Showing 3 results for Surrogate Model
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
N. Cheraghi, M. Miri, M. Rashki,
Volume 39, Issue 1 (8-2020)
Abstract
This paper presents a probabilistic assessment on the free vibration analysis of functionally graded material plates, including layers with magneto-electro-elastic properties, using the 3D solution and surrogate models. The plate is located on an elastic foundation and the intra-layer slipping effect is also considered in the analysis by employing the generalized intra-layer spring model. Due to the high computational cost of the 3D solution in calculating the free vibration frequency of the plate, surrogate models are used. The meta models including kriging method, radial fundamental function method and polynomial response surface method are used to construct the surrogate model. For surrogate models training, the results of the three-dimensional solving method are used. The elastic foundation hardness, the intra-layer slipping effect, the material properties index, and the layer densities are considered as the variables with uncertainty. The three-dimensional solution method is validated through a comparison with other available reference. The results obtained through the surrogate models have been compared to those of the 3D solution formulation, showing a good agreement. The effects of some parameters including the elastic foundation hardness, the intra-layer slipping effect, the density of each layer, and the material properties index on the fundamental frequency of functionally graded material plates are investigated. By using three-dimensional solution method and Kriging Surrogate Model, it is shown that the shear and transverse components of elastic foundation hardness and the density of each layer have the greatest effect on the fundamental frequency of the functionally graded material plates.
