An efficient approach to model updating for a multispan railway bridge using orthogonal diagonalization combined with improved particle swarm optimization

In this paper, a novel approach to model updating for a large-scale railway bridge using orthogonal diagonalization (OD) coupled with an improved particle swarm optimization (IPSO) is proposed. Particle swarm optimization (PSO) is a well-known and widely applied evolutionary algorithm. However, as other evolutionary algorithms (EAs), PSO has two main drawbacks that may reduce its capability to tackle optimization issues. A fundamental shortcoming of PSO is premature convergence. On the other hand, since PSO employs all populations to seek the best solution through iterations, it is very time-consuming. This makes PSO as well as EAs difficult to apply for optimization problems of large-scale structural models. In order to overcome those drawbacks, we propose coupling OD with IPSO (ODIPSO). OD is applied to arrange the position of particles and to select only particles with the best solution for next iterations, which helps to reduce the computational cost dramatically. There are several significant features of ODIPSO: (1) IPSO is employed to tackle the problem of premature convergence of PSO; (2) only one guide is used to update the velocity of particles instead of utilizing both guides, consisting of the local best and the global best; and (3) in each iteration, only the velocity and the position of the best particles are updated. In order to assess the effectiveness of the proposed approach, a large-scale railway bridge calibrated on the field is employed. This paper also introduces the use of wireless triaxial sensors (replacing classical wired systems) to obtain structural dynamic characteristics. The appearance of wireless triaxial transducers increases significantly the freedom in designing an ambient vibration test. The results show that ODIPSO not only outperforms PSO, IPSO and OD combined with PSO (ODPSO) in terms of accuracy, but also dramatically reduces the computational time compared to PSO and IPSO.