surrogates allow you to replace a computationally costly model by an analytical function so that 10^6 samples or more can be evaluated in no time. In contrast FORM/SORM methods are approximation methods for reliability analysis that aim at reducing to the very minimum the number of calls to the (possibly costly) model/limit state function.
Once you have a surrogate, say an accurate polynomial chaos expansions, it does not make any sense to use FORM which would replace it by a linearized function in the standard normal space! Some simulation method, e.g. crude Monte Carlo if the probability of failure is not too small, or subset simulation in general, should be used with the surrogate.
So: you can in theory combine any surrogate modelling technique with any reliability method, but certain combinations do not really make sense, e.g. FORM or SORM after building a Kriging or a PCE model.
Our recent active learning module allows you to do these general combinations, also including various enrichment (a.k.a. infill) criteria. We recommend PC-Kriging and subset simulation as the default setup.