R. Ghiasi, M. R. Ghasemi, M. R. Sohrabi,
Volume 36, Issue 1 (9-2017)
Abstract
Utilizing surrogate models based on artificial intelligence methods for detecting structural damages has attracted the attention of many researchers in recent decades. In this study, a new kernel based on Littlewood-Paley Wavelet (LPW) is proposed for Extreme Learning Machine (ELM) algorithm to improve the accuracy of detecting multiple damages in structural systems. ELM is used as metamodel (surrogate model) of exact finite element analysis of structures in order to efficiently reduce the computational cost through updating process. In the proposed two-step method, first a damage index, based on Frequency Response Function (FRF) of the structure, is used to identify the location of damages. In the second step, the severity of damages in identified elements is detected using ELM. In order to evaluate the efficacy of ELM, the results obtained from the proposed kernel were compared with other kernels proposed for ELM as well as Least Square Support Vector Machine algorithm. The solved numerical problems indicated that ELM algorithm accuracy in detecting structural damages is increased drastically in case of using LPW kernel.
R. Ghiasi , M. R. Ghasemi ,
Volume 39, Issue 1 (8-2020)
Abstract
This paper focuses on the processing of structural health monitoring (SHM) big data. Extracted features of a structure are reduced using an optimization algorithm to find a minimal subset of salient features by removing noisy, irrelevant and redundant data. The PSO-Harmony algorithm is introduced for feature selection to enhance the capability of the proposed method for processing the measured big data, which have been collected from sensors of the structure and uncertainties associated with this process. Structural response signals under ambient vibration are preprocessed according to wavelet packet decomposition (WPD) and statistical characteristics for feature extraction. It optimizes feature vectors to be used as inputs to surrogate models based on the wavelet weighted support vector machine (WWLS-SVM) and radial basis function neural network (RBFNN). Two illustrative test examples are considered, the benchmark dataset from IASC-ASCE SHM group and a 120-bar dome truss. The results indicate that the features acquired by WPT from vibrational signal have higher sensitivity to the damage of the structure. Furthermore, the proposed PSO-Harmony is compared with four well-known metaheuristic optimization algorithms. The obtaind results show that the proposed method has a better performance and convergence rate. Finally, the proposed feature subset selection method has the capability of 90% data reduction
