• Geomechanics and Engineering
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• v.31 no.3
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• pp.291-304
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• 2022

Maximum shear modulus (G_max or G₀) is an important soil property useful for many engineering applications, such as the analysis of soil-structure interactions, soil stability, liquefaction evaluation, ground deformation and performance of seismic design. In the current study, bender element (BE) tests are used to evaluate the effect of the void ratio, effective confining pressure, grading characteristics (D₅₀, C_u and C_c), anisotropic consolidation and initial fabric anisotropy produced during specimen preparation on the G_max of sand-gravel mixtures. Based on the tests results, an empirical equation is proposed to predict G_max in granular soils, evaluated by the experimental data. The artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were also applied. Coefficient of determination (R²) and Root Mean Square Error (RMSE) between predicted and measured values of G_max were calculated for the empirical equation, ANN and ANFIS. The results indicate that all methods accuracy is high; however, ANFIS achieves the highest accuracy amongst the presented methods.

• Composites Research
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• v.13 no.1
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• pp.1-10
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• 2000

During the compression molding process of the continuous fiber-reinforced polymeric composites, two main problems such as fiber-matrix separation and fiber orientation are produced by the difference of flow velocity. Molded parts are lead to be nonhomogeneous and anisotropic. As the mechanical property of the products are dependent on the separation and orientation, it is important to research the fiber mat structure and molding condition. If the fiber mat structure is changed by the increment of needling, the separation decreases and after compression molding the orientation is easily aligned. As it were, the compression moldability is good. But the defects as tears, thin thickness are produced in the products. Therefore, it is important to clarify the moldability in relation to the usage of products and the expenses of produce on the actual process. Therefore we must make the measurement methods that can define the moldability of products. In this research, the effects of the fiber mat structure(NP = 0, 5, 10, 25, 50 punches/$cm^2$) and the mold geometry($r_p$ = 1, 25, 50 mm) on the moldability of products were discussed. We investigated the case of one-dimensional flow in order to obtain the degree of nonhomogeneity and the fiber orientation function. In result, we could gain the correlation coefficient of the continuous fiber-reinforced polymeric composites. Also we experimented on the cup-type compression molding which was appeared the wrinkle on the flange part by the complex stress condition in order to gain the degree of nonhomogeneity and area ratio. In result, the moldability of products was expressed as the correlation coefficient and area ratio.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1207-1217
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• 2022

Vegetation indices using the reflectance of selected wavelength, associating with the monitoring purpose such as identifying the progress of crop growth, on the vegetation canopy surface is widely used in the digital agriculture technology. However, the surface reflectance anisotropy can distort the true value of vegetation index related to the condition of surface, even though the surface property be unchanged. That causes difficulty to observe accurately crop growth on the monitoring system. In this study, a simple type goniometer was designed to measure the reflectance from the anisotropic surface according to various zeniths and azimuths of sun and viewing sensor in the field. On the tarp like as Lambertian surface, the reflectance of Blue, Green, Red, Near-Infrared band was similar to the tarps' reflectance properties. However, the reflectance was slightly overestimated in the cloudy day. The relative difference values of vegetation indices on grass were overestimated for the forward viewing and underestimated for the backward viewing. In addition, enhanced vegetation index (EVI) showed less sensitive according to the positions of sun and sensor viewing. Field observation with a goniometer will be helpful to understand the anisotropy characteristics on the vegetation surface.