• Geomechanics and Engineering
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• v.36 no.5
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• pp.465-474
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• 2024

Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.2
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• pp.125-130
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• 2011

Since conventional optimization that is classified as a deterministic method does not consider the uncertainty involved in a modeling or manufacturing process, an optimum design is often determined to be on the boundaries of the feasible region of constraints. Reliability-based design optimization is a method for obtaining a solution by minimizing the objective function while satisfying the reliability constraints. This method includes an optimization process and a reliability analysis that facilitates the quantization of the uncertainties related to design variables. Moment-based reliability analysis is a method for calculating the reliability of a system on the basis of statistical moments. In general, on the basis of these statistical moments, the Pearson system estimates seven types of distributions and determines the reliability of the system. However, it is technically difficult to practically consider the Pearson Type IV distribution. In this study, we propose an enhanced Pearson Type IV distribution based on a kriging model and validate the accuracy of the enhanced Pearson Type IV distribution by comparing it with a Monte Carlo simulation. Finally, reliability-based design optimization is performed for a system with type IV distribution by using the proposed method.

• Animal Bioscience
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• v.37 no.2
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• pp.274-283
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• 2024

Objective: The aim of the study was to evaluate the physicochemical properties (nutrient composition, pH, water content and activity, sorption properties) and mechanical properties (compression force and energy) of granulated feed mixtures with various inclusion levels of crude fibre concentrates ARBOCEL and VITACEL for broiler chickens, i.e. +0.0% (control group - group C), +0.3%, +0.8%, +1.0%, +1.2%. Methods: The feed mixtures were analyzed for their physicochemical properties (nutrient composition by near-infrared spectroscopy, pH with the use a CP-401 pH meter with an IJ-44C glass electrode, water content was determined with the drying method and activity was determined with the Aqua Lab Series 3, sorption properties was determined with the static method) and mechanical properties (compression force and energy with the use TA-HD plus texture analyzer). The Guggenheim-Anderson-de Boer (GAB) model applied in the study correctly described the sorption properties of the analyzed feed mixtures in terms of water activity. Results: The fibre concentrate type affected the specific surface area of the adsorbent and equilibrium water content in the GAB monolayer (p≤0.05) (significantly statistical). The type and dose of the fibre concentrate influenced the dimensionless C and k parameters of the GAB model related to the properties of the monolayer and multilayers, respectively (p≤0.05). They also affected the pH value of the analyzed feed mixtures (p≤0.05). In addition, crude fibre type influenced water activity (p≤0.05) as well as compression energy (J) and compression force (N) (p≤0.001) (highly significantly statistical) of the feed mixtures. Conclusion: The physicochemical analyses of feed mixtures with various inclusion levels (0.3%, 0.8%, 1.0%, 1.2%) of crude fiber concentrates ARBOCEL or VITACEL demonstrated that both crude fiber types may be used in the feed industry as a feedstuff material to produce starter type mixtures for broiler chickens.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.6
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• pp.587-593
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• 2016

Reliability-based design optimization (RBDO) requires a high computational cost owing to its reliability analysis. A surrogate model is introduced to reduce the computational cost in RBDO. The accuracy of the reliability depends on the accuracy of the surrogate model of constraint boundaries in the surrogated-model-based RBDO. In earlier researches, constraint boundary sampling (CBS) was proposed to approximate accurately the boundaries of constraints by locating sample points on the boundaries of constraints. However, because CBS uses sample points on all constraint boundaries, it creates superfluous sample points. In this paper, efficient constraint boundary sampling (ECBS) is proposed to enhance the efficiency of CBS. ECBS uses the statistical information of a kriging surrogate model to locate sample points on or near the RBDO solution. The efficiency of ECBS is verified by mathematical examples.

