• 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.

• 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.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.

• 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.

• Advances in nano research
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• v.15 no.6
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• pp.495-511
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• 2023

In this research, the impact of micro-silica, nano-silica, and polypropylene fibers on the fracture energy of self-compacting concrete was thoroughly examined. Enhancing the fracture energy is very important to increase the crack propagation resistance. The study focused on evaluating the self-compacting properties of the concrete through various tests, including J-ring, V-funnel, slump flow, and T50 tests. Additionally, the mechanical properties of the concrete, such as compressive and tensile strengths, modulus of elasticity, and fracture parameters were investigated on hardened specimens after 28 days. The results demonstrated that the incorporation of micro-silica and nano-silica not only decreased the rheological aspects of self-compacting concrete but also significantly enhanced its mechanical properties, particularly the compressive strength. On the other hand, the inclusion of polypropylene fibers had a positive impact on fracture parameters, tensile strength, and flexural strength of the specimens. Utilizing the response surface method, the relationship between micro-silica, nano-silica, and fibers was established. The optimal combination for achieving the highest compressive strength was found to be 5% micro-silica, 0.75% nano-silica, and 0.1% fibers. Furthermore, for obtaining the best mixture with superior tensile strength, flexural strength, modulus of elasticity, and fracture energy, the ideal proportion was determined as 5% micro-silica, 0.75% nano-silica, and 0.15% fibers. Compared to the control mixture, the aforementioned parameters showed significant improvements of 26.3%, 30.3%, 34.3%, and 34.3%, respectively. In order to accurately model the tensile cracking of concrete, the authors used softening curves derived from an inverse algorithm proposed by them. This method allowed for a precise and detailed analysis of the concrete under tensile stress. This study explores the effects of micro-silica, nano-silica, and polypropylene fibers on self-compacting concrete and shows their influences on the fracture energy and various mechanical properties of the concrete. The results offer valuable insights for optimizing the concrete mix to achieve desired strength and performance characteristics.

• 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.

• 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.

• Tunnel and Underground Space
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• v.26 no.2
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• pp.100-109
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• 2016

This study suggests three spatial distribution functions of strength parameters, which can be adopted in the derivation of failure conditions for transversely isotropic rocks. All three proposed functions, which are the oblate spheroidal function, the exponential function, and the function based on the directional projection of the strength parameter tensor, consist of two model parameters. With assumption that the cohesion and friction angle can be described by the proposed distribution functions, the transversely isotropic Mohr-Coulomb criterion is formulated and used as a failure condition in the simulation of the conventional triaxial tests. The simulation results confirm that the failure criteria incorporating the proposed distribution functions could reproduce the general trend in the variations of the axial stress at failure and the directions of failure planes with varying inclination of the weankness planes and confining pressure. Among three distribution functions, the function based on the directional projection of the strength parameter tensor yields the highest axial strength, while the axial strength estimated by the oblate spheroidal distribution function is the lowest.

• 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.

• Composite Materials and Engineering
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• v.3 no.3
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• pp.201-220
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• 2021

In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.