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This paper introduces a novel approach that focuses on application of cooperative game theory model for automated finite element model updating. The approach involves transforming the conventional single-objective optimization finite element model updating problem into a game theory problem. Among the different alternatives, a cooperative game model is considered to solve the transformed updating problem. This new approach overcomes the limitations of the conventional single-objective optimization formulation, which include challenges related to selecting appropriate weighting factor of residuals and analysing their influence on optimization results. To demonstrate the effectiveness of the proposed approach, an ultralight fibre reinforced polymer footbridge is considered. The numerical model of the footbridge is updated using the experimentally determined inherent properties (natural frequencies and mode shapes) using the proposed approach. A comparison with the conventional sensitivity-based weighted single objective method reveals that the inclusion of the weighting factor in the optimization process under the cooperative game model method allows for more efficient solution of the single-objective updating optimization problem, while maintaining accuracy and quality in the updated model. [ABSTRACT FROM AUTHOR]

• A new algorithm is proposed and validated for the model updating of structures. • The algorithm takes advantage of the virtues of different mathematical techniques. • The algorithm allows reducing the simulation time for the updating process. • The algorithm allows obtaining a robust selection of the best updated model. • The performance of this algorithm has been validated via a real case study. Finite element model updating has become a key tool to improve the numerical modelling of existing civil engineering structures, by adjusting the numerical response to the observed experimental behaviour of the structure. At present, model updating is mostly conducted using the maximum likelihood method. Following this approach, the updating problem can be transformed into a multi-objective optimization problem. Due to the complex nonlinear behaviour of the resulting objective functions, metaheuristic optimization algorithms are normally employed to solve such optimization problem. However, and although this is nowadays a well-established technique, there are still two main drawbacks that need to be addressed for practical engineering applications, namely: (i) the high simulation time required to compute the problem; and (ii) the uncertainty associated with the selection of the best updated model among all the Pareto optimal solutions. In order to overcome these limitations, a new collaborative algorithm is proposed herein, which takes advantage of the collaborative coupling among two optimization algorithms (harmony search and active-set algorithms), a machine learning technique (artificial neural networks) and a statistical tool (principal component analysis). The implementation details of our proposal are discussed in detail throughout the paper and its performance is illustrated with a case study addressing the model updating of a real steel footbridge. Two are the main advantages of the newly proposed algorithm: (i) it leads to a clear reduction of the simulation time; and (ii) it further permits a robust selection of the best updated model. [ABSTRACT FROM AUTHOR]