Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability

TL;DR: It is argued that ML is capable of providing novel insights and opportunities to solve important challenges in reliability and safety applications and is also capable of teasing out more accurate insights from accident datasets than with traditional analysis tools, and this can lead to better informed decision-making and more effective accident prevention.

TL;DR: A review of the machine learning-based structural reliability analysis methods is presented in this article , where the authors focus on the different models' structures and diverse applications of each ML method in different aspects of SRA.

TL;DR: A state-of-the art review for the modelling and propagation of p-boxes with a special focus on structural reliability analysis is given.

TL;DR: A new learning function called Folded Normal based Expected Improvement Function (FNEIF) is proposed to efficiently estimate the failure probability of the surrogate model for reliability analysis.

TL;DR: In this paper, the authors used the representations of the noise currents given in Section 2.8 to derive some statistical properties of I(t) and its zeros and maxima.

TL;DR: An iterative approach based on Monte Carlo Simulation and Kriging metamodel to assess the reliability of structures in a more efficient way and is shown to be very efficient as the probability of failure obtained with AK-MCS is very accurate and this, for only a small number of calls to the performance function.

TL;DR: This paper develops an efficient reliability analysis method that accurately characterizes the limit state throughout the random variable space and is both accurate for any arbitrarily shaped limit state and computationally efficient even for expensive response functions.

TL;DR: The new method is found to be more efficient than other existing discretization methods, and more practical than a series expansion method employing the Karhunen‐Loeve theorem, and particularly useful for stochastic finite element studies involving random media.