• Elastomers and Composites
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• v.44 no.4
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• pp.455-465
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• 2009

In this study, Organo-montmorillonite(MMT) was synthesized by intercalation of various amine(Octylamine, Dodecylamine, Dimethyldodecylamine, Octadecylamine) compounds into layered silicate. Natural Rubber(NR)/MMT nanocomposites were prepared by reinforcement of Organo-MMT. X-ray diffraction(XRD) and Scanning electron microscope(SEM) were employed to characterize the layer distance of Organo-MMT and the morphology of the NR/MMT nanocomposites. The structures of the synthesized Organo-MMTs were analyzed by the measurement of FT-IR. Cure characteristics, surface free energy and mechanical properties such as tensile strength, modulus and hardness of NR/MMT nanocomposites were carefully studied by contact angle meter, ODR, UTM, and hardness tester. FT-IR analysis showed a insertion of the alkyl and amine chains into the interlayers of the MMT. It was shown that the cure time of the organo-MMT was more decreased than that of $Na^+$-MMT. Surface free energy and tensile strength of the NR/DDA-MMT nanocomposite were the highest. NR/ODA-MMT nanocomposite was the highest in hardness.

• Elastomers and Composites
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• v.49 no.1
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• pp.43-52
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• 2014

The effect of nanoclay (Cloisite 20A) on the self-adhesion behavior of uncured brominated-isobutylene-isoprene rubber (BIIR) has been studied. The dispersion state of nanoclay into the rubber matrix was examined by SEM, TEM and XRD analysis. The thermal degradation behavior of the filled and unfilled samples was examined by TGA and improvement in the thermal stability of the nanocomposites occurred based on the weight loss (%) measurements. Also, addition of nanoclay enhanced the cohesive strength of the material by reinforcement action thereby reducing the degree of molecular diffusion across the interface of butyl rubber. However, the average depth of penetration of the inter-diffused chains was still adequate to form entanglement on either side of the interface, and thus offered greater resistance to peeling, resulting in high tack strength measurements. The improvement in tack strength was only achieved at critical nanoclay loading above 8 phr. Contact angle measurement was also made to examine the surface characteristics. There was no significant interfacial property change by employing the nanoclay.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.4
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• pp.375-387
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• 2004

A new finite element model will be presented to analyze the nonlinear behavior of RC beams and slabs strengthened by a patch repair. The numerical approach is based on the p-version degenerate shell element including theory of anisotropic laminated composites, theory of materially and geometrically nonlinear plates. In the nonlinear formulation of this model, the total Lagrangian formulation is adopted with large deflections and moderate rotations being accounted for in the sense of von Karman hypothesis. The material model is based on hardening rule, crushing condition, plate-end debonding strength model and so on. The Gauss-Lobatto numerical quadrature is applied to calculate the stresses at the nodal points instead of Gauss points. The validity of the proposed p-version nonlinear finite element model is demonstrated through the load-deflection curves, the ultimate loads, and the failure modes of RC beams or slabs bonded with steel plates or FRP plates compared with available result of experiment and other numerical methods.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.105-111
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• 2021

Rice lodging is an annual occurrence caused by typhoons accompanied by strong winds and strong rainfall, resulting in damage relating to pre-harvest sprouting during the ripening period. Thus, rapid estimations of the area of lodged rice are necessary to enable timely responses to damage. To this end, we obtained images related to rice lodging using a drone in Gimje, Buan, and Gunsan, which were converted to 128 × 128 pixels images. A convolutional neural network (CNN) model, a deep learning model based on these images, was used to predict rice lodging, which was classified into two types (lodging and non-lodging), and the images were divided in a 8:2 ratio into a training set and a validation set. The CNN model was layered and trained using three optimizers (Adam, Rmsprop, and SGD). The area of rice lodging was evaluated for the three fields using the obtained data, with the exception of the training set and validation set. The images were combined to give composites images of the entire fields using Metashape, and these images were divided into 128 × 128 pixels. Lodging in the divided images was predicted using the trained CNN model, and the extent of lodging was calculated by multiplying the ratio of the total number of field images by the number of lodging images by the area of the entire field. The results for the training and validation sets showed that accuracy increased with a progression in learning and eventually reached a level greater than 0.919. The results obtained for each of the three fields showed high accuracy with respect to all optimizers, among which, Adam showed the highest accuracy (normalized root mean square error: 2.73%). On the basis of the findings of this study, it is anticipated that the area of lodged rice can be rapidly predicted using deep learning.