• Advances in aircraft and spacecraft science
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• 제9권3호
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• pp.263-278
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• 2022

The time-varying structural reliability of an aeroelastic launch vehicle subjected to stochastic parameters is investigated. The launch vehicle structure is under the combined action of several stochastic loads that include aerodynamics, thrust as well as internal combustion pressure. The launch vehicle's main body structural flexibility is modeled via the normal mode shapes of a free-free Euler beam, where the aerodynamic loadings on the vehicle are due to force on each incremental section of the vehicle. The rigid and elastic coupled nonlinear equations of motion are derived following the Lagrangian approach that results in a complete aeroelastic simulation for the prediction of the instantaneous launch vehicle rigid-body motion as well as the body elastic deformations. Reliability analysis has been performed based on two distinct limit state functions, defined as the maximum launch vehicle tip elastic deformation and also the maximum allowable stress occurring along the launch vehicle total length. In this fashion, the time-dependent reliability problem can be converted into an equivalent time-invariant reliability problem. Subsequently, the first-order reliability method, as well as the Monte Carlo simulation schemes, are employed to determine and verify the aeroelastic launch vehicle dynamic failure probability for a given flight time.

• Smart Structures and Systems
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• 제29권2호
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Journal of Soil and Groundwater Environment
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• 제27권6호
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• pp.11-21
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• 2022

Sorption of phenanthrene (PHE) and pyrene (PYR) in several sorbents, i.e., natural soil, BionSoil^®, Pahokee peat, vermicompost and Devonian Ohio Shale and a surfactant (hexadecyltrimethyl ammonium chloride)-modified montmorillonite (HDTMA-M) were investigated. Pyrene exhibited higher sorption tendency than phenanthrene, as predicted by its higher octanol to water partition coefficient (K_ow). Several sorption models: linear, Freundlich, solubility-normalized Freundlich model, and Polanyi-Manes model (PMM) were used to analyze sorption isotherms. Linear isotherms were observed for natural soil, BionSoil^®, Pahokee peat, vermicompost, while nonlinear Freundlich isotherms fitted for Ohio shale and HDTMA-M. The relationship between sorption model parameters, organic carbon content (f_oc), and elemental C/N ratio was studied. In the binary competitive sorption of phenanthrene and pyrene in natural soil, competition between the solutes caused reduction in the sorption of each solute compared with that in the single-solute system. The ideal adsorbed solution theory (IAST) coupled with the single-solute Freundlich model was not successful in describing the binary competitive sorption equilibria. This was due to the inherent nature of linear sorption of phenanthrene and pyrene in natural soil. The result indicates that the applicability of IAST for the prediction of binary competitive sorption is limited when the sorption isotherms are inherently linear.

• Geomechanics and Engineering
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• 제33권3호
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• pp.261-269
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• 2023

In this study, the compressive strength of cement stabilized soil was predicted using the electrical resistivity measurement. The effects of the water to cement (w/c) ratio and recovered Carbon Black (rCB) contents were examined. A series of electrical resistivity and compressive strength tests were conducted on two types of stabilized soil after 28 days of curing. Multiple nonlinear regression (MNLR) analysis was used to evaluate the relationship between the compressive strength and the electrical resistivity in terms of the rCB, C_u (uniformity coefficient), and w/c ratio. The results showed that the w/c ratio and C_u have a strong influence on the compressive strength and electrical resistivity of the cement stabilized soil compared to the rCB content. The use of a small amount of rCB led to a decrease in the void space in the specimen and was attributed to the increase strength and decrease electrical resistivity. A high w/c ratio also induced a low electrical resistivity and compressive strength, whereas 3% rCB in the cemented soil provided the optimum strength for all w/c ratios. Finally, a prediction equation for the compressive strength using the electrical resistivity measurement was suggested based on its reliability, time effectiveness, non-destructiveness, and cost-effectiveness.

• Composites Research
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• 제21권4호
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• pp.22-28
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• 2008

The composite joint has become an important research area because the structural efficiency of a structure with a joint is determined by its joints rather than by its basic structure since the joints are often the weakest areas in composite structures. In this paper, the strengths of adhesive joints consisting of metal and composites were predicted and tested by the maximum strain theory and damage zone theory. Nonlinear finite element analyses of adhesive Joints considering the material nonlinearity of the adhesive layer were performed. From the tests and analyses, the strengths of the adhesive joints could be predicted to within 22.2% using the damage zone ratio.

• Transactions of Materials Processing
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• 제32권2호
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• pp.74-80
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• 2023

A deformation behavior of commercially pure titanium (CP-Ti) is highly dependent on material and processing parameters, such as deformation temperature, deformation direction, and strain rate. This study aims to predict the multivariable and nonlinear tensile behavior of CP-Ti using machine learning based on three algorithms: artificial neural network (ANN), light gradient boosting machine (LGBM), and long short-term memory (LSTM). The predictivity for tensile behaviors at the cryogenic temperature was lower than those in the room temperature due to the larger data scattering in the train dataset used in the machine learning. Although LGBM showed the lowest value of root mean squared error, it was not the best strategy owing to the overfitting and step-function morphology different from the actual data. LSTM performed the best as it effectively learned the continuous characteristics of a flow curve as well as it spent the reduced time for machine learning, even without sufficient database and hyperparameter tuning.

• Steel and Composite Structures
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• 제46권6호
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• 제58권2호
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• pp.159-174
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• 2022

Flow prediction was carried out through observational survey and three dimensional multi-layered numerical diagnostic model experiment to clarify the time and spatial structure of tidal current and residual flow dominant in the sea exchange and material circulation of the waters around Geumo Islands in the southern waters of Korea. The horizontal variation of tidal current is so large that it causes asymmetric tidal mixing due to horizontal eddies and the topographical effect creating convergence and dispersion of flow direction and velocity. Due to strong tidal currents flowing northwest-southeast, counterclockwise and clockwise eddies are formed on the left and right sides of the south of Sori Island. These topographical eddies are created by horizontal turbulence and bottom friction causing nonlinear effects. Baroclinic density flows are less than 5 cm/s at coastal area in summer and the entire sea area in winter. The wind driven currents assuming summer and winter seasonal winds are also less than 5 cm/s and the current flow rate is high in winter. Density current in summer and wind driven current in winter have a relatively greater effect on the net residual flows (tidal residual current + density current + density driven current) around Geumo Islands Sea area.

• The Journal of shipping and logistics
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• 제35권1호
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• pp.93-109
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• 2019

This study applied the statistically significant factors to the short-run model in the existing nonlinear long-run equilibrium relation analysis for the forecasting of maritime economy using the mixed cycle model. The most common univariate AR(1) model and out-of-sample forecasting are compared with the root mean squared forecasting error from the mixed-frequency model, and the prediction power of the mixed-frequency approach is confirmed to be better than the AR(1) model. The empirical results from the analysis suggest that the new approach of high-level mixed frequency model is a useful for forecasting marine industry. It is consistent that the inclusion of more information, such as higher frequency, in the analysis of long-run equilibrium framework is likely to improve the forecasting power of short-run models in multivariate time series analysis.

• KIPS Transactions on Computer and Communication Systems
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• 제13권1호
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• pp.1-9
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• 2024

In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.