• Structural Engineering and Mechanics
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• v.46 no.1
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• pp.53-73
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• 2013

Prediction of prestressed concrete girder integral abutment bridge (IAB) load effect requires understanding of the inherent uncertainties as it relates to thermal loading, time-dependent effects, bridge material properties and soil properties. In addition, complex inelastic and hysteretic behavior must be considered over an extended, 75-year bridge life. The present study establishes IAB displacement and internal force statistics based on available material property and soil property statistical models and Monte Carlo simulations. Numerical models within the simulation were developed to evaluate the 75-year bridge displacements and internal forces based on 2D numerical models that were calibrated against four field monitored IABs. The considered input uncertainties include both resistance and load variables. Material variables are: (1) concrete elastic modulus; (2) backfill stiffness; and (3) lateral pile soil stiffness. Thermal, time dependent, and soil loading variables are: (1) superstructure temperature fluctuation; (2) superstructure concrete thermal expansion coefficient; (3) superstructure temperature gradient; (4) concrete creep and shrinkage; (5) bridge construction timeline; and (6) backfill pressure on backwall and abutment. IAB displacement and internal force statistics were established for: (1) bridge axial force; (2) bridge bending moment; (3) pile lateral force; (4) pile moment; (5) pile head/abutment displacement; (6) compressive stress at the top fiber at the mid-span of the exterior span; and (7) tensile stress at the bottom fiber at the mid-span of the exterior span. These established IAB displacement and internal force statistics provide a basis for future reliability-based design criteria development.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.2
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• pp.155-164
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• 2009

In this paper, a spectral finite element method for a rectangular sandwich plate with viscoelastic core having the Levy-type boundary conditions has been plated. The sandwich plate consists of two isotropic and elastic face plates with a surfaced-bonded viscoelastic core. For the analysis, the in-plane and transverse energy in the face plates and only shear energy in the core are considered, respectively. To account for the frequency dependent complex shear modulus of the viscoelastic core, the Golla-Hughes-McTavish model is adopted. To evaluate the validity and accuracy of the proposed method, the frequency response function and dynamic responses of the sandwich plate with all edges simply supported subject to an impact load are calculated and compared with those calculated by a finite element method. Though these calculations, it is confirmed that the proposed method is very reliable and efficient one for vibration analysis of a rectangular sandwich plate with viscoelastic core having the Levy-type boundary conditions.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.103-115
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• 2011

Concrete slab curls and warps due to the uneven distribution of temperature and moisture and as the result, internal stress develops within the slab. Therefore, environmental loads must be considered in addition to the traffic loads to predict the lifespan of the concrete pavement more accurately. The strength of the concrete slab is gradually decreases to a certain level at which fatigue cracking is generated by the repetitive traffic and environmental loadings. In this study, a new fatigue regression model was developed based on the results from previously performed studies. To verify the model, another laboratory flexural fatigue test program which was not used in the model development, was conducted and compared with the predictions of other existing models. Each fatigue model was applied to analysis logic of cumulative fatigue damage of concrete pavement developed in the study. The sensitivity of cumulative fatigue damage calculated by each model was analyzed for the design factors such as slab thickness, joint spacing, complex modulus of subgrade reaction and the load transfer at joints. As the result, the model developed in this study could reflect environmental loading more reasonably by improving other existing models which consider R, minimum/maximum stress ratio.

• Journal of the Korean Ceramic Society
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• v.49 no.5
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• pp.475-483
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• 2012

High capacitance X5R MLCCs based on $BaTiO_3$ ceramic dielectric layers exhibit a single broad, asymmetric arc shape impedance and modulus response over the wide frequency range between 1 MHz to 0.01 Hz. Analysis according to the conventional brick-layer model for polycrystalline conductors employing a series connection of multiple RC parallel circuits leads to parameters associated with large errors and of little physical significance. A new parametric impedance model is shown to satisfactorily describe the experimental spectra, which is a parallel network of one resistor R representing the DC conductivity thermally activated by 1.32 eV, one ideal capacitor C exactly representing bulk capacitance, and a constant phase element (CPE) Q with complex capacitance $A(i{\omega})^{{\alpha}-1}$ with ${\alpha}$ close to 2/3 and A thermally activated by 0.45 eV or ca. 1/3 of activation energy of DC conductivity. The feature strongly indicate the CK1 model by J. R. Macdonald, where the CPE with 2/3 power-law exponent represents the polarization effects originating from mobile charge carriers. The CPE term is suggested to be directly related to the trapping of the electronic charge carriers and indirectly related to the ionic defects responsible for the insulation resistance degradation.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.107-118
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• 2022

The social need for stable hydrogen storage technologies that respond to the increasing demand for hydrogen energy is increasing. Among them, underground hydrogen storage is recognized as the most economical and reasonable storage method because of its vast hydrogen storage capacity. In Korea, low-depth hydrogen storage using artificial protective structures is being considered. Further, establishing corresponding safety standards and ground stability evaluation is becoming essential. This study evaluated the hydro-mechanical behavior of the ground during a hydrogen gas leak from a low-depth underground hydrogen storage facility through the HM coupled analysis model. The predictive reliability of the simulation model was verified through benchmark experiments. A parameter study was performed using a metamodel to analyze the sensitivity of factors affecting the surface uplift caused by the upward infiltration of high-pressure hydrogen gas. Accordingly, it was confirmed that the elastic modulus of the ground was the largest. The simulation results are considered to be valuable primary data for evaluating the complex analysis of hydrogen gas explosions as well as hydrogen gas leaks in the future.

• Journal of the Korean Geotechnical Society
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• v.27 no.5
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• pp.85-92
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• 2011

The dynamic behavior of piles becomes very complex due to soil-pile dynamic interaction, soil non-linearity, resonance phenomena of soil-pile system and so on. Therefore, the proper numerical simulation of the pile behavior needs much effort and calculation time. In this research, a new modeling method, which can be applied to the conventional finite difference analysis program FLAC 3D, was developed to reduce the calculation time. The soil domain in this method is divided into a near-field region and a far-field region, which is not influenced by the soil-pile dynamic interaction. Then, the ground motion of the far-field is applied to the boundaries of the near-field instead of modeling the far-field region as finite meshes. In addition, the soil non-linearity behavior is modeled by using the hysteretic damping model, which determines the soil tangent modulus as a function of shear strain and the interface element was applied to simulate the separation and slip between the soil and pile. The proposed method reduced the calculation time by as much as one third compared with a usual modeling method and maintained the accuracy of the calculated results. The calculated results by the proposed method showed a good agreement with the prototype pile behavior, which was obtained by applying a similitude law to the 1-g shaking table test results.

• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.