Finite-element-model updating of civil engineering structures using a hybrid UKF-HS algorithm

Abstract

Structure and Infrastructure EngineeringHarmony 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.

Parameter identification of the dynamic Winkler soil–structure interaction model using a hybrid unscented Kalman filter–multi-objective harmony search algorithm

“…In order to overcome these limitations, hybrid algorithms are normally employed. Among these algorithms, a recent proposal – the UKF–MHS algorithm (Naranjo-Perez et al, 2020) – has been considered herein due to its great efficiency and accuracy to solve the FE model updating problem of complex civil engineering structures.…”

“…The hybrid UKF–MHS algorithm is a local–global optimization algorithm which takes advantage of the virtues of the two well-known optimization algorithms to reduce the simulation time of the optimization process without compromising the accuracy of the solutions provided (Naranjo-Pérez et al, 2020).…”

“…The hybrid UKF-MHS algorithm is a local-global optimization algorithm which takes advantage of the virtues of the two well-known optimization algorithms to reduce the simulation time of the optimization process without compromising the accuracy of the solutions provided (Naranjo-Pe´rez et al, 2020). The general layout of this algorithm is shown in Figure 2 and it may be summarized as follows: (1) the MHS algorithm proposes a set of candidate values for the parameters; (2) for each candidate value, the UKF algorithm obtains the value of u kjk ; (3) the different terms of the objective function are evaluated for each value of u kjk ; (4) the solutions are classified according to the non-dominating sorting technique; (5) the process is repeated iteratively until some convergence criterion is met; and (6) finally, the Pareto front is obtained.…”

“…For instance, a hybrid UKF-simulated annealing algorithm was proposed by Astroza et al (2016) that allowed reducing the simulation time when compared to the sole implementation of the simulated annealing algorithm. More recently, Naranjo-Perez et al (2020) proposed a hybrid UKF–harmony search (HS) algorithm for the FE model updating of complex civil engineering structures. This hybrid algorithm combined the virtues of two individual algorithms as follows: (1) the (local) UKF algorithm, which reduces the uncertainty associated with the parameter identification based on experimental data; and (2) the (global) HS algorithm (Geem et al, 2001), which reduces significantly simulation times when compared to other classical metaheuristic algorithms (Jiménez-Alonso et al, 2017).…”

Estimation of structural stiffness with the use of Particle Swarm Optimization

“…Moreover, the number of stiffness coefficients estimated may be too high to apply analytical optimization techniques. Due to these problems, the FEM update method was developed based on various forms of Artificial Intelligence (AI) techniques (Maia and Silva, 1997;Levin and Lieven, 1998;Marwala, 2010;Park et al, 2017;Negri et al, 2018;Bartilson et al, 2019;Bianconi et al, 2020;Kang et al, 2020;Naranjo-Pérez et al, 2020;Zhang and Sun, 2020). The main difference of the proposed method is that the computation of the cost function is not based on the modal sensitivity analysis (Fox and Kapoor, 1968;Adhiakri, 1999), but on the low dimensional eigenvalue problem.…”