• Journal of Korean Neurosurgical Society
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• 제43권1호
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• pp.26-30
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• 2008

Objective : The authors developed a stereotactic device for irradiation of small animals with Leksell Gamma Knife Model C. Development and verification procedures were described in this article. Methods : The device was designed to satisfy three requirements. The mechanical accuracy in positioning was to be managed within 0.5 mm. The strength of the device and structure were to be compromised to provide enough strength to hold a small animal during irradiation and to interfere the gamma ray beam as little as possible. The device was to be used in combination with the Leksell G-$frame^{(R)}$ and $KOPF^{(R)}$ rat adaptor. The irradiation point was determined by separate imaging sequences such as plain X-ray images. Results : The absolute dose rate with the device in a Leksell Gamma Knife was 3.7% less than the value calculated from Leksell Gamma $Plan^{(R)}$. The dose distributions measured with $GAFCHROMIC^{(R)}$ MD-55 film corresponded to those of Leksell Gamma $Plan^{(R)}$ within acceptable range. The device was used in a series of rat experiments with a 4 mm helmet of Leksell Gamma Knife. Conclusion : A stereotactic device for irradiation of small animals with Leksell Gamma Knife Model C has been developed so that it fulfilled above requirements. Absorbed dose and dose distribution at the center of a Gamma Knife helmet are in acceptable ranges. The device provides enough accuracy for stereotactic irradiation with acceptable practicality.

• Computers and Concrete
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• 제19권4호
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• pp.419-427
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• 2017

With the rising global environmental awareness on energy saving and carbon reduction, as well as the environmental transition and natural disasters resulted from the greenhouse effect, waste resources should be efficiently used to save environmental space and achieve environmental protection principle of "sustainable development and recycling". This study used recycled cement mortar and adopted the volumetric method for experimental design, which replaced cement (0%, 10%, 20%, 30%) with recycled materials (fly ash, slag, glass powder) to test compressive strength and ultrasonic pulse velocity (UPV). The hyperbolic function for nonlinear multivariate regression analysis was used to build prediction models, in order to study the effect of different recycled material addition levels (the function of $R_m$(F, S, G) was used and be a representative of the content of recycled materials, such as fly ash, slag and glass) on the compressive strength and UPV of cement mortar. The calculated results are in accordance with laboratory-measured data, which are the mortar compressive strength and UPV of various mix proportions. From the comparison between the prediction analysis values and test results, the coefficient of determination $R^2$ and MAPE (mean absolute percentage error) value of compressive strength are 0.970-0.988 and 5.57-8.84%, respectively. Furthermore, the $R^2$ and MAPE values for UPV are 0.960-0.987 and 1.52-1.74%, respectively. All of the $R^2$ and MAPE values are closely to 1.0 and less than 10%, respectively. Thus, the prediction models established in this study have excellent predictive ability of compressive strength and UPV for recycled materials applied in cement mortar.

• Composite Materials and Engineering
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• 제3권3호
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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)%.

• Structural Engineering and Mechanics
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• 제83권4호
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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.

• Computers and Concrete
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• 제32권2호
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• Steel and Composite Structures
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• 제49권3호
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• pp.337-348
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• 2023

Successive wetting and drying cycles of concrete due to weather changes can endanger the safety of engineering structures over time. Considering wetting and drying cycles in concrete tests can lead to a more correct and reliable design of engineering structures. This study aims to provide a model that can be used to estimate the resistance properties of concrete under different wetting and drying cycles. Complex sample preparation methods, the necessity for highly accurate and sensitive instruments, early sample failure, and brittle samples all contribute to the difficulty of measuring the strength of concrete in the laboratory. To address these problems, in this study, the potential ability of six machine learning techniques, including ANN, SVM, RF, KNN, XGBoost, and NB, to predict the concrete's tensile strength was investigated by applying 240 datasets obtained using the Brazilian test (80% for training and 20% for test). In conducting the test, the effect of additives such as glass and polypropylene, as well as the effect of wetting and drying cycles on the tensile strength of concrete, was investigated. Finally, the statistical analysis results revealed that the XGBoost model was the most robust one with R2 = 0.9155, mean absolute error (MAE) = 0.1080 Mpa, and variance accounted for (VAF) = 91.54% to predict the concrete tensile strength. This work's significance is that it allows civil engineers to accurately estimate the tensile strength of different types of concrete. In this way, the high time and cost required for the laboratory tests can be eliminated.

