Dimensionality Reduction and Visualization for Structural Reliability Analysis using Artificial Neural Networks

Resumo

Structural reliability analyses may become very computationally demanding, especially when numerical simulations are employed to represent the structural behavior, and/or these analyses are used within structural optimization procedures. To overcome this problem, surrogate models have been largely used in the last decades, helping to avoid evaluations of the demanding parts of the computational code and to reduce the overall computational demand. However, the efficiency of the surrogates is usually compromised when dealing with high dimensional problems. In fact, high dimensionality imposes some difficulties not only to surrogate models but also for some structural reliability methods available in the literature. For these reasons, the present paper proposes to investigate the application of Artificial Neural Networks to reduce the dimensionality of structural reliability problems. A proper dimensionality reduction may help visualizing and understanding the problem and may assist surrogate models and reliability methods which would otherwise lose accuracy, precision and/or efficiency when applied to high dimensional problems.