Seismic Reliability Analysis in the Framework of Metamodelling Based Monte Carlo Simulation

2017 ◽  
pp. 192-208 ◽  
Author(s):  
Shyamal Ghosh ◽  
Soham Mitra ◽  
Swarup Ghosh ◽  
Subrata Chakraborty
Keyword(s):  
Least Squares ◽  
Reliability Analysis ◽  
Response Surface ◽  
Seismic Response ◽  
Least Squares Method ◽  
Response Prediction ◽  
Seismic Intensity ◽  
Seismic Reliability ◽  
Moving Least Squares Method ◽  
Artificial Neural Network Ann

A comparative study of various metamodelling approaches namely the least squares method (LSM), moving least squares method (MLSM) and artificial neural network (ANN) based response surface method (RSM) are presented to demonstrate the effectiveness to approximate the nonlinear dynamic response of structure required for efficient seismic reliability analysis (SRA) of structures. The seismic response approximation by the LSM, MLSM and ANN based RSMs are explained with a brief note on the important issue of ground motion bin generation. The procedure adopted herein for SRA is based on the dual response surface approach. In doing so, the repetition of seismic intensity for SRA at different intensity levels is avoided by including this as one of the predictors in the seismic response prediction model. A nonlinear SDOF system has been taken up to elucidate the effectiveness of various metamodels in SRA.

Seismic reliability analysis of reinforced concrete bridge pier using efficient response surface method–based simulation

2018 ◽  
Vol 21 (15) ◽  
pp. 2326-2339 ◽  
Author(s):  
Shyamal Ghosh ◽  
Swarup Ghosh ◽  
Subrata Chakraborty
Keyword(s):  
Least Squares ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Least Squares Method ◽  
Time History ◽  
Bridge Pier ◽  
Direct Monte Carlo Simulation ◽  
Seismic Reliability ◽  
Surface Method

Seismic reliability analysis of bridge structures during and succeeding an earthquake event is of significant importance. The more accurate and robust approach of seismic reliability analysis is based on direct Monte Carlo simulation technique. But it is computationally challenging due to the requirement of large number of nonlinear time history analyses. The response surface method–based metamodeling approach is a viable alternative in such situation. This study explores the advantage of moving least squares method–based adaptive response surface method compared to the usually applied least squares method–based response surface method for improved seismic reliability analysis of multi-span bridge pier. The nonlinear time history analyses of the bridge pier are performed in the OpenSees with fibre sections considering a ground motion bin corresponding to the specified hazard level of the bridge site. The seismic reliability analysis results obtained by the usual least squares method and the proposed moving least squares method–based response surface method are compared with that of obtained by more accurate direct Monte Carlo simulation technique to elucidate the effectiveness of the proposed approach.

RESPONSE SURFACE MODEL USING MOVING LEAST SQUARES METHOD

2000 ◽  
Vol 2000.4 (0) ◽  
pp. 181-186
Author(s):  
Akihiro KAMINAGA ◽  
Katsuyuki SUZUKI ◽  
Daiji FUJII ◽  
Hideomi OHTSUBO
Keyword(s):  
Least Squares ◽  
Response Surface ◽  
Least Squares Method ◽  
Response Surface Model ◽  
Moving Least Squares ◽  
Surface Model ◽  
Moving Least Squares Method

Efficient Response Surface Modeling by Using Moving Least-Squares Method and Sensitivity

AIAA Journal ◽  
2005 ◽  
Vol 43 (11) ◽  
pp. 2404-2411 ◽  
Author(s):  
Chwail Kim ◽  
Semyung Wang ◽  
Kyung K. Choi
Keyword(s):  
Least Squares ◽  
Response Surface ◽  
Least Squares Method ◽  
Surface Modeling ◽  
Moving Least Squares ◽  
Response Surface Modeling ◽  
Moving Least Squares Method

Strength design of a knuckle component using moving least-squares response-surface-based approximate optimization methods

2009 ◽  
Vol 223 (8) ◽  
pp. 1019-1032 ◽  
Author(s):  
C Y Song ◽  
J-S Lee
Keyword(s):  
Least Squares ◽  
Response Surface ◽  
Least Squares Method ◽  
Optimization Methods ◽  
Moving Least Squares ◽  
Element Analysis ◽  
Cross Sectional ◽  
Approximate Optimization ◽  
Strength Design ◽  
Moving Least Squares Method

The paper deals with the strength design of an automotive knuckle component under bump and brake loading conditions. The design problem is formulated such that cross-sectional sizing variables are determined by minimizing the weight of a knuckle component subject to stresses, deformations, and frequency constraints. The initial design model is generated on the basis of an actual vehicle specification. The finite element analysis is conducted using ABAQUS, and optimal solutions are obtained via the moving least-squares method (MLSM) in the context of response-surface-based approximate optimization. For the meta-modelling of inequality constraint functions such as stresses, deformations, and frequency, a constraint-feasible moving least-squares method (CF-MLSM) is suggested in the present study. The method of CF-MLSM, compared with a conventional MLSM, has been shown to ensure the constraint feasibility in a case where the approximate optimization process is employed. The solution results from proposed optimization methods present improved design performances under both bump and brake conditions.

