Reliability-based structural optimization using neural networks and Monte Carlo simulation

By Manolis Papadrakakis, N. Lagaros. 2002

543 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8f28c2070827db340ac27b1c617e736e59b153f0

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In Civil and Structural Engineering

In Statistics and Probability

Citations in the corpus

Metamodel-based importance sampling for structural reliability analysis (2011, methodology): “Various surrogates have been used amongst which are: quadratic response surfaces (Bucher and Bourgund, 1990; Kim and Na, 1997; Das and Zheng, 2000) and the more recent metamodels such as support vector machines (Hurtado, 2004; Deheeger and Lemaire, 2007), neural networks (Papadrakakis and Lagaros, 2”
A survey on approaches for reliability-based optimization (2010, methodology): “The application of meta-modeling techniques has allowed replacing numerically intensive virtual simulation models by inexpensive ones (see, e.g. Hurtado (2004); Jin et al (2003); Papadrakakis and Lagaros (2002); Zhang and Foschi (2004)).”
A new adaptive response surface method for reliability analysis (2013, methodology): “This new model, called metamodel, can be of various kinds: quadratic response surfaces [21], polynomial chaos [23,6], kriging [40,27], neural network [36,13,20] or even support vector machine [25].”
Reliability-based design optimization using kriging surrogates and subset simulation (2011, background): “…Zheng, 2000), polynomial chaos expansions (Sudret and Der Kiureghian, 2002; Berveiller et al., 2006; Blatman and Sudret, 2008, 2010), support vector machines (Hurtado, 2004; Deheeger, 2008; Bourinet et al., 2010), neural networks (Papadrakakis and Lagaros, 2002) and kriging (Bi-chon et al., 2008).”
Review and application of Artificial Neural Networks models in reliability analysis of steel structures (2015, methodology): “An extensive description, as well as, numerical examples of the use of ANN-based MCS in structural reliability can be found in ([31], [32], [27], [33], [34]).”
A new adaptive importance sampling Monte Carlo method for structural reliability (2013, methodology): “The key-idea of this technique is to obtain a non-negative sampling density located in the neighborhood of the most probable failure point (Papadrakakis and Lagaros, 2002).”
Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures (2020, background): “…increases especially when numerous simulations are required to account for the optimization [7, 8] and stochastic uncertainties of external loads (e.g., Monte Carlo simulations [9–11] or incremental dynamic analysis (IDA) [12–14] of nonlinear structural systems for fragility/reliability analysis).”
A new learning function for Kriging and its applications to solve reliability problems in engineering (2015, methodology): “It is called metamodel with various kinds: Quadratic Response Surfaces [14], Polynomial Chaos [15], Kriging [16,17], Neural Network [18], and Support Vector Machine [19,20], etc.”