• Computers and Concrete
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• v.27 no.3
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• pp.185-197
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• 2021

Throughout the previous years, many efforts focused on incorporating non-biodegradable wastes as a partial replacement and sustainable alternative for natural aggregates in cement-based materials. Currently, rubberized concrete is considered one of the most important green concrete materials produced by replacing natural aggregates with rubber particles from old tires in a concrete mixture. The main benefits of this material, in addition to its importance in sustainability and waste management, comes from the ability of rubber to considerably damp vibrations, which, when used in reinforced concrete structures, can significantly enhance its energy dissipation and vibration behavior. Nowadays, the literature has many experimental findings that provide an interesting view of rubberized concrete's dynamic behavior. On the other hand, it still lacks research that collects, interprets, and numerically investigates these findings to provide some correlations and construct reliable prediction models for rubberized concrete's dynamic properties. Therefore, this study is intended to propose prediction approaches for the dynamic properties of rubberized concrete. As a part of the study, multiple linear regression and artificial neural networks will be used to create prediction models for dynamic modulus of elasticity, damping ratio, and natural frequency.

• Journal of Environmental Impact Assessment
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• v.10 no.4
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• pp.297-304
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• 2001

Recently the road traffic noise has appeared as a significant environmental issue because of dramatic increase of vehicles and expansion of newly constructed road. Therefore, this study proposes the method that improves prediction factors and models through analysis of the existing road traffic noise prediction model. Prediction factors can be improved by establishing guideline for diffraction attenuation and applying daily traffic discharge, peak traffic discharge, and average traveling speed through an analysis of level service. Prediction must be made by periods of one or five years during 20 years. Prediction models also can be improved to include better prediction model through setting the database, establishing functional relation between physical properties and noise levels by acoustic analysis, and developing models for road traffic noise prediction in residential areas.

• Korean Journal of Food Science and Technology
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• v.20 no.6
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• pp.827-833
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• 1988

The objectives of this investigation were to develop an improved analytical method and to review with respect to experimental parameters and thermo-physical properties influencing the freezing time prediction. The results indicate that the relationship between freezing time and product size is dependent on the surface heat transfer coefficient. As the magnitude of surface heat transfer coefficient decreases, the influence of product size on freezing time becomes more profound. But the freezing time does decrease slightly as the coefficients are increased to values greater than 150 $w/m^2^{\circ}C$. In addition, influence of thermo-physical properties on the freezing time prediction shown generally density, water content, specific heat and thermal conductivity, in order of % difference. Multiple linear regression equation for freezing time prediction were obtained with respect to 4 different food materials with varying thickness.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.253-262
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• 2024

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.

• Science of Emotion and Sensibility
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• v.14 no.1
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• pp.127-136
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• 2011

The objectives of this study were to investigate the effects of color characteristics on tactile sensation / sensibility image of silk fabrics and to provide prediction models for their tactile sensation/sensibility image by both mechanical properties and color characteristics. As results, some of tactile sensation/sensibility terms including 'smooth', 'buoyant', 'thick', 'stiff', 'unique', 'casual', 'rural', and 'modern' seemed to be influenced by color characteristics such as achromatic/chromatic and hue / tone as well as by mechanical properties of silk. Moreover, red or green silk was more strongly felt than gray ones for 'thick' and 'stiff' as well as pale or vivid was. On the other hands, 'Rural' and 'casual' were respectively evaluated more highly for green, pale, or vivid silk. These results imply that color could give an effect on subjective tactile sensation / sensibility. Finally, prediction models for some of tactile sensation / sensibility of silk fabrics by both mechanical properties and color characteristics were established.

• 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.

• Computers and Concrete
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• v.24 no.5
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• pp.399-412
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• 2019

In this investigation, Elman neural networks were utilized for predicting the mechanical properties of Self-Compacting Concretes (SCCs). Elman models were designed by using experimental data of many different concrete mixdesigns of various types of SCC that were collected from the literature. In order to investigate the effectiveness of the selected input variables on the network performance in predicting intended properties, utilized data in artificial neural networks were considered in two sets of 8 and 140 input variables. The obtained outcomes showed that not only can the developed Elman ANNs predict the mechanical properties of SCCs with high accuracy, but also for all of the desired outputs, networks with 140 inputs, compared to ones with 8, have a remarkable percent improvement in the obtained prediction results. The prediction accuracy can significantly be improved by using a more complete and accurate set of key factors affecting the desired outputs, as input variables, in the networks, which is leading to more similarity of the predicted results gained from networks to experimental results.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10a
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• pp.173-178
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• 2000

New prediction model is investigated estimating splitting tensile strength and modulus of elasticity with curing temperature and aging. New prediction model is based on the model which was proposed to predict compressive strength, and splitting tensile strength and modulus of elasticity calculated by this model are compared with experimental values. New prediction model well estimated splittinge tensile strength and elastic modulus as well as compressive strength.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.11
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• pp.2399-2408
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• 2002

A prediction procedure has been developed to evaluate the microtructures and material properties of heat affected zone (HAZ) in pressure vessel steel weld, based on temperature analysis, thermodynamics calculation and reaction kinetics model. Temperature distributions in HAE are calculated by finite element method. The microstructures in HAZ are predicted by combining the temperature analysis results with the reaction kinetics model for austenite grain growth and austenite decomposition. Substituting the microstructure prediction results into the previous experimental relations, the mechanical material properties such as hardness, yielding strength and tensile strength are calculated. The prediction procedure is modified and verified by the comparison between the present results and the previous study results for the simulated HAZ in reactor pressure vessel (RPV) circurnferential weld. Finally, the microstructures and mechanical material properties are determined by applying the final procedure to real RPV circumferential weld and the local weak zone in HAZ is evaluated based on the application results.

• Carbon letters
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• v.28
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• pp.9-15
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• 2018

In the aerospace field, Carbon/Cork composites have been used for rocket propulsion systems as a light weight structural component with a high bending stiffness and high thermal insulation properties. For the fabrication of a carbon composite with a heat insulation cork part, the bonding properties between them are very important to determine the service life of the Carbon/Cork composite structure. In this study, the changes in the interfacial adhesion and mechanical properties of Carbon/Cork composites under accelerated aging conditions were investigated. The accelerated aging experiments were performed with different temperatures and humidity conditions. The properties of the aged Carbon/Cork composites were evaluated mainly with the interfacial strength. Finally, the lifetime prediction of the Carbon/Cork composites was performed with the long-term property data under accelerated conditions.