• Earthquakes and Structures
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• v.27 no.5
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• pp.361-383
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• 2024

Earthquake soil-structure interaction (ESSI) is the dynamic interaction between seismic waves, soil layers underlying structures, and the structures themselves during earthquakes, which affects the structures' response. This relationship impacts foundation behaviour, soil amplification, energy dissipation, nonlinear effects, resonance phenomena, and earthquake design considerations. Comprehending ESSI is crucial for evaluating structural performance, creating resilient structures and executing efficient seismic retrofitting procedures in earthquake-prone areas. Present seismic standards do not account for interbuilding dynamic interactions through the soil, and hence the associated seismic risk is ignored. However, due to recent population growth in cities and rising land costs, there has been a rise in city building surface density, resulting in buildings being more closely spaced. The seismic analysis of a city with high building surface density is very complex due to detailed requirement material and geometrical properties of historical as well as present structures. The construction of new building adjacent to preexisting building can either reduce or increase its structural response. This phenomenon of dynamic interaction between existing and newly built buildings is known as dynamic cross interaction (DCI) whereas site-city interactions (SCI) describe the effects of a group of structures on the overall seismic response of the site or city. This study covers the entire literature review of the pioneer findings in the field of ESSI considering different types of structures, mitigation techniques, ESSI modelling techniques, comparison between experimental and numerical techniques for earthquake analysis and latest concepts related to ESSI, DCI and SCI further the research gaps and future scope is also discussed.

• Journal of Korean Society of Forest Science
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• v.86 no.3
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• pp.365-377
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• 1997

Much of the Cumberland Plateau of Tennessee, USA is in mixed hardwoods for which there are no applicable growth and yield predictors. Use of site index as a variable in growth and yield prediction models is limited in most stands because their history is not known and many may not be even-aged. Landtypes may offer an alternative to site index for these mixed stands because they were designed to include land of about equal productivity. To determine vegetation by landtype, dependency between landtype and detailed forest type was tested with Chi-square. Differences in productivity among landtypes were tested by employing regression analyses and analysis of variance(ANOVA). Basal area growth was fitted to the nonlinear models developed by Moser and Hall(1969). Basal area growth and volume growth were also predicted as a function of initial total basal area and initial volume with linear regression by landtype and by landtype class. Differences in basal area growth and volume growth by landtype were tested with ANOVA. Dependency between site class and landtype was tested with Chi-square. Vegetation types seem to be related to landtypes in the study area although the validity of the test is questionable because of a high proportion of sparsely occupied cells. No statistically significant differences in productivity among landtypes were found in this study.

• Proceedings of the Korean Geotechical Society Conference
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• 1994.09a
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• pp.235-246
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• 1994

At pre-construction design stage, most of the design data are based on the site investigation results or property estimation which often does not provide satisfactory output for the tunnel analysis. Nonlinear FEM tunnel analysis was cariied out by Hoek-Brown model which is principly semi-empirical design method based on insitu rock descriptions, rock test results as well as field measurement data. The results of the analytical methods from Hoek-Brown model and Mohr-Coulomb model are compared with the sige measurement data from two-NATM tunnel construction sites. It was found that the Hoek-Brown model can be satisfactorily adopted as a feed back analysis technique in order to examin the safety of NATM tunnel at any construction stage.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.04a
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• pp.367-374
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• 2000

This study investigates the effect of hysteretic characteristics to the Inelastic Demand Spectrum (IDS) which was expressed by an acceleration(Sa) and a displacement response spectrum (Sd). Elasto Perfectly Plastic(EPP) model is used in this study and inelastic demand spectrum (Sa vs, Sd) are obtained from a given target ductility ratio. For a given target ductility ratio IDS can be obtained by using nonlinear time history analysis of single degree of system with forth five recorded earthquake ground motions for stiff soil site. The effect EPP model under demand spectrum is investigated by ductility factor and natural frequency. According to the results obtained in this study IDS has dependency on ductility factor and natural frequency.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.663-670
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• 2003

