Inverse Response Surface Method in Reliability-Based Design

Lehký, David; Šomodíková, Martina

doi:10.1515/tvsb-2017-0025

Cited by 3 publications

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An adaptive ANN‐based inverse response surface method

Šomodíková

Lehký

2018

Beton und Stahlbetonbau

Self Cite

View full text Add to dashboard Cite

The paper deals with an adaptive artificial neural network‐based inverse response surface method utilized when performing reliability‐based design optimization of complex structural systems. Since calculating their reliability indicators (failure probabilities or reliability indices) is usually a time‐consuming task, the utilization of approximation methods with a view to reducing the computational effort to an acceptable level is an appropriate solution. A popular approximation method is the response surface method, where the limit state function is approximated using a suitable surrogate model. In this case, an artificial neural network is utilized. Construction of a response surface requires all variables of stochastic model to be known in advance. However, during the structural design, which is an inverse task, there are design parameters which are subject of reliability‐based design optimization procedure and thus not known at the start of the process. For such cases, an adaptive inverse response surface procedure is proposed. The procedure is based on a coupling of the adaptive response surface method and the artificial neural network‐based inverse reliability method. The validity and accuracy of the method is tested using example with explicit nonlinear limit state function. Obtained results as well as important aspects of the method are discussed.

show abstract

An adaptive ANN‐based inverse response surface method

Šomodíková

Lehký

2018

Beton und Stahlbetonbau

Self Cite

View full text Add to dashboard Cite

show abstract

Inverse Response Surface Method for Structural Reliability Analysis

Nagesh

Balu

2020

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

ANN Based Design Parameter Estimation for Structural Systems

Nagesh

Balu

2020

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

Estimation of the probability of failure of multi-dimensional structural systems is expensive from the computation perspective. To decrease the burden of computation, one can use simple approximation methods like Surrogate models, Kriging model, Support vector machine, Artificial neural network, and more based on the suitability for the problems. In Surrogate or Response surface modeling, the limit state function of any system is suitably approximated by making use of known mathematical models like polynomials, exponentials, etc. During the construction of surrogates, variables in the model should be well known prior to the approximation. In practical consideration, the design parameters are the unknowns that need to be evaluated before reliability-based design. Inverse Response surface procedure is proposed in the paper to address the above-mentioned issue. The procedure developed is the combination of adaptive Response surface method with appropriate experimental design i.e. Halton low discrepancy sequence sampling technique for evaluating the probability of failure or reliability index and an Artificial neural network is utilised as an inverse reliability procedure for design optimisation. The method gives an accurate result and the efficiency is increased for the same number of iterations in comparison to the work of David Lehky and Martina Somodikova [1] with Latin hypercube sampling as experimental design.

show abstract

Inverse Response Surface Method in Reliability-Based Design

Cited by 3 publications

References 11 publications

An adaptive ANN‐based inverse response surface method

An adaptive ANN‐based inverse response surface method

Inverse Response Surface Method for Structural Reliability Analysis

ANN Based Design Parameter Estimation for Structural Systems

Contact Info

Product

Resources

About