• Geomechanics and Engineering
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• v.15 no.1
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• pp.631-643
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• 2018

The estimation of the strength and other mechanical parameters characterizing the tensile behavior of granites can play an important role in civil engineering tasks such as design, construction, rehabilitation and repair of existing structures. The purpose of this paper is to apply data mining techniques, such as multiple regression (MR), artificial neural networks (ANN) and support vector machines (SVM) to estimate the mechanical properties of granites. In a first phase, the mechanical parameters defining the complete tensile behavior are estimated based on the tensile strength. In a second phase, the estimation of the mechanical properties is carried out from different combination of the physical properties (ultrasonic pulse velocity, porosity and density). It was observed that the estimation of the mechanical properties can be optimized by combining different physical properties. Besides, it was seen that artificial neural networks and support vector machines performed better than multiple regression model.

• Computers and Concrete
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• v.32 no.6
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• pp.595-606
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• 2023

Dune sand (DS) has been widely used as a partial replacement for regular sand in concrete construction. Therefore, investigating its mechanical properties is critical for the analysis and design of structural elements using DS as a construction material. This paper presents a comprehensive investigation of the mechanical properties of DS concrete, considering different replacement ratios and strength grades. Regression analysis is utilized to develop strength prediction models for different mechanical properties of DS concrete. The proposed models exhibit high calculation accuracy, with R² values of 0.996, 0.991, 0.982, and 0.989 for cube compressive strength, axial compressive strength, splitting tensile strength, and elastic modulus, respectively, and an error within ±20%. Furthermore, a stress-strain relationship specific to DS concrete is established, showing good agreement with experimental results. Additionally, nonlinear finite element analysis is performed on concrete-filled steel tube columns incorporating DS concrete, utilizing the established stress-strain relationship. The analytical and experimental results exhibit good agreement, confirming the validity of the proposed stress-strain relationship for DS concrete. Therefore, the findings presented in this paper provide valuable references for the design and analysis of structures utilizing DS concrete as a construction material.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.4
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• pp.519-528
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• 1986

본 연구에서는 우선 1차적으로 한국중공업에서 제조한 실제의 터어빈 회전자 축에서 시편을 채취하여 화력발전소 터어빈의 작동 온도에서의 크리이프 거동을 실험 하고, Larson-Miller 법 및 Orr-sherby-Dorn 법에 의하여 수명을 예상하엿으며 열처리 조건의 변화에 따른 크리이프 특성 변화를 고찰하였다.

• Journal of the Korean Home Economics Association
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• v.39 no.11
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• pp.223-232
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• 2001

This study, by explaining the relationship between mechanical properties and clothing formability, aims to propose functional data for tailoring performance of fabrics of good tailorability. The KES-FB system was used to measure factors of mechanical properties and also the technique of stepwise-block-regression method was applied to investigate relationship between functional properties and mechanical properties of men's shirks. As results of vasual inspection of men's shirts, it showed that good fabrics had higher value in the LT, bending properties, shear properties and RC than poor fabrics in Total Appearance Value(TAV). And finally, A formula was obtained for calculating the VIA of men's shirts from functional properties which were calculated from the mechanical properties.

• Structural Engineering and Mechanics
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• v.57 no.2
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• pp.265-280
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• 2016

A NLFE model was proposed to investigate the mechanical behavior of short columns, cast using plain or fibrous lightweight aggregate concrete (LWAC), and subjected to elevated temperatures of up to $700^{\circ}C$. The model was validated, before its predictions were extended to study the effect of other variables, not studied experimentally. The three-dimensional NLFE model was developed using ANSYS software and involved rational simulation of thermal mechanical behavior of plain and fibrous LWAC as well as longitudinal and lateral steel reinforcement. The prediction from the NLFE model of columns' mechanical behavior, as represented by the stress-strain diagram and its characteristics, compared well with the experimental results. The predictions of the proposed models, considering wide range of lateral reinforcement ratios, confirmed the behaviors observed experimentally and stipulated the importance of steel confinement in preserving post-heating mechanical properties of plain and fibrous LWAC columns, being subjected to high temperature.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.10a
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• pp.146-151
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• 2003

