• Steel and Composite Structures
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• v.33 no.4
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• pp.595-614
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• 2019

In cold-formed steel (CFS) structures, such as trusses, transmission towers and portal frames, the use of back-to-back built-up CFS unequal angle sections are becoming increasingly popular. In such an arrangement, intermediate welds or screw fasteners are required at discrete points along the length, preventing the angle sections from buckling independently. Limited research is available in the literature on axial strength of back-to-back built-up CFS unequal angle sections. The issue is addressed herein. This paper presents an experimental investigation on both the welded and screw fastened back-to-back built-up CFS unequal angle sections under axial compression. The load-axial shortening and the load verses lateral displacement behaviour along with the deformed shapes at failure are reported. A nonlinear finite element (FE) model was then developed, which includes material non-linearity, geometric imperfections and modelling of intermediate fasteners. The FE model was validated against the experimental test results, which showed good agreement, both in terms of failure loads and deformed shapes at failure. The validated FE model was then used for the purpose of a parametric study to investigate the effect of different thicknesses, lengths and, yield stresses of steel on axial strength of back-to-back built-up CFS unequal angle sections. Five different thicknesses and seven different lengths (stub to slender columns) with two different yield stresses were investigated in the parametric study. Axial strengths obtained from the experimental tests and FE analyses were used to assess the performance of the current design guidelines as per the Direct Strength Method (DSM); obtained comparisons show that the current DSM is conservative by only 7% on average, while predicting the axial strengths of back-to-back built-up CFS unequal angle sections.

• Geomechanics and Engineering
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• v.25 no.3
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• pp.195-205
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• 2021

Time effect on the deformation and strength characteristics of geogrid reinforced sand retaining wall has become an important issue in geotechnical and transportation engineering. Three physical model tests on geogrid reinforced sand retaining walls performed under various loading conditions were simulated to study their rate-dependent behaviors, using the presented nonlinear finite element method (FEM) analysis procedure. This FEM was based on the dynamic relaxation method and return mapping scheme, in which the combined effects of the rate-dependent behaviors of both the backfill soil and the geosynthetic reinforcement have been included. The rate-dependent behaviors of sands and geogrids should be attributed to the viscous property of materials, which can be described by the unified three-component elasto-viscoplastic constitutive model. By comparing the FEM simulations and the test results, it can be found that the present FEM was able to be successfully extended to the boundary value problems of geosynthetic reinforced soil retaining walls. The deformation and strength characteristics of the geogrid reinforced sand retaining walls can be well reproduced. Loading rate effect, the trends of jump in footing pressure upon the step-changes in the loading rate, occurred not only on sands and geogrids but also on geogrid reinforced sands retaining walls. The lateral earth pressure distributions against the back of retaining wall, the local tensile force in the geogrid arranged in the retaining wall and the local stresses beneath the footing under various loading conditions can also be predicted well in the FEM simulations.

• Steel and Composite Structures
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• v.42 no.1
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• pp.1-22
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• 2022

In cold-formed steel (CFS) structures, such as trusses, transmission towers and portal frames, the use of back-to-back built-up CFS unequal angle sections are becoming increasingly popular. In such an arrangement, intermediate welds or screw fasteners are required at discrete points along the length, preventing the angle sections from buckling independently. Limited research is available in the literature on axial strength of back-to-back built-up CFS unequal angle sections. The issue is addressed herein. This paper presents an experimental investigation reported by the authors on back-to-back built-up CFS unequal angle sections with intermediate stiffeners under axial compression. The load-axial shortening behaviour along with the deformed shapes at failure are reported. A nonlinear finite element (FE) model was then developed, which includes material non-linearity, geometric imperfections and modelling of intermediate fasteners. The FE model was validated against the experimental test results, which showed good agreement, both in terms of failure loads and deformed shapes at failure. The validated finite element model was then used for the purpose of a parametric study comprising 96 models to investigate the effect of longer to shorter leg ratios, stiffener provided in the longer leg, thicknesses and lengths on axial strength of back-to-back built-up CFS unequal angle sections. Four different thicknesses and seven different lengths (stub to slender columns) with three overall widths to the overall depth (B/D) ratios were investigated in the parametric study. Axial strengths obtained from the experimental tests and FE analyses were used to assess the performance of the current design guidelines as per the Direct Strength Method (DSM); obtained comparisons show that the current DSM is conservative by only 7% and 5% on average, while predicting the axial strengths of back-to-back built-up CFS unequal angle sections with and without the stiffener, respectively.

• Geomechanics and Engineering
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• v.35 no.4
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• pp.449-464
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• 2023

Advanced nonlinear effective stress constitutive models are started to be frequently used in one-dimensional (1D) and two-dimensional (2D) site response analysis for assessment of porewater generation and liquefaction potential in soft soil deposits. The emphasis of this research is on the assessment of the implementation of this category of models at the element stage. Initially, the performance of a coupled porewater pressure (PWP) and constitutive models were evaluated employing a catalogue of 40 unidirectional cyclic simple shear tests with a variety of relative densities between 35% and 80% and effective vertical stresses between 40 and 80 kPa. The authors evaluated three coupled constitutive models (PDMY02, PM4SAND and PDMY03) using cyclic direct simple shear tests and for decide input parameters used in the model, procedures are recommended. The ability of the coupled model to capture dilation as strength is valuable because the studied models reasonably capture the cyclic performance noted in the experiments and should be utilized to conduct effective stress-based 1D and 2D site response analysis. Sandy soils may become softer and liquefy during earthquakes as a result of pore-water pressure (PWP) development, which may have an impact on seismic design and site response. The tested constitutive models are mathematically coupled with a cyclic strain-based PWP generation model and can capture small-strain stiffness and large-strain shear strength. Results show that there are minor discrepancies between measured and computed excess PWP ratios, indicating that the tested constitutive models provide reasonable estimations of PWP increase during cyclic shear (r_u) and the banana shape is reproduced in a proper way indicating that dilation and shear- strain behavior is well captured by the models.