The Strength Evaluation and Reliability Analysis of Periodic Symmetric Struts Support

2011 ◽  
Vol 462-463 ◽  
pp. 1164-1169
Author(s):  
Jing Xiang Yang ◽  
Ya Xin Zhang ◽  
Mamtimin Gheni ◽  
Ping Ping Chang ◽  
Kai Yin Chen ◽  
...  
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Stress Distribution ◽  
Least Squares ◽  
Reliability Analysis ◽  
Least Squares Method ◽  
Nonlinear Least Squares ◽  
Distribution Model ◽  
Different Types ◽  
Nonlinear Least Squares Method

In this paper, strength evaluations and reliability analysis are conducted for different types of PSSS(Periodically Symmetric Struts Supports) based on the FEA(Finite Element Analysis). The numerical models are established at first, and the PMA(Prestressed Modal Analysis) is conducted. The nodal stress value of all of the gauss points in elements are extracted out and the stress distributions are evaluated for each type of PSSS. Then using nonlinear least squares method, curve fitting is carried out, and the stress probability distribution function is obtained. The results show that although using different number of struts, the stress distribution function obeys the exponential distribution. By using nonlinear least squares method again for the distribution parameters a and b of different exponential functions, the relationship between number of struts and distribution function is obtained, and the mathematical models of the stress probability distribution functions for different supports are established. Finally, the new stress distribution model is introduced by considering the DSSI(Damaged Stress-Strength Interference), and the reliability evaluation for different types of periodically symmetric struts supports is carried out.

scholarly journals Meshless membrane model based on the moving least-squares method

2006 ◽  
Vol 73 (2) ◽  
Author(s):  
Hiroshi Noguchi ◽  
Gerhard Gompper
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Membrane Model ◽  
Moving Least Squares ◽  
Model Based ◽  
Moving Least Squares Method

scholarly journals Multi-Dimensional Moving Least Squares Method for Elasticity Problems

2012 ◽  
Vol 78 (786) ◽  
pp. 142-151
Author(s):  
Kohei SAKIHARA ◽  
Hitoshi MATSUBARA ◽  
Takaaki EDO ◽  
Hisao HARA ◽  
Genki YAGAWA
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Moving Least Squares ◽  
Moving Least Squares Method ◽  
Elasticity Problems

A Hybrid Method Using Response Surface and Pattern Search for Design Optimization

2005 ◽  
Author(s):  
T. Zhang ◽  
K. K. Choi ◽  
S. Rahman
Keyword(s):  
Radial Basis Function ◽  
Least Squares ◽  
Response Surface ◽  
Basis Function ◽  
Function Approximation ◽  
Least Squares Method ◽  
Order Approximation ◽  
Pattern Search ◽  
Moving Least Squares ◽  
Radial Basis

This paper presents a new method to construct response surface function and a new hybrid optimization method. For the response surface function, the radial basis function is used for a zeroth-order approximation, while new bases is proposed for the moving least squares method for a first-order approximation. For the new hybrid optimization method, the gradient-based algorithm and pattern search algorithm are integrated for robust and efficient optimization process. These methods are based on: (1) multi-point approximations of the objective and constraint functions; (2) a multi-quadric radial basis function for the zeroth-order function representation or radial basis function plus polynomial based moving least squares approximation for the first-order function approximation; and (3) a pattern search algorithm to impose a descent condition. Several numerical examples are presented to illustrate the accuracy and computational efficiency of the proposed method for both function approximation and design optimization. The examples for function approximation indicate that the multi-quadric radial basis function and the proposed radial basis function plus polynomial based moving least squares method can yield accurate estimates of arbitrary multivariate functions. Results also show that the hybrid method developed provides efficient and convergent solutions to both mathematical and structural optimization problems.

scholarly journals An Improved Moving Least Squares Method for Curve and Surface Fitting

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Lei Zhang ◽  
Tianqi Gu ◽  
Ji Zhao ◽  
Shijun Ji ◽  
Ming Hu ◽  
...  
Keyword(s):  
Least Squares ◽  
Random Error ◽  
Least Squares Method ◽  
Surface Fitting ◽  
Measured Data ◽  
Moving Least Squares ◽  
Generate Curve ◽  
Moving Least Squares Method ◽  
Curve And Surface Fitting ◽  
Mls Method

The moving least squares (MLS) method has been developed for the fitting of measured data contaminated with random error. The local approximants of MLS method only take the error of dependent variable into account, whereas the independent variable of measured data always contains random error. Considering the errors of all variables, this paper presents an improved moving least squares (IMLS) method to generate curve and surface for the measured data. In IMLS method, total least squares (TLS) with a parameterλbased on singular value decomposition is introduced to the local approximants. A procedure is developed to determine the parameterλ. Numerical examples for curve and surface fitting are given to prove the performance of IMLS method.

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