You should consider the purpose of using Monte Carlo Simulation (MCS). For example, you want to determine the probability of failure of a structure under a seismic event with different cross sections of beams and columns. Firstly, you generate a set of random variables relating to the cross sections by MCS. Then, all the input data of cross-sections are applied to your model (if your model is a FEM model, coding for automatical running is the best way). After running the model throughout all the random variables, you can calculate the probability of failure by the equation, Pf = Nf/N (where N is the total number of running cases and Nf is the number of failure cases). You should remember that using MCS has a disadvantage that it is necessary to have about 1 million random variables to get a good result. Using Latin Hypercube Sampling should be considered if you want to reduce your random variables.
As my understanding, you want to generate a set of random variables for Sdi by using MCS, to obtain a set of random variables of Umax by your Equation. In my opinion, it is not a good way, especially for complex structures behaving in a nonlinear state. In our procedure, we generate random variables just for input parameters, then use these input parameters to analyze the structures (assign the input data to the model like natural randomness). All the output random variables are taken after the models to be analyzed. For example, If you have 1000 random variables (e.g. an earthquake event), you need to assign 1000 times to your FEM model, then you will receive 1000 random variables of output (e.g. the displacement of the top of the building).