• Journal of Electrochemical Science and Technology
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• v.6 no.4
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• pp.131-138
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• 2015

Electrical discharge machining is an unconventional machining process in which successive sparks applied to machine the electrically conductive materials. Any changes in electrical discharge machining parameters lead to the pieces with distinct surface roughness. The electrical discharge machining process is well applied for high hardness materials or when it is difficult to use traditional techniques to do material removing. Furthermore, this method is widely applied in industries such as aerospace, automobile, molding, and tool making. CK45 is one of the important steels in industrial and electrical discharge machining can be considered as a proper way for its machining because of high hardness of CK45 after thermal operation of the electrical discharge machining process. Optimization of surface roughness as an output parameters as well as electrical discharge machining parameters including current, voltage and frequency for electrical discharge machining of CK45 has been studied using copper tools and kerosene as the dielectric. For such a purpose and to achieve the precise statistical analysis of the experiment results design of experiment was applied while non linear regression method was chosen to assess the response of surface roughness. Then, the results were analyzed by means of ANOVA method and machining parameters with more effects on the desired outputs were determined. Finally, mathematical model obtained for surface roughness.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1493-1510
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• 2000

Theoretical models that can be used to predict the range of main lobe widths and the probability distribution of the peak sidelobe levels of two-dimensionally sparse arrays are presented here. The arrays are considered to comprise microphones that are randomly positioned on a segmented grid of a given size. First, approximate expressions for the expected squared magnitude of the aperture smoothing function and the variance of the squared magnitude of the aperture smoothing function about this mean are formulated for the random arrays considered in the present study. By using the variance function, the mean value and the lower end of the range i.e., the first I percent of the mainlobe distribution can be predicted with reasonable accuracy. To predict the probability distribution of the peak sidelobe levels, distributions of levels are modeled by a Weibull distribution at each peak in the sidelobe region of the expected squared magnitude of the aperture smoothing function. The two parameters of the Weibull distribution are estimated from the means and variances of the levels at the corresponding locations. Next, the probability distribution of the peak sidelobe levels are assumed to be determined by a procedure in which the peak sidelobe level is determined as the maximum among a finite number of independent random sidelobe levels. It is found that the model obtained from the above approach predicts the probability density function of the peak sidelobe level distribution reasonably well for the various combinations of two different numbers of microphones and grid sizes tested in the present study. The application of these models to the design of random, sparse arrays having specified performance levels is also discussed.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.339-346
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• 2023

The uncertainties of microstructural features affect the properties of materials. Numerous pores that are randomly distributed in materials make it difficult to predict the properties of the materials. The distribution of pores in cementitious materials has a great influence on their mechanical properties. Existing studies focus on analyzing the statistical relationship between pore distribution and material responses, and the correlation between them is not yet fully determined. In this study, the mechanical response of cementitious materials is predicted through an image-based data approach using a convolutional neural network (CNN), and the correlation between pore distribution and material response is analyzed. The dataset for machine learning consists of high-resolution micro-CT images and the properties (tensile strength) of cementitious materials. The microstructures are characterized, and the mechanical properties are evaluated through 2D direct tension simulations using the phase-field fracture model. The attributes of input images are analyzed to identify the spot with the greatest influence on the prediction of material response through CNN. The correlation between pore distribution characteristics and material response is analyzed by comparing the active regions during the CNN process and the pore distribution.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.183-190
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• 2015

Invasive blood pressure (IBP) is measured for the patient's real time arterial pressure (ABP) to monitor the critical abrupt disorders of the cardiovascular system. It can be used for the estimation of cardiac output and the opening and closing time detection of the aortic valve. Although the unexplained inflections on ABP make it difficult to find the mathematical relations with other cardiovascular parameters, the estimations based on ABP for other data have been accepted as useful methods as they had been verified with the statistical results among vast patient data. Previous windkessel models were composed with systemic resistance and vascular compliance and they were successful at explaining the average systolic and diastolic values of ABP simply. Although it is well-known that the blood pressure reflection from peripheral arteries causes complex inflection on ABP, previous models do not contain any elements of the reflections because of the complexity of peripheral arteries' shapes. In this study, to simulate a reflection wave of blood pressure, a new mathematical model was designed with four elements that were the impedance of aorta, the compliance of aortic arch, the peripheral resistance, and the compliance of peripheral arteries. The parameters of the new model were adjusted to have three types of arterial blood pressure waveform that were measured from a patient. It was used to find the relations between the inflections and other cardiovascular parameters such as the opening-closing time of aortic valve and the cardiac output. It showed that the blood pressure reflection can bring wide range errors to the closing time of aortic valve and cardiac output with the conventional estimation based on ABP and that the changes of one-stroke volumes can be easily detected with previous estimation while the changes of heart rate can bring some error caused by unexpected reflections.

• Computers and Concrete
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• v.31 no.2
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.