Global sensitivity of structural variability by random sampling

This paper presents an efficient sampling-based algorithm for the estimation of the upper bounds of the total sensitivity indices. These upper bounds, introduced by Sobol', are based on the integration of the classical (local) gradient sensitivity analysis within the whole parameter space of the inputs. Hence, in this work the idea is to repeat the estimation of the local sensitivity analysis adopting a very efficient Monte Carlo procedure, along the points generated from Markov-chains. The introduced procedure is simple, model-independent and generally applicable. Furthermore, it is especially efficient for functions involving large number of input parameters. Presented numerical examples prove the efficiency and the applicability of the proposed approach.