Reduced Activation Ferritic/Martenstic (RAFs) are leading candidates for structural materials of D-T fusion reactor. One of The RAFs, JLF-1 (9Cr-2W-V, Ta) has been developed and proved to have good resistance against high-fluency neutrino irradiation and good phase stability. Recently, in order to clarify the strengthening mechanical at high temperature, a new scheme to improve high temperature mechanical properties is desired. Therefore, the creep properties and creep life prediction by Larson-Miller Parameter method for JLF-1 to be used for fusion reactor materials or other high temperature components were presented at the elevated temperatures of $500^{\circ}C$, $550^{\circ}C$, $600^{\circ}C$, $650^{\circ}C$ and $704^{\circ}C$. It was confirmed experimentally and quantitatively that a creep life predictive e벼ation at such various high temperatures was well derived by LMP.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.6
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• pp.1465-1473
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• 1994

This paper describes the development strategy and contents of a fatigue life evaluation system, FLEVA. The system is composed of 4 parts; material properties, load histories, cycle counting and life prediction. The cycle counting is based on the rain-flow counting method and peak counting method, and the life prediction is performed based on the linear damage rule. Material properties(static, fatigue) are also provided as a database obtained by a computer aided test system. Case study is performed to verify the developed program.

• Journal of the Korean Institute of Gas
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• v.25 no.2
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• pp.64-71
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• 2021

Research on polymer matrix composites with excellent molding processability and mechanical properties in the automotive field including hydrogen fuel cell electric vehicles is expanding to Computer-Aided Engineering (CAE) to support the design of materials with specific mechanical properties. CAE automation requires the prediction of the mechanical properties and behavior of materials. Unlike single materials, the mechanical properties prediction of polymer matrix composites is difficult to explain with formulas because the mechanical behavior is complicated to be explained only by the relationship between the matrix and the filler. In this study, the stress-strain curve according to the composition of polymer matrix composites, which was difficult to predict due to its sensitivity to large plastic deformation and composition, was predicted based on machine learning of the test data. The developed model finds a complex correlation between matrix and filler types and compositions, and predicts the total stress-strain curve meaningfully even in the absence of learned test data. It is expected that the material design AI system can be completed in the future based on the developed model that predicts the mechanical properties of polymer matrix composites even for the combination and composition that have not been learned.

• Journal of the Semiconductor & Display Technology
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• v.11 no.1
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• pp.49-54
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• 2012

There have been plenty of difficulties because properties of Alq3 are unable to acquire in a process of manufacture of OLED. In this paper it will predict a viscosity of Alq3 through DSMC technique and suggest the way regarding a study to estimate properties of material through the computer simulation. There could generate errors of a simulation process in a vacuum deposition process since the properties of material that is used in a high-degree vacuum environment are not secured. Therefore, we would like to propose the new methods that can not only predict properties of a molecular unit but also raise an accuracy of simulation process by forecasting properties of Alq3.

• Transactions of Materials Processing
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• v.32 no.6
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• pp.367-375
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• 2023

Composite materials, known for their excellent mechanical properties and lightweight characteristics, are applied in various engineering fields. Recently, efforts have been made to develop an automotive battery protection panel using a plain-woven composite composed of glass fiber and polypropylene to reduce the weight of automobiles. However, excessive warpage occurs during the GF/PP compression molding process, which makes car assembly challenging. This study aims to develop a model that predicts the warpage during the compression molding process. Obtaining out-of-plane properties such as elastic or shear modulus, essential for predicting warpages, is tricky. Existing mechanical methods also have limitations in calculating these properties for woven composite materials. To address this issue, finite element analysis is conducted using representative volume elements (RVE) for woven composite materials. A warpage prediction model is developed based on the estimated physical properties of GF/PP composite materials obtained through representative volume elements. This model is expected to be used for reducing warpages in the compression molding process.