• Computers and Concrete
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• v.11 no.2
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• pp.123-148
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• 2013

The nonlinear finite element method with eight noded isoparametric quadrilateral element for concrete and two noded element for reinforcement is used for the prediction of the behavior of reinforcement concrete structures. The disturbed state concept (DSC) including the hierarchical single surface (HISS) plasticity model with associated flow rule with modifications is used to characterize the constitutive behavior of concrete both in compression and in tension which is named DSC/HISS-CT. The HISS model is applied to shows the plastic behavior of concrete, and DSC for microcracking, fracture and softening simulations of concrete. It should be noted that the DSC expresses the behavior of a material element as a mixture of two interacting components and can include both softening and stiffening, while the classical damage approach assumes that cracks (damage) induced in a material treated acts as a void, with no strength. The DSC/HISS-CT is a unified model with different mechanism, which expresses the observed behavior in terms of interacting behavior of components; thus the mechanism in the DSC is much different than that of the damage model, which is based on physical cracks which has no strength and interaction with the undamaged part. This is the first time the DSC/HISS-CT model, with the capacity to account for both compression and tension yields, is applied for concrete materials. The DSC model allows also for the characterization of non-associative behavior through the use of disturbance. Elastic perfectly plastic behavior is assumed for modeling of steel reinforcement. The DSC model is validated at two levels: (1) specimen and (2) practical boundary value problem. For the specimen level, the predictions are obtained by the integration of the incremental constitutive relations. The FE procedure with DSC/HISS-CT model is used to obtain predictions for practical boundary value problems. Based on the comparisons between DSC/HISS-CT predictions, test data and ANSYS software predictions, it is found that the model provides highly satisfactory predictions. The model allows computation of microcracking during deformation leading to the fracture and failure; in the model, the critical disturbance, Dc, identifies fracture and failure.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.97-106
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• 2011

Mode choice model is an essential element for estimating- the demand of new means of transportation in the planning stage as well as in the establishment phase. In general, current demand analysis model developed for the mode choice analysis applies common parameters of utility function in each region which causes inaccuracy in forecasting mode choice behavior. Several critical problems from using common parameters are: a common parameter set can not reflect different distribution of coefficient for travel time and travel cost by different population. Consequently, the resulting model fails to accurately explain policy variables such as travel time and travel cost. In particular, the nonlinear logit model applied to aggregation data is vulnerable to the aggregation error. The purpose of this paper is to consider the regional characteristics by adopting the parameters fitted to each area, so as to reduce prediction errors and enhance accuracy of the resulting mode choice model. In order to estimate parameter of each area, this study used Household Travel Survey Data of Metropolitan Transportation Authority. For the verification of the model, the value of time by marginal rate of substitution is evaluated and statistical test for resulting coefficients is also carried out. In order to crosscheck the applicability and reliability of the model, changes in mode choice are analyzed when Seoul subway line 9 is newly opened and the results are compared with those from the existing model developed without considering the regional characteristics.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.3
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• pp.34-40
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• 2012

A pressure control law to regulate pressure of gas generator is suggested. To design a model based control law, the governing equation which consists of Robert and conservation equation is built and verified through the ground burning test. PID and nonlinear adaptive control laws are designed to analyze the loop response characteristics under the system which has varying eigen properties arisen from combustion volume change. It is suggested that new approach, gain scheduling design, is required to overcome the defects identified from numerical simulation results of the two control laws. The newly suggested scheme shows good control performance even under disturbances and measurement noise.

• Journal of the Optical Society of Korea
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• v.17 no.2
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• pp.213-218
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• 2013

Although infrared focal plane array (IRFPA) detectors have been commonly used, non-uniformity correction (NUC) remains an important problem in the infrared imaging realm. Non-uniformity severely degrades image quality and affects radiometric accuracy in infrared imaging applications. Residual non-uniformity (RNU) significantly affects the detection range of infrared surveillance and reconnaissance systems. More effort should be exerted to improve IRFPA uniformity. A novel NUC method that considers the surrounding temperature variation compensation is proposed based on the binary nonlinear non-uniformity theory model. The implementing procedure is described in detail. This approach simultaneously corrects response nonlinearity and compensates for the influence of surrounding temperature shift. Both qualitative evaluation and quantitative test comparison are performed among several correction technologies. The experimental result shows that the residual non-uniformity, which is corrected by the proposed method, is steady at approximately 0.02 percentage points within the target temperature range of 283 K to 373 K. Real-time imaging shows that the proposed method improves image quality better than traditional techniques.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.4D
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• pp.571-576
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• 2011

In general, conventional road pavements are designed under the assumption that the shear strength of geomaterials are under saturated state. In reality, however, most of the pavement geomaterials exists under the unsaturated state. To deal with this gap between saturated and unsaturated conditions, in this paper, unsaturated shear strength was estimated using the results from the triaxial compression test and soil-water characteristics curves. Then, yield loads were assessed using 2-Dimensional finite element method with the selected nonlinear elastic model and the Mohr-Coulomb yield criteria. In addition, various unsaturated condition and surface layer effects on the yield load of granular materials were identified. Therefore, the results demonstrated would provide a possibility to estimate bearing capacity of paved or unpaved roads using unsaturated soil mechanics.