• Advances in concrete construction
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• v.14 no.3
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• pp.185-194
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• 2022

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R² metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.

• 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.

• Fibers and Polymers
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• v.2 no.4
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• pp.196-202
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• 2001

In order to investigate the relationship between subjective sensation for fabric sound and touch and the objective measurements, eight different apparel fabrics were selected as specimens. Sound parameters of fabrics including level pressure of total sound (LPT), level range (ΔL), and frequency differences (Δf) and mechanical properties by Kawabata Evaluation System (KES) were obtained. For subjective evaluation, seven aspects of the sound (softness, loudness, pleasantness, sharpness, clearness, roughness, and highness) and eight of the tough (hardness, smoothness, fineness, coolness, pliability, crispness, heaviness, and thickness) were rated using semantic differential scale. Polyester ultrasuede was evaluated to sound softer and more pleasant while polyester taffeta to sound louder and rougher than any other fabrics. Wool fabric such as worsted and woolen showed similar sensation for sound but differed in some touch sensation in that woolen was coarseast, heaviest, and thickest in touch. In the prediction model for sound sensation, LPT affected positively subjective roughness and highness as well as loudness, while ΔL was found as a parameter related positively with softness and pleasantness. Touch sensation was explained by some of mechanical properties such as surface, compressional, shear, and bending properties implying that a touch sensation could be expressed by a variety of properties.

• Journal of Ocean Engineering and Technology
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• v.18 no.2
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• pp.58-63
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• 2004

Reduced Activation Ferritic/Martensitic Steels (RAFs) are leading candidntes for structural materials of a D-T fusion reactor. One of the RAFs, JLF-l (9Cr-2W-V, Ta) has been developed and has shown to have good resistance against high-fluency neutrino irradiation and good phase stability. Recently, in order to clarify the strengthening mechanisms at high temperatures, a new scheme to improve high temperature mechanical properties is desired. Therefore, the test technique development of high temperature creep behaviors for this material is very important. In this paper, the creep properties and creep life prediction, using the Larson-Miler parameter method for JLF-l to be used for fusion reactor materials or other high temperature components, are presented at the elevated temperatures of 50$0^{\circ}C$, 55$0^{\circ}C$, $600^{\circ}C$, $650^{\circ}C$ and 704$^{\circ}C$. It was confirmed, experimentally and quantitatively, that a creep life predictive equation, at such various high temperatures, is well derived mr the LMP method.

• Advances in concrete construction
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• v.6 no.5
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• pp.465-483
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• 2018

The most crucial factor determining the pumping performance of concrete is the characteristics of the lubricating layer formed between the pipe wall and the inner concrete. Thus, it is important to accurately identify the rheological properties of the lubricating layer to predict the pumping of concrete. In this study, a new method is proposed for measuring the rheological properties of the lubricating layer with improved convenience. To verify the new method, a pumping test was conducted with 337 m-long horizontal piping. The rheological properties of the lubricating layer were assessed by a previously verified method and the new method proposed in this study for a total of four concrete mixtures with design strength ranging from 27 MPa to 60 MPa. The correlation between the existing method and the new method in relation to the viscosity of the lubricating layer was determined, and it was possible to predict the pumping performance with an accuracy of about 88.5% using the viscosity of the lubricating layer obtained from this correlation.

• Transactions of Materials Processing
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• v.32 no.1
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• pp.28-34
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• 2023

In this research, a representative volume element (RVE)-based FE Model is presented to estimate the mechanical properties of additively manufactured continuous fiber-reinforced composites with different fiber orientations. To construct the model, an ABAQUS Python script has been implemented to produce matrix and fiber in the desired orientations at the RVE. A script has also been developed to apply the periodic boundary conditions to the RVE. Experimental tests were conducted to validate the numerical models. Tensile specimens with the fiber directions aligned in the 0, 45, and 90 degrees to the loading direction were manufactured using a continuous fiber 3D printer and tensile tests were performed in the three directions. Tensile tests were also simulated using the RVE models. The predicted Young's moduli compared well with the measurements: the Young's modulus prediction accuracy values were 83.73, 97.70, and 92.92 percent for the specimens in the 0, 45, and 90 degrees, respectively. The proposed method with periodic boundary conditions precisely evaluated the elastic properties of additively manufactured continuous fiber-reinforced composites with complex microstructures.

• 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.

• Smart Structures and Systems
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• v.34 no.1
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• pp.17-23
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• 2024

Non-destructive methods are extensively utilized for assessing the integrity of rock bolts, with longitudinal wave speed being a crucial property for evaluating rock bolt quality. This research aims to propose a method for predicting reliable longitudinal wave velocities by leveraging various properties of the rock surrounding the rock bolt. The prediction algorithm employed is the Multilayer Neural Network (MNN), and the input properties includes elastic modulus, shear wave speed, compressive strength, compressional wave speed, mass density, porosity, and Poisson's ratio, totaling seven. The implementation of the MNN demonstrates high reliability, achieving a coefficient of determination of 0.996. To assess the impact of each input property on longitudinal wave speed, an importance score is derived using the random forest algorithm, with the elastic modulus identified as having the most significant influence. When the elastic modulus is the sole input parameter, the coefficient of determination for predicting the longitudinal wave speed is observed to be 0.967. The findings of this study underscore the reliability of selecting specific properties for predicting longitudinal wave speed and suggest that these insights can assist in identifying relevant input properties for rock bolt integrity assessments in future construction site experiments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.599-605
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• 2012

It is important to decide traffic opening time for construction plan of epoxy asphalt pavement. For this purpose, strength prediction model of epoxy asphalt concrete is required. In this study, Marshall stability was measured according to temperature and time for making strength properties equation. Strength prediction model was developed using chemical kinetics considering temperature variation. The traffic opening time of epoxy asphalt pavement on bridge deck has been predicted using the developed model. The prediction and actual traffic opening times were different by 17-days, because weathers of year 2009-2011 used in prediction model were different from weather of year 2012. When the prediction model used the actually measured temperatures of pavement, the difference between real opening time and prediction opening time was two days. The correlation analysis result between measured strength and prediction strength revealed that the $R^2$ using accurate temperature of pavement was 0.95. An improved precise prediction result is to be obtained if the prediction model uses accurate temperature data of pavement.

• Steel and Composite Structures
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• v.17 no.3
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• pp.215-236
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• 2014

The flexural behaviour of steel beams significantly affects the structural performance of the steel frame structures. In particular, the flexural overstrength (namely the ratio between the maximum bending moment and the plastic bending strength) that steel beams may experience is the key parameter affecting the seismic design of non-dissipative members in moment resisting frames. The aim of this study is to present a new formulation of flexural overstrength factor for steel beams by means of artificial neural network (NN). To achieve this purpose, a total of 141 experimental data samples from available literature have been collected in order to cover different cross-sectional typologies, namely I-H sections, rectangular and square hollow sections (RHS-SHS). Thus, two different data sets for I-H and RHS-SHS steel beams were formed. Nine critical prediction parameters were selected for the former while eight parameters were considered for the latter. These input variables used for the development of the prediction models are representative of the geometric properties of the sections, the mechanical properties of the material and the shear length of the steel beams. The prediction performance of the proposed NN model was also compared with the results obtained using an existing formulation derived from the gene expression modeling. The analysis of the results indicated that the proposed formulation provided a more reliable and accurate prediction capability of beam overstrength.