• Computers and Concrete
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• v.24 no.2
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• pp.137-150
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• 2019

In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.378-385
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• 2023

The importance of wearing respiratory protective equipment has been highlighted even more during the COVID-19 pandemic. Even if the suitability of respiratory protection has been confirmed through testing in a laboratory environment, there remains the potential for leakage points in the respirators due to improper application by the wearer, damage to the equipment, or sudden movements in real working conditions. In this paper, we propose a method to detect the occurrence of leak holes by measuring the pressure changes inside the mask according to the wearer's breathing activity by attaching an IoT sensor to a full-face respirator. We designed 9 experimental scenarios by adjusting the degree of leak holes of the respirator and the breathing cycle time, and acquired respiratory data for the wearer of the respirator accordingly. Additionally, we analyzed the respiratory data to identify the duration and pressure change range for each breath, utilizing this data to train a neural network model for detecting leak holes in the respirator. The experimental results applying the developed neural network model showed a sensitivity of 100%, specificity of 94.29%, and accuracy of 97.53%. We conclude that the effective detection of leak holes can be achieved by incorporating affordable, small-sized IoT sensors into respiratory protective equipment.

• BMB Reports
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• v.40 no.3
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• pp.448-452
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• 2007

We examined the contribution of CYP2C9 and CYP2C19 genotypes and drug interactions to the phenytoin metabolism among 97 Korean epileptic patients to determine if pharmacogenetic testing could be utilized in routine clinical practice. The CYP2C9 polymorphism is a wellknown major genetic factor responsible for phenytoin metabolism. The CYP219 polymorphism, with a high incidence of variant alleles, has a minor influence on phenytoin treated Koran patients. Using a multiple regression model for evaluation of the CYP2C9 and CYP2C19 genotypes, together with other non-genetic variables, we explained 39.6% of the variance in serum phenytoin levels. Incorporation of genotyping for CYP2C9 and CYP2C19 into a clinical practice may be of some help in the determination of phenytoin dosage. However, because concurrent drug treatment is common in patients taking phenytoin and many environmental factors are likely to play a role in drug metabolism, these factors may overwhelm the relevance of CYP polymorphisms in the clinical setting. Further investigations with an approach to dose assessment that includes comprehensive interpretation of both pharmacogenetic and