Evolutionary game theory-based finite element model updating of a moveable cable-stayed footbridge

Abstract Evolutionary game theory allows determining directly the solution of the maximum likelihood finite element model updating problem via the transformation of a bi-objective optimization problem into a game theory problem. The formulation of the updating problem as a game avoids the computation of the Pareto front and the solution of the subsequent decision-making problem, the selection of the best solution among the elements of the Pareto front. For this purpose, each term of the bi-objective function is considered as a player that interacts collaboratively or non-collaboratively with the other player during the game. One of the main advantages of this method is that a different global optimization algorithm can be associated with each player. In this manner, a higher performance in the solution of the updating problem is expected via the linking between each term of the objective function (a player) and the algorithm considered for its minimization. In this study, this advantage is analysed in detail. For this purpose, the finite element model updating process of a real footbridge, the Viana do Castelo footbridge, has been considered as a benchmark. As global optimization algorithms, different nature-inspired computational algorithms have been considered. The updating problem has been solved using two different methods: (i) the linking of a conventional bi-objective optimization method together with a decision-making method; and (ii) an evolutionary game theory method. As a result, a higher performance of the game theory method has been highlighted. Additionally, the influence of the considered optimization algorithm in the updating process has been noted.