based design optimization of large-scale structures that consists of two re-analysis methods; one for estimating the deterministic vibratory response and another for estimating the probability of the response exceeding a certain level. Deterministic re-analysis can analyze efficiently large-scale finite element models consisting of tens or hundreds of thousand degrees of freedom and large numbers of design variables that vary in a wide range. Probabilistic re-analysis calculates very efficiently the system reliability for different probability distributions of the design variables by performing a single Monte Carlo simulation. The methodology is demonstrated on probabilistic vibration analysis and a reliability-based design optimization of a realistic vehicle model. It is shown that computational cost of the proposed reanalysis method for a single reliability analysis is about 1/20th of the cost of the same analysis using NASTRAN. Moreover, the probabilistic re-analysis approach enables a designer to perform reliability-based design optimization of the vehicle at a cost almost equal to that of a single reliability analysis. Without using the probabilistic re-analysis approach, it would be impractical to perform reliability-based design optimization of the vehicle.

Reliability Based Design Optimization of Primary Shield Structure Consisting of Steel-Plate Composite (SC) Walls Under Seismic Load

2017 ◽  
Author(s):  
Arindam Chakraborty ◽  
Satish Krishnasamy Radha ◽  
Kadir C. Sener ◽  
Amit H. Varma
Keyword(s):  
Finite Element ◽  
Design Optimization ◽  
Steel Plate ◽  
Seismic Load ◽  
Reliability Based Design Optimization ◽  
3D Fem ◽  
Fem Model ◽  
Finite Element Software ◽  
Reliability Based Design ◽  
Purdue University

This paper presents a reliability based design optimization (RBDO) of a primary shield wall (PSW) specimen consisting of steel-plate composite (SC) walls scaled from a typical pressurized water reactor (PWR) nuclear power plant under seismic loads. The PSW structure consists of thick SC wall segments with complex and irregular geometry that surround the central reactor vessel’s cavity. Previously, researchers at Purdue University have developed a 3D finite element model (FEM) model of the well-established experimental test setup of a 1/6th scale PSW specimen and the seismic load-deformation behavior was simulated. This paper extends the efforts of the researchers at Purdue University, through RBDO, thereby using the same 3D FEM model. For the simplicity of RBDO, only monotonic loading is considered and response surface method (RSM) is used for approximating the response of the 3D FEM model. Yielding of steel due to tension and/or shear is being considered as a milestone failure mode. The thicknesses of steel plate thicknesses are being considered as the objective to be optimized. The yield stress of steel, applied displacement and steel plate thicknesses are considered to be normal random variables. General purpose finite element software Abaqus (FEA) along with process automation and design exploration software Isight, both developed by Dassault Systèmes Simulia, are being used for the RBDO.

An Adaptive Directional Boundary Sampling Method for Efficient Reliability-Based Design Optimization

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Zeng Meng ◽  
Dequan Zhang ◽  
Zhaotao Liu ◽  
Gang Li
Keyword(s):  
Design Optimization ◽  
Computational Efficiency ◽  
Sampling Method ◽  
Engineering Application ◽  
Structural Response ◽  
Kriging Model ◽  
Reliability Based Design Optimization ◽  
Computational Accuracy ◽  
Reliability Based Design ◽  
Sample Points

Due to the nested optimization loop structure and time-demanding computation of structural response, the computational accuracy and cost of reliability-based design optimization (RBDO) have become a challenging issue in engineering application. Kriging-model-based approach is an effective tool to improve the computational efficiency in the practical RBDO problems; however, a larger number of sample points are required for meeting high computational accuracy requirements in traditional methods. In this paper, an adaptive directional boundary sampling (ADBS) method is developed in order to greatly reduce the computational sample points with a reasonable accuracy, in which the sample points are added along the ideal descending direction of objective function. Furthermore, only sample points located near the constraint boundary are mainly selected in the vicinity of the optimum point according to the strategy of multi-objective optimization; thus, substantial number of sample points located in the failure region is neglected, resulting in the improved performance of computational efficiency. Four numerical examples and one engineering application are provided for demonstrating the efficiency and accuracy of the proposed sampling method.

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