Finite-element-model updating allows reducing the discrepancies between the numerical and the experimental dynamic behaviour of civil engineering structures. Among the different methods to tackle the updating problem, the maximum likelihood method has been widely used for practical engineering applications. In this method, the updating problem is transformed into an optimization problem where the relative differences between the numerical and experimental modal properties of the structure are reduced via the modification of the most relevant physical parameters of the model. However, this method often presents the drawback of requiring high simulation times in order to perform the updating process when dealing with complex structures. To overcome this limitation, in this paper a novel hybrid Unscented Kalman Filter – Harmony Search (UKF-HS) algorithm is proposed and its implementation details are discussed. In order to validate such hybrid algorithm and further illustrate its performance, the finite-element-model updating of a benchmark footbridge is performed using two different approaches (single-objective and multi-objective) and three different computational algorithms, namely: (i) genetic algorithms; (ii) harmony search; and (iii) the novel UKF-HS hybrid algorithm. The obtained results reveal that the proposed hybrid algorithm may be considered as an adequate alternative tool to efficiently perform the finite-element-model updating of civil engineering structures in practical engineering applications.

In finite-element model updating, numerical models are calibrated in order to better mimic the real behaviour of structures. Such updating process is usually performed under the maximum likelihood method in practical engineering applications. According to this, the updating problem is transformed into an optimization problem. The objective function of this problem is usually defined in terms of the relative differences between the numerical and the experimental modal properties of the structure. To this aim, either (1) a single-objective or (2) a multi-objective approach may be adopted. Due to the complexity of the problem, global optimizers are usually considered for its solution. Among these algorithms, nature-inspired computational algorithms have been widely employed. Nevertheless, such model updating approach presents two main limitations: (1) a clear dependence between the updated model and the objective function considered; and (2) a high computational cost. In order to overcome these drawbacks, a detailed study has been performed herein both to establish the most adequate objective function to tackle the problem and to further assist in the selection of the most efficient computational algorithm among several well-known ones. For this purpose, a laboratory footbridge has been considered as benchmark to conduct the updating process under different scenarios.

Tuned mass dampers (TMDs) are well-known control devices used widely to mitigate the pedestrian-induced vibrations on footbridges. The main purpose of this study is the robust optimum design of TMDs using multi-objective genetic algorithms in order to control the pedestrian-induced vibrations on footbridges considering the uncertainties associated with their modal properties. The performance of the TMD is improved via the robust optimum design of its modal parameters using multi-objective genetic algorithms. As an example, a benchmark footbridge, modelled by frame and shell elements, is used to assess numerically the performance and accuracy of the proposed method. The pedestrian action is simulated by an equivalent harmonic load. The proposed method is compared with the conventional Den Hartog's proposal. The results show that this new method is more effective than the conventional proposal and the resulting damping device is more economic due to the smaller value of its design parameters.

In this paper we analyse a case study where a finite element model (FEM) was updated on the basis of the experimental modal parameters recorded for a reinforced concrete truss bridge built in Almeria (Spain) in 1927. The final aim of this study is to help understand the actual state of structural conservation of the bridge, in order to select an appropriate retrofitting technique for its reinforcement before a planned widening of its deck. At present, the two most widely used methods for retrofitting are either reinforcing the structure with external prestressing or increasing its flexural strength by adhering carbon fibre reinforced polymer (CFRP) laminates. The first method is especially advantageous if the structure has deteriorated to such an extent that the strengthening needs to focus not only on increasing the flexural strength of the bridge, but also on avoiding the deflection problems associated with the reduced inertia of the structure. If this were not the case, strengthening with CFRP would be an adequate option. To this end, and given the monumental character of this ancient construction, it is not possible to apply destructive or static load tests directly to determine the properties of its constituent materials, so that it becomes necessary to estimate its deterioration state indirectly with the use of non-destructive techniques. For this purpose, in this study, a FEM is updated based on experimental modal parameters, which allows for checking of its service condition through the estimation of the value of several physical parameters of the bridge. The resulting updated model constitutes a valuable tool to help establish the most adequate retrofitting method.