FORM is a structural reliability analysis method that approximates the probability of failure by linearizing the limit state function around the design point.
The uncertain model parameters are transformed into independent standard Normal random variables. For the performance and stability of numerical algorithms, performing such a transformation step is sometimes preferable.
It is the point in the failure domain that has the smallest distance to the origin in standard Normal space. This point is also referred to as design point.
When the limit state function can be expressed as the difference between capacity and demand, the problem is sometimes referred to as basic reliability problem.
The underlying distribution can be highly skewed, even if the total number of samples in the Monte Carlo simultion is very large. This is why the Normal approximation often performs poorly in practice.
The distribution quantifying the uncertainty about the probability of failure can be highly skewed, even for a large number of samples. The coefficient of variation is easier to interprete for symmetric distributions.
Even if Monte Carlo simulation returns not a single sample in the failure domain, we can still quantify the uncertainty about whether a specified target reliability level is maintained.