Computation of a novel structural resilience index: challenges and first attempt

At first, my talk will shortly introduce a novel resilience index proposed by Chiaia et al. Since its definition is based on a combinatorial optimization problem resembling that for the computation of the kinematic upper bound in plastic analysis, I will delve into the description of the challenges related to its computation. I will then be motivated to introduce two algorithms aimed at overcoming these challenges, namely the branch-and-bound (BnB) and genetic (GA) algorithms. Concerning the BnB algorithm, branching criteria yielding to an unconstrained optimization problem will be introduced along with fathoming conditions and exploration rules for the solution tree. Concerning the GA, its several parameters —as the population size and those related to random distributions, as an instance— and rules leading to the definition of how each generation is created from the previous one, will be introduced. The whole solution procedure will be outlined for both methodologies and a comparison between their performances will be presented for some benchmark tests, for different setup conditions. To have an as-fair-as-possible comparison, since the ga package of the Matlab software has been utilized for running computations based on the genetic solution strategy, the BnB algorithm has been implemented into an in-house code written in the Matlab language.