A new approach for solving inverse reliability problems with implicit response functions

Cheng, Jin; Zhang, Jie; Cai, C.S.; Xiao, Rucheng

doi:10.1016/j.engstruct.2006.04.005

Cited by 40 publications

(24 citation statements)

References 13 publications

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“…Although the above-mentioned response surface methods have been applied for solving forward reliability problems, their applications to inverse reliability problems were realized very recently by Cheng et al [10], in which they proposed an ANN-based response surface method for solving inverse reliability problems with implicit response functions. However, to the knowledge of the authors, no detailed study has been reported in the literature to compare the performance of the ANN-based response surface method with the polynomial-based response surface method in the context of inverse reliability analysis of structures.…”

Section: Discussionmentioning

confidence: 99%

“…(2), the above introduced inverse FORM usually requires too much computational efforts and is difficult to compute the derivatives of the structural response functions. To overcome these drawbacks of the inverse FORM, an ANN-based RSM was recently developed by Cheng et al [10]. In their method, artificial neural network is applied to approximate the response function.…”

Section: The Ann-based Rsm Methodsmentioning

confidence: 99%

“…Although the details of an ANN model are not given here due to lack of space, they can be found in Ref. [10,12].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The accuracy of the constructed response surface is a key when using different response surface methods for solving the inverse reliability problem with implicit limit state function expressed in Eq. (10). To check the accuracy of the constructed response surface by the different response surface methods, we can generate eight groups of random samples simultaneously.…”

Section: Example 1: Linear Steel Frame Structurementioning

confidence: 99%

“…Nevertheless, the application of the RSM to the inverse reliability analysis of structures is very limited. Cheng et al [10] developed an artificial neural network (ANN) based RSM to solve inverse reliability problems with implicit response functions for the first time. In their paper, the ANN model was used to approximate the structural response function.…”

Section: Introductionmentioning

confidence: 99%

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Application of the response surface methods to solve inverse reliability problems with implicit response functions

Cheng

2008

Comput Mech

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The inverse first-order reliability method (FORM) is considered to be one of the most widely used methods in inverse reliability analysis. It has been recognized that there are shortcomings of the inverse FORM in solving inverse reliability problems with implicit response functions, primarily inefficiency and difficulties involved in evaluating derivatives of the implicit response functions with respect to random variables. In order to apply the inverse FORM to structural inverse reliability analysis, response surface methods can be used to overcome the shortcomings. In the present paper, two different response surface methods, namely the polynomial-based response surface method and the artificial neural network-based response surface method, are developed to solve the inverse reliability problems with implicit response functions, and the accuracy and efficiency of the two response surface methods are demonstrated through two numerical examples of steel structures. It is found that the polynomial-based response surface method is more efficient and accurate than the artificial neural networkbased response surface method. Recommendations are made regarding the suitability of the two response surface methods to solve the inverse reliability problems with implicit response functions.Keywords Inverse reliability method · Artificial neural network · The response surface method · The first-order reliability method

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Section: Discussionmentioning

confidence: 99%

Section: The Ann-based Rsm Methodsmentioning

confidence: 99%

“…Although the details of an ANN model are not given here due to lack of space, they can be found in Ref. [10,12].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Example 1: Linear Steel Frame Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of the response surface methods to solve inverse reliability problems with implicit response functions

Cheng

2008

Comput Mech

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Response surface‐based finite‐element model updating of a historic masonry minaret for operational modal analysis

Alpaslan

Hacıefendioğlu

Demir

et al. 2020

Structural Design Tall Build

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SummaryThis study aimed to use the response surface (RS) method for finite element (FE) model updating, using operational modal analysis (OMA). The RS method was utilized to achieve better agreement between the numerical and field‐measured structure response. The OMA technique for the field study was utilized to obtain modal parameters of the selected historic masonry minaret. The natural frequencies and mode shapes were experimentally determined by the enhanced frequency domain decomposition (EFDD) method. The optimum results between the experimental and numerical analyses were found by using the optimization method. The central composite design was used to construct the design of experiments, and the genetic aggregation approach was performed to generate the RS models. After obtaining the RS models, an attempt was made to converge the natural frequency values corresponding to the five‐mode shapes with the frequency values identified by the experimental analysis. ANSYS software was used to perform 3D finite element (FE) modeling of the historic masonry minaret and to numerically identify the natural frequencies and mode shapes of the minaret. The results of the experimental, initial, and updated FE model were compared with each other. Significant differences can be seen when comparing the experimental and analytical results with the initial conditions.

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Reliability Analysis of Offshore Structures

Karadeniz

Saka

Toğan

2012

Springer Series in Reliability Engineering

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A new approach for solving inverse reliability problems with implicit response functions

Cited by 40 publications

References 13 publications

Application of the response surface methods to solve inverse reliability problems with implicit response functions

Application of the response surface methods to solve inverse reliability problems with implicit response functions

Response surface‐based finite‐element model updating of a historic masonry minaret for operational modal analysis

Reliability Analysis of Offshore Structures

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