  Active learning methods have recently surged in the literature due to their
ability to solve complex structural reliability problems within an affordable
computational cost. These methods are designed by adaptively building an
inexpensive surrogate of the original limit-state function. Examples of such
surrogates include Gaussian process models which have been adopted in many
contributions, the most popular ones being the efficient global reliability
analysis (EGRA) and the active Kriging Monte Carlo simulation (AK-MCS), two
milestone contributions in the field. In this paper, we first conduct a survey
of the recent literature, showing that most of the proposed methods actually
span from modifying one or more aspects of the two aforementioned methods. We
then propose a generalized modular framework to build on-the-fly efficient
active learning strategies by combining the following four ingredients or
modules: surrogate model, reliability estimation algorithm, learning function
and stopping criterion. Using this framework, we devise 39 strategies for the
solution of $20$ reliability benchmark problems. The results of this extensive
benchmark (more than $12,000$ reliability problems solved) are analyzed under
various criteria leading to a synthesized set of recommendations for
practitioners. These may be refined with a priori knowledge about the feature
of the problem to solve, i.e. dimensionality and magnitude of the failure
probability. This benchmark has eventually highlighted the importance of using
surrogates in conjunction with sophisticated reliability estimation algorithms
as a way to enhance the efficiency of the latter.
