• Structural Engineering and Mechanics
• /
• v.65 no.2
• /
• pp.173-181
• /
• 2018

In this paper, finite element (FE) model updating based on multi-objective optimization with the surrogate model for a steel plate girder bridge is investigated. Conventionally, FE model updating for bridge structures uses single-objective optimization with finite element analysis (FEA). In the case of the conventional method, computational burden occurs considerably because a lot of iteration are performed during the updating process. This issue can be addressed by replacing FEA with the surrogate model. The other problem is that the updating result from single-objective optimization depends on the condition of the weighting factors. Previous studies have used the trial-and-error strategy, genetic algorithm, or user's preference to obtain the most preferred model; but it needs considerable computation cost. In this study, the FE model updating method consisting of the surrogate model and multi-objective optimization, which can construct the Pareto-optimal front through a single run without considering the weighting factors, is proposed to overcome the limitations of the single-objective optimization. To verify the proposed method, the results of the proposed method are compared with those of the single-objective optimization. The comparison shows that the updated model from the multi-objective optimization is superior to the result of single-objective optimization in calculation time as well as the relative errors between the updated model and measurement.

• Smart Structures and Systems
• /
• v.17 no.4
• /
• pp.611-629
• /
• 2016

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

• Structural Engineering and Mechanics
• /
• v.41 no.1
• /
• pp.25-41
• /
• 2012

Kriging surrogate model provides explicit functions to represent the relationships between the inputs and outputs of a linear or nonlinear system, which is a desirable advantage for response estimation and parameter identification in structural design and model updating problem. However, little research has been carried out in applying Kriging model to crack identification. In this work, a scheme for crack identification based on a Kriging surrogate model is proposed. A modified rectangular grid (MRG) is introduced to move some sample points lying on the boundary into the internal design region, which will provide more useful information for the construction of Kriging model. The initial Kriging model is then constructed by samples of varying crack parameters (locations and sizes) and their corresponding modal frequencies. For identifying crack parameters, a robust stochastic particle swarm optimization (SPSO) algorithm is used to find the global optimal solution beyond the constructed Kriging model. To improve the accuracy of surrogate model, the finite element (FE) analysis soft ANSYS is employed to deal with the re-meshing problem during surrogate model updating. Specially, a simple method for crack number identification is proposed by finding the maximum probability factor. Finally, numerical simulations and experimental research are performed to assess the effectiveness and noise immunity of this proposed scheme.

• Smart Structures and Systems
• /
• v.22 no.5
• /
• pp.561-574
• /
• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Nuclear Engineering and Technology
• /
• v.52 no.1
• /
• pp.109-114
• /
• 2020

This work presents the results of laboratory scale tests of the CARBOFLUOREX (CARBOnate FLUORide EXtraction) process - a novel technology for the recovery of U and Pu from the solid fluorides residue (fluorination ash) of Fluoride Volatility Method (FVM) reprocessing of spent nuclear fuel (SNF). To study the oxidative leaching of U from the fluorination ash (FA) by Na₂CO₃ or Na₂CO₃-H₂O₂ solutions followed by solvent extraction by methyltrioctylammonium carbonate in toluene and purification of U from the fission products (FPs) impurities we used a surrogate of FA consisting of UF₄ or UO₂F₂, and FPs fluorides with stable isotopes of Ce, Zr, Sr, Ba, Cs, Fe, Cr, Ni, La, Nd, Pr, Sm. Purification factors of U from impurities at the solvent extraction refining stage reached the values of 10⁴-10⁵, and up to 10⁶ upon the completion of the processing cycle. Obtained results showed a high efficiency of the CARBOFLUOREX process for recovery and separating of U from FPs contained in FA, which allows completing of the FVM cycle with recovery of U and Pu from hardly processed FA.

• Steel and Composite Structures
• /
• v.32 no.4
• /
• pp.455-466
• /
• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• The Journal of the Acoustical Society of Korea
• /
• v.40 no.1
• /
• pp.64-72
• /
• 2021

This paper carried out the optimal design of Tuned Mass Damper (TMD) to attenuate the vibrational energy of pipeline subjected to fluid movement. Under the uncertainty of the vibration source and the specification of a pipeline system, an adaptive approach to design TMD is suggested. A surrogate pipeline system model was designed using MATLAB, and the optimal design method was developed based on the surrogate pipe model. The developed optimization method was validated using Finite Element (FE) model in ANSYS Workbench. And the TMD was designed to account for measurement error and installed on the industrial pipeline system. It showed that the pipeline vibrational amplitude was reduced by 95 % after installing the TMD.

• Structural Engineering and Mechanics
• /
• v.83 no.3
• /
• pp.293-304
• /
• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• Journal of Korean Society of Environmental Engineers
• /
• v.34 no.9
• /
• pp.590-596
• /
• 2012

Enhanced coagulation is best available technologies to treat NOM in water to produce clean drinking water. In this research, the comparison experiments between conventional coagulation (CC) and enhanced coagulation (EC) using 4 type coagulants i.e., ferric chloride, aluminium sulphate (alum), poly aluminium sulphate organic magnesium (PSOM) and poly aluminium chloride (PACl) were performed in terms of surrogate parameters such as dissolved organic carbon (DOC), trihalomethane formation potential (THMFP), haloacetic acid formation potential (HAAFP) and zeta potential variation in order to find out the most effective coagulant and conditions to fit Nakdong River water. When applied to EC process, the turbidity removal efficiency did not increased gradually compared to the CC process when adding coagulants. Furthermore, the removal efficiency of turbidity became decreased much more as coagulants were added increasingly whereas the removal efficiency of DOC, THMFP and HAAFP became increased by 13~18%, 9~18% and 9~18% respectively compared to the CC process. The characteristics of turbidity removal showed relatively high removal efficiency considering the pH variation in entire pH range when using $FeCl_3$ and PACl. Additionally, in case of alum and PSOM steady removal efficiency was shown between pH 5 and pH 8. In terms of DOC surrogate the coagulants including 4 type coagulants indicated high removal efficiency between pH 5 and pH 7. The removal efficiency of dissolved organic matter (DOM) in EC between less than 1 kDa and more than 10 kDa augmented by 11~21% and 16% respectively compared to the CC process. The removal efficiency of hydrophobic and hydrophilic organic matter proved to be increased by 27~38% and 11~15% respectively. In conclusion, the most effective coagulant relating to EC for Nakdong River water was proved to be $FeCl_3$ followed by PSOM, PAC and alum in order.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.3 no.4
• /
• pp.309-317
• /
• 2005

A study on the separation of $^{99}Tc,\;^{94}Nb,\;^{55}Fe,\;^{90}Sr\;and\;^{59/63}Ni$ in various radioactive wastes discharged from nuclear power plants has been performed for a use in their quantification which is indispensible for the evaluation of the radionuclide inventory Ni was recovered along with Ca, Mg, Al, Cr, Ti, Mn, Ce, Na, K, and Cu through the sequential separation procedure of Re(as a surrogate of $^{99}Tc$), Nb, Fe and Sr by anion exchange and Sr-Spec extraction chromatography. In this research, chemical separation of Ni from the co-existing elements was investigated by cation exchange and Ni-Spec extraction chromatography. Precipitation behaviour of Ni and the co-existing elements with dimethylglyoxime(DMG) was investigated in ammonium $citrate/ethanol-H_2O$ and tartaric $acid/acetone-H_2O$ in order to purify separated Ni fractions and to prepare $^{59/63}Ni$ source for the radioactivity measurement using a gas proportional counter. Recovery of Ni separated through ion exchange chromatographic separation procedure was $92.1\%$ with relative standard deviation of $0.9\%$. In addition, recovery of Ni with DMG in the tartaric $acid/acetone-H_2O$ was $85.6\%$ with relative standard deviation of $1.9\%$.