• Computers and Concrete
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• 제33권1호
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• 한국의학물리학회지:의학물리
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• 제28권4호
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• pp.190-196
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• 2017

For the $ViewRay^{(R)}$ system (ViewRay Inc., Cleveland, OH, USA) which is representative of magnetic resonance (MR) guided radiotherapy machine, it is important to evaluate effectiveness of AAPM's TG-51 protocol and the effect of the magnetic field on absolute dosimetry. In order to measure the absolute dose, MR-compatible chamber and water phantom system manufactured in this study were used. The materials of the water phantom system were plastic of polymethyl methacrylate (PMMA) and non-ferrous materials. Due to the inherent feature of the $ViewRay^{(R)}$, all Co-60 sources are not located at gantry angle of $0^{\circ}$ while being located at gantry angle of $90^{\circ}$. For this reason, absolute dosimetry was performed based on the measurements in solid water phantom (SWP) and water which determine the SWP to water correction factor. For evaluation of output constancy with gantry angle, measurements were made with ionization chamber inserted in cylindrical water-equivalent phantom. For measured doses in water, the values of dose deviation according to a reference dose of 200 cGy for Head 1, Head 2 and Head 3 were -0.27%, -0.45% and -0.22%, respectively. For measured doses in SWP, the values of dose deviation according to a reference dose of 200 cGy for Head 1, Head 2 and Head 3 were -1.91%, -2.07% and -1.84%, respectively. All values of dose measured in SWP tended to be less than those measured in water by -1.63%. With the reference gantry angles of $0^{\circ}$ and $90^{\circ}$, the maximum values of deviation for Head 1, Head 2 and Head 3 were 0.48%, 1.06% and 0.40%, respectively. The measurement agreement is within the range of results obtainable for conventional treatment machines. The low strength of the magnetic field does not affect dose measurements. Using the SWP to water correction factor, absolute doses for $ViewRay^{(R)}$ system can be measured.

• 산업경영시스템학회지
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• 제37권2호
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• pp.27-34
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• 2014

In this paper, we investigate the statistical correlation of the time series for temperature measured at the heat box in the automobile drying process. We show, in terms of the sample variance, that a significant non-linear correlation exists in the time series that consist of absolute temperature changes. To investigate further the non-linear correlation, we utilize the volatility, an important concept in the financial market, and induce volatility time series from absolute temperature changes. We analyze the time series of volatilities in terms of the de-trended fluctuation analysis (DFA), a method especially suitable for testing the long-range correlation of non-stationary data, from the correlation perspective. We uncover that the volatility exhibits a long-range correlation regardless of the window size. We also analyze the cross correlation between two (inlet and outlet) volatility time series to characterize any correlation between the two, and disclose the dependence of the correlation strength on the time lag. These results can contribute as important factors to the modeling of forecasting and management of the heat box's temperature.

• 한국분말재료학회지
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• 제22권6호
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• pp.403-407
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• 2015

Poly-methylmetacrylate (PMMA) is mainly applied in the plastic manufacturing industry, but PMMA is weak and gradually got discolor. The strength of PMMA can be improved through organic-inorganic hybrid nano composites with inorganic nano particles such as, $SiO_2$ or ZrO. However, inorganic nano particles are mostly agglomerated spontaneously. In this study, the zeta potential is controlled using different types of organic solvent with different concentrations, dispersibillity of $SiO_2$ nano particles on the PMMA particle are analyzed. When 3 M acetic acid is used, absolute value of the zeta potential is higher, $SiO_2$ nano particle is well attached, and dispersed on the PMMA particle surface. Results indicate that the absolute value of the zeta potential affects the stability of $SiO_2$ dispersion.