Particle Swarm Optimization with Sequential Niche Technique for Dynamic Finite Element Model Updating

Abstract

Due to uncertainties associated with material properties, structural geometry, boundary conditions, and connectivity of structural parts as well as inherent simplifying assumptions in the development of finite element (FE) models, actual behavior of structures often differs from model predictions. FE model updating comprises a multitude of techniques that systematically calibrate FE models in order to match experimental results. Updating of structural models can be posed as an optimization problem where model parameters that minimize the errors between the responses of the model and actual structure are sought. However, due to limited number of experimental responses and measurement errors, the optimization problem may have multiple admissible solutions in the search domain. Global optimization algorithms (GOAs) are useful and efficient tools in such situations as they try to find the globally optimal solution out of many possible local minima, but are not totally immune to missing the right minimum in complex problems such as those encountered in updating. A methodology based on particle swarm optimization (PSO), a GOA, with sequential niche technique (SNT) for FE model updating is proposed and explored in this article. The combination of PSO and SNT enables a systematic search for multiple minima and considerably increases the confidence in finding the global minimum. The method is applied to FE model updating of a pedestrian cable‐stayed bridge using modal data from full‐scale dynamic testing.

An efficient algorithm for architecture design of Bayesian neural network in structural model updating

“…Thus, the FE model must be adjusted to improve its matching quality, which is generally an inverse process and known as the FE model updating. There is a strong interest in developing the FE model updating methods based on vibration measurements over the past few decades (Friswell & Mottershead, ), which have been applied to a variety of structural systems and components in diverse engineering fields, such as beams (Levin & Lieven, ; Simoen, De Roeck, & Lombaert, ; Teughels, De Roeck, & Suykens, ), trusses and frames (Adeli & Cheng, ; Adeli & Jiang, ; Boulkaibet, Mthembu, De Lima Neto, & Marwala, ; Katafygiotis & Beck, ; Law, Chan, & Wu, ; Oh, Kim, & Park, ; Yin, Jiang, & Yuen, ; Yin, Lam, Chow, & Zhu, ; Yu & Yin, ; Yuen, ), bridges (Brownjohn, Moyo, Omenzetter, & Lu, ; Jaish & Ren, ; Jensen, Millas, Kusanovic, & Papadimitriou, ; Park, Kim, Kim, & Lee, ; Shabbir & Omenzetter, ), highrise and historic buildings (Astroza, Nguyen, & Nestorovic, ; Jiang & Adeli, ; Torres, Almazán, Sandoval, & Boroschek, ), railway sleepers (Lam, Hu, & Wong, ), pipelines (Yin, Yuen, Lam, & Zhu, ; Yin, Zhu, & Fu, ; Zhu, Hao, & Peng, ), and aerospace structures (Mottershead, Link, & Friswell, ; Stochino, Cazzani, Poppi, & Turco, ).…”

Statistical Regularization for Identification of Structural Parameters and External Loadings Using State Space Models

“…In particular, several time domain model‐based SID methodologies, such as the least‐squares methods (Yang and Huang, ), the meta‐heuristic algorithm (e.g., the genetic algorithms, the artificial bee colony algorithms, the particle swarm optimization, etc.) (Perry and Koh, ; Fuggini et al., ; Sun et al., , b; Shabbir and Omenzetter, ), the Kalman filtering (Koh et al., ), the neural networks (Jiang and Adeli, ; Adeli and Jiang, ; Adeli and Panakkat, ), and the Bayesian inference (Yuen et al., ; Yuen and Katafygiotis, ) can be pointed out. Other recent developments of SID are, for example, Jiang and Adeli () who presented a non‐parametric SID technique for damage detection of high‐rise building structures subjected to seismic excitations using the dynamic fuzzy wavelet neural network.…”

Neurocomputing in Civil Infrastructure

“…Gholizadeh and Mohammadi [92] combined particle swarm optimization [93][94][95], bat algorithm, and wavelet backpropagation neural networks for reliability-based seismic optimization of steel momentresisting frames. They applied the model to a threestory steel frame.…”