Urban tunnelling need to consider not only the stability of tunnel itself but also the ground movement regarding adjacent structures. This paper present 3-D behavior of adjacent structures due to tunnelling induced ground movements by means of field measuring data and nonlinear FEM tunnel analysis. The results of the analytical methods from Mohr-Coulomb model are compared with the site measurement data obtained during the twin tunnel construction. It was found that the location and stiffness of the structure influence greatly the shape and pattern of settlement trough. The settlement trough for Greenfield condition was different from the trough for existing adjacent structures. Therefore the load and stiffness of adjacent structures should be taken into account for the stability analysis of the structures.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.545-550
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• 2002

Concrete is one of the essential structural materials in the construction. But, concrete consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructor. Therefore, concrete mixes depend on experiences of experts. However, it is more and more difficult to determine concrete mixes design by empirical means because more ingredients like mineral and chemical admixtures are included. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network are used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength and slump are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

• Earthquakes and Structures
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• v.10 no.4
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• pp.965-988
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• 2016

This paper investigates the seismic behavior of reinforced concrete shear wall buildings with multiple underground stories. A base-case where the buildings are modeled with a fixed condition at ground level is adopted, and then the number of basements is incrementally increased to evaluate changes in performance. Two subsurface site conditions, corresponding to very dense sands and medium dense sands, are used for the analysis. In addition, three ground shaking levels are used in the study. Results of the study indicated that while the common design practice of cropping the structure at the ground surface leads to conservative estimation of the base shear for taller and less rigid structures; it results in unpredicted and nonconservative trends for shorter and stiffer structures.

• Journal of the Korean Magnetic Resonance Society
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• v.3 no.2
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• pp.84-89
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• 1999

The 87Rb quadrupole coupling constants (e2qQ/h) and the asymmetry parameters (η) in RbMnCl3 were determined from a nonlinear least-squares fit to the 87Rb NMR powder spectra. The spectra were acquired in the temperature range from 260K to 330K. An important feature in this work is the determination of the quadrupole coupling constants and the asymmetry parameters for two physically nonequivalent Rb sites, Rb(I) and Rb(II), as a function of temperature. In addition, a structural phase transition at room temperature was conformed with the changes in the quadrupole coupling constant and the asymmetry parameter of Rb(II) site.

• International Journal of Fluid Machinery and Systems
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• v.8 no.2
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• pp.63-72
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• 2015

Water hammer phenomena are important issues for the design and the operation of hydro power plants. Especially, if several reversible pump-turbines are coupled hydraulically there may be strong unit interactions. The precise prediction of all relevant transients is challenging. Regarding a recent pump-storage project, dynamic measurements motivate an improved turbine modeling approach making use of a Thoma number dependency. The proposed method is validated for several transient scenarios and turns out to improve correlation between measurement and simulation results significantly. Starting from simple scenarios, this allows better prediction of more complex transients. By applying a fully automated simulation procedure broad operating ranges of the highly nonlinear system can be covered providing a consistent insight into the plant dynamics. This finally allows the optimization of the closing strategy and hence the overall power plant performance.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.786-789
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• 2002

A recurrent artificial neural network (ANN) model is developed to simulate hydrocarbon recovery process at petroleum-contaminated site. The groundwater extraction rate, vacuum pressure, and saturation hydraulic conductivity are selected as the input variables, while the cumulative hydrocarbon recovery volume is considered as the output variable. The experimental data fer establishing the ANN model are from implementation of a multiphase flow model for dual phase remediation process under different input variable conditions. The complex nonlinear and dynamic relationship between input and output data sets are then identified through the developed ANN model. Reasonable agreements between modeling results and experimental data are observed, which reveals high effectiveness and efficiency of the neural network approach in modeling complex hydrocarbon recovery behavior.