• Composites Research
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• v.35 no.1
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• pp.38-41
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• 2022

In this study, the strain rate dependent tensile and compressive properties of PP-LGF and TPO was investigated under the high strain rate by using the Split Hopkinson Pressure Bar (SHPB). The SHPB is the most widely used apparatus to characterize dynamic mechanical behavior of materials at high strain rates between 100 s^-1 and 10,000 s^-1. The SHPB test is based on the wave propagation theory which was developed to give the stress, strain and strain rate in the specimen using the strains measured in the incident and transmission bars. In addition, to verify the strain data obtained from SHPB, the specimen was photographed with a high-speed camera and compared with the strain data obtained through the Digital Image Correlation (DIC).

• Steel and Composite Structures
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• v.43 no.4
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• pp.447-456
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• 2022

Recent experimental studies showed that deep steel I-shaped profiles classified as high ductility class sections in seismic design international codes exhibit low deformation capacity when subjected to cyclic loading. This paper presents an innovative retrofit solution to increase the rotation capacity of beams using bonded carbon fiber reinforced polymers (CFRP) patches validated with advanced finite element analysis. This investigation focuses on the flexural cyclic behaviour of I-shaped hot rolled steel deep section used as beams in moment-resisting frames (MRF) retrofitted with CFRP patches on the web. The main goal of this CFRP reinforcement is to increase the rotation capacity of the member without increasing the overstrength in order to avoid compromising the strong column-weak beam condition in MRF. A finite element model that simulates the cyclic plasticity behavior of the steel and the damage in the adhesive layer is developed. The damage is modelled using the cohesive zone modelling (CZM) technique that is able to capture the crack initiation and propagation. Details on the modelling techniques including the mesh sensitivity near the fracture zone are presented. The effectiveness of the retrofit solution depends strongly on the selection of the appropriate adhesive. Different adhesive types are investigated where the CZM parameters are calibrated from high fidelity fracture mechanics tests that are thoroughly validated in the literature. This includes a rigid adhesive commonly found in the construction industry and two tough adhesives used in the automotive industry. The results revealed that the CFRP patch can increase the rotation capacity of a steel member considerably when using tough adhesives.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.1
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• pp.66-73
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• 2022

The purpose of this study was to develop self-heating concrete by utilizing the conduction resistance of concrete in order to reduce the risk of occurrence of black ice in the concrete pavement in winter and to prevent damage caused by freez-thawing effect. For this purpose, it was attempted to evaluate the strength and temperature exothermic characteristics using powder and liquid waste CNTs and a waste cathode agent as a conduction promotion. It was analyzed that liquid waste CNT had an effective dispersion degree in the mortar and a small decrease in strength occurred. In addition, DC 24 V was supplied by applying steel mesh, copper foil and copper wire to the mortar as electrodes, and the temperature change characteristics according to the mixing ratio of spent CNTs, anodes and carbon fibers were evaluated. In addition, by evaluating the temperature characteristics according to the electrode spacing from the selected optimal mixture, it was confirmed that it had sufficient heating characteristics up to an electrode spacing of 100 mm up to AC 50 V.

• Composites Research
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• v.35 no.1
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• pp.23-30
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• 2022

Polycarbosilane(PCS) is an important precursor for melt-spinning the silicon carbide(SiC) fibers and manufacturing ceramics. The PCS is a metal-organic polymer precursor capable of producing continuous SiC fibers having excellent performance such as high-temperature resistance and oxidation resistance. The SiC fibers are manufactured through melt-spinning, stabilization, and heat treatment processes using the PCS manufactured by synthesis, purification, and control of the molecular structure. In this paper, we analyzed the effect of purification of unreacted substances and low molecular weight through solvent treatment of PCS and the effect of heat treatment at various temperatures change the polymerization and network rearrangement of PCS. Especially, we investigated the complex viscosity and structural arrangement of PCS precursors according to solvent treatment and heat treatment through the rheological properties.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• Textile Coloration and Finishing
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• v.34 no.4
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• pp.224-233
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• 2022

Recently, consumer tastes of various classes at home and abroad prefer comfortable, unadorned, and simple clothing, and the athleisure trend, which can be used freely in daily life as well as exercise, has expanded to overall clothing products. Existing materials used for athleisure are composite knitted fabrics using polyester yarn and PU yarn, which has problems due to a chronic lack of color fastness and contamination by dyes even when PU laminating is applied, making it difficult to apply various colors. There is a quality problem in which deformation of the product occurs due to lack of durability. In this study, CDP yarn(75de/72f) and PU yarn(40de) were selected to commercialize the circular knitting for athleisure using CDP yarn in order to solve the problems that occur in the dyeing and laminating process when using polyester materials. CDP yarns were used to knit into single(CP75-S) and double(CP75-D) knit and single knit were found to be suitable as athleisure fabrics. After pretreatment and treatment under various conditions, the stainability of CDP circular knitting was examined. After pretreatment and dyeing process under various conditions, the property of scouring and dyeability of CP75-S were evaluated.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.148-154
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• 2022

As the 4th industrial age approaches, the demand for semiconductors is increasing enough to be used in all electronic devices. At the same time, semiconductor technology is also developing day by day, leading to ultraprecision and low power consumption. Semiconductors that keep getting smaller generate heat because the energy density increases, and the generated heat changes the shape of the semiconductor package, so it is important to manage. The temperature change is not only self-heating of the semiconductor package, but also heat generated by external damage. If the package is deformed, it is necessary to manage it because functional problems and performance degradation such as damage occur. The package burn in test in the post-process of semiconductor production is a process that tests the durability and function of the package in a high-temperature environment, and heat dissipation performance can be evaluated. In this paper, we intend to review a new material formulation that can improve the performance of the adapter, which is one of the parts of the test socket used in the burn-in test. It was confirmed what characteristics the basic base showed when polyamide, a high-molecular material, and alumina, which had high thermal conductivity, were mixed for each magnification. In this study, functional evaluation was also carried out by injecting an adapter, a part of the test socket, at the same time as the specimen was manufactured. Verification of stiffness such as tensile strength and flexural strength by mixing ratio, performance evaluation such as thermal conductivity, and manufacturing of a dummy device also confirmed warpage. As a result, it was confirmed that the thermal stability was excellent. Through this study, it is thought that it can be used as basic data for the development of materials for burn-in sockets in the future.

• Steel and Composite Structures
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• v.46 no.5
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• pp.689-707
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• 2023

Geopolymer concrete production is interesting as it is an alternative to portland cement concrete. However, workability, setting time and strength expectations limit the sustainable application of geopolymer concrete in practice. This study aims to improve the production of geopolymer concrete to mitigate these drawbacks. The improvement in the workability and setting time were achieved with the additional use of NaOH solution whereas an increase in the strength was gained with the addition of recycled steel fibers from waste tires. In addition, the use of 25% basalt powder instead of fly ash and the addition of recycled steel fibers from waste tires improved its environmental feature. The samples with steel fiber ratios ranging between 0.5% and 5% and basalt powder of 25%, 50% and 75% were tested under both compressive and flexure forces. The compressive and flexural capacities were significantly enhanced by utilizing recycled steel fibers from waste tires. However, decreases in these capacities were detected as the basalt powder ratio increased. In general, as the waste wire ratio increased, the compressive strength gradually increased. While the compressive strength of the reference sample was 26 MPa, when the wire ratio was 5%, the compressive strength increased up to 53 MPa. With the addition of 75% basalt powder, the compressive strength decreases by 60%, but when the 3% wire ratio is reached, the compressive strength is obtained as in the reference sample. In the sample group to which 25% basalt powder was added, the flexural strength increased by 97% when the waste wire addition rate was 5%. In addition, while the energy absorption capacity was 0.66 kN in the reference sample, it increased to 12.33 kN with the addition of 5% wire. The production phase revealed that basalt powder and waste steel wire had a significant impact on the workability and setting time. Furthermore, SEM analyses were performed.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.57-65
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• 2023

A vibration response-based detection system was used to investigate the adhesive areas of single-lap joints using a nonlinear transformation approach for deep learning. In industry or engineering fields, it is difficult to know the condition of an invisible part within a structure that cannot easily be disassembled and the conditions of adhesive areas of adhesively bonded structures. To address these issues, a detection method was devised that uses nonlinear transformation to determine the adhesive areas of various single-lap-jointed specimens from the vibration response of the reference specimen. In this study, a frequency response function with nonlinear transformation was employed to identify the vibration characteristics, and a virtual spectrogram was used for classification in convolutional neural network based deep learning. Moreover, a vibration experiment, an analytical solution, and a finite-element analysis were performed to verify the developed method with aluminum, carbon fiber composite, and ultra-high-molecular-weight polyethylene specimens.