• Physical Therapy Korea
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• v.19 no.3
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• pp.20-30
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• 2012

The purpose of this study was to determine the effect of ankle joint mobilization with movement (MWM) on the range of motion (ROM) in the ankle, on the muscle strength of lower extremities, and on spatiotemporal gait parameters in chronic hemiplegic patients. Fifteen subjects with chronic stroke were divided into two groups: an experimental group (8 subjects) and a control group (7 subjects). Both groups attended two or three sessions of physical therapy each week. The experimental group also attended additional MWM training sessions three times a week for five weeks. For both groups, the ROM of the ankle, the muscle strength of the lower extremities, and the spatiotemporal gait parameters in paretic limbs were evaluated before and after the training period. The results showed that the experimental group experienced more significant increases than did the control group in terms of passive (6.10%) and active (21.96%) ROM of the ankle, gait velocity (12.96%), and peak torque, of the knee flexor (81.39%), the knee extensor (24.88%), and the ankle plantar flexor (41.75%)(p<.05). These results suggest that MWM training in patients with chronic stroke may be beneficial in increasing ROM in the ankle, muscle strength in the lower extremities, and gait speed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2234-2241
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• 2015

This study has been undertaken to develop a relationship between the shear strength coefficients and the cone index. The theoretic mathematical equations for the relationship were rigorously investigated, and then a Artificial Neural Network(ANN) analysis was adapted to enhance the reliability of the investigation. The theoretical investigation involved various assumptions resulting in the significant error involvement of geotechnical behaviors of ground. Therefore, a model using the ANN has been learned to enhance the prediction of the cone index form the shear strength parameters. Site investigation reports from various construction fields were used for ANN model learning. The results of the study show that the model predicts the cone index from the shear strength parameters of soils very well. The further study that is undertaking has a potential promise of the generalized prediction technique for the cone index from the soil parameters.

• Journal of the Korean GEO-environmental Society
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• v.14 no.5
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• pp.49-56
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• 2013

The necessity of exploration in deep sea increases to develop the natural resources. The deep marine sediments, which were recovered from the hydrate occurrence regions during the Ulleung Basin Gas Hydrate Expedition 2 (UBGH2), East Sea, Korea in 2010, are explored to obtain the geotechnical characteristics and strength parameters. The index properties of the specimens including the atterberg limits, specific surface, and particle size distribution are measured and compared with the previous studies. X-ray diffraction, scanning electron microscope, and X-ray energy dispersive spectroscopy are conducted to analyze the clay mineralogy, chemical composition, and microstructure of the sediments. Strength parameters and shear wave velocities are measured with the axial strain by using an instrumented triaxial device. The strength parameters estimated by empirical equations are compared with the experimental results.

• Structural Engineering and Mechanics
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• v.38 no.1
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• pp.1-25
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• 2011

A new nonlinear model was developed to evaluate the shear resistance of steel fiber-reinforced concrete beams (SFRCB) using linear genetic programming (LGP). The proposed model relates the shear strength to the geometrical and mechanical properties of SFRCB. The best model was selected after developing and controlling several models with different combinations of the influencing parameters. The models were developed using a comprehensive database containing 213 test results of SFRC beams without stirrups obtained through an extensive literature review. The database includes experimental results for normal and high-strength concrete beams. To verify the applicability of the proposed model, it was employed to estimate the shear strength of a part of test results that were not included in the modeling process. The external validation of the model was further verified using several statistical criteria recommended by researchers. The contributions of the parameters affecting the shear strength were evaluated through a sensitivity analysis. The results indicate that the LGP model gives precise estimates of the shear strength of SFRCB. The prediction performance of the model is significantly better than several solutions found in the literature. The LGP-based design equation is remarkably straightforward and useful for pre-design applications.

• Computers and Concrete
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• v.19 no.5
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• pp.467-475
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• 2017

The purpose of this study is to establish a prediction model for the electrical resistivity ($E_r$) of self-consolidating concrete by using waste LCD (liquid crystal display) glass as part of the fine aggregate and then, to analyze the results obtained from a series of laboratory tests. A hyperbolic function is used to perform nonlinear multivariate regression analysis of the electrical resistivity prediction model, with parameters such as water-binder ratio (w/b), curing age (t) and waste glass content (G). Furthermore, the relationship of compressive strength and electrical resistivity of waste LCD glass concrete is also found by a logarithm function, while compressive strength is evaluated by the electrical resistivity of non-destructive testing (NDT). According to relative regression analysis, the electrical resistivity and compressive strength prediction models are developed, and the results show that a good agreement is obtained using the proposed prediction models. From the comparison between the predicted analysis values and test results, the MAPE value of electrical resistivity is 17.0-18.2% and less than 20%, the MAPE value of compressive strength evaluated by $E_r$ is 5.9-10.6% and nearly less than 10%. Therefore, the prediction models established in this study have good predictive ability for electrical resistivity and compressive strength of waste LCD glass concrete. However, further study is needed in regard to applying the proposed prediction models to other ranges of mixture parameters.

• Computers and Concrete
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• v.20 no.1
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• pp.31-38
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• 2017

In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN's are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.

• Geomechanics and Engineering
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• v.1 no.1
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• pp.35-52
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• 2009

A significantly thick zone of steep slopes is commonly encountered above groundwater table and the soils within this zone are unsaturated with negative pore-water pressures (i.e., matric suction). Matric suction contributes significantly to the shear strength of soil and to the factor of safety of unsaturated slopes. However, infiltration during rainfall increases the pore-water pressure in soil resulting in a decrease in the matric suction and the shear strength of the soil. As a result, rainfall infiltration may eventually trigger a slope failure. Therefore, understanding of shear strength characteristics of saturated and unsaturated soils under shearing-infiltration (SI) conditions have direct implications in assessment of slope stability under rainfall conditions. This paper presents results from a series of consolidated drained (CD) and shearing-infiltration (SI) tests. Results show that the failure envelope obtained from the shearing-infiltration tests is independent of the infiltration rate. Failure envelopes obtained from CD and SI tests appear to be similar. For practical purposes the shear strength parameters from the CD tests can be used in stability analyses of slopes under rainfall conditions. The SI tests might be performed to obtain more conservative shear strength parameters and to study the pore-water pressure changes during infiltration.

• Advances in concrete construction
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• v.5 no.4
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• pp.313-330
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• 2017

Geopolymer is a sustainable concrete, replaces traditional cement concrete using alternative sustainable construction materials as binders and alkaline solution as alkaline activator. This paper presents the strength characteristics of geopolymer concrete (GPC) developed with fly ash and GGBS as binders, combined Sodium silicate ($Na_2SiO_3$) and Sodium Hydroxide (NaOH) solution as alkaline activators. The parameters considered in this research work are proportions of fly ash and GGBS (70-30 and 50-50), curing conditions (Outdoor curing and oven curing at $600^{\circ}C$ for 24 hours), two grades of concrete (GPC20 and GPC50). The mechanical properties such as compressive strength, split tensile strength and flexural strength along with durability characteristics were determined. For studying the durability characteristics of geopolymer concrete 5% $H_2SO_4$ solutions was used and the specimens were immersed up to an exposure period of 56 days. The main parameters considered in this study were Acid Mass Loss Factor (AMLF), Acid Strength Loss Factor (ASLF) and products of degradation. The results conclude that GPC with sufficient strength can be developed even under Outdoor curing using fly ash and GGBS combination i.e., without the need for any heat curing.

• Steel and Composite Structures
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• v.11 no.4
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• pp.273-290
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• 2011

In current ductility-based earthquake-resistant design, the estimation of design forces continues to be carried out with the application of response modification factors on elastic design spectra. It is well-known that the response modification factor (R) takes into account the force reduction, strength, redundancy, and damping of structural systems. The key components of the response modification factor (R) are force reduction ($R_{\mu}$) and strength ($R_S$) factors. However, the response modification and strength factors for structural systems presented in design codes were based on professional judgment and experiences. A numerical study has been accomplished to evaluate force reduction, strength, and response modification factors for special steel moment resisting frames. A total of 72 prototype steel frames were designed based on the recommendations given in the AISC Seismic Provisions and UBC Codes. Number of stories, soil profiles, seismic zone factors, framing systems, and failure mechanisms were considered as the design parameters that influence the response. The effects of the design parameters on force reduction ($R_{\mu}$), strength ($R_S$), and response modification (R) factors were studied. Based on the analysis results, these factors for special steel moment resisting frames are evaluated.

• Computers and Concrete
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• v.27 no.3
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• pp.225-239
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• 2021

Carbon fiber reinforced polymer (CFRP) has extensive use in strengthening reinforced concrete structures due to its high strength and elastic modulus, low weight, fast and easy application, and excellent durability performance. Many studies have been carried out to determine the performance of the CFRP confined concrete cylinder. Although studies about the prediction of confined compressive strength using ANN are in the literature, the insufficiency of the studies to predict the strain of confined concrete cylinder using ANN, which is the most appropriate analysis method for nonlinear and complex problems, draws attention. Therefore, to predict both strengths and also strain values, two different ANNs were created using an extensive experimental database. The strength and strain networks were evaluated with the statistical parameters of correlation coefficients (R2), root mean square error (RMSE), and mean absolute error (MAE). The estimated values were found to be close to the experimental results. Mathematical equations to predict the strength and strain values were derived using networks prepared for convenience in engineering applications. The sensitivity analysis of mathematical models was performed by considering the inputs with the highest importance factors. Considering the limit values obtained from the sensitivity analysis of the parameters, the performances of the proposed models were evaluated by using the test data determined from the experimental database. Model performances were evaluated comparatively with other analytical models most commonly used in the literature, and it was found that the closest results to experimental data were obtained from the proposed strength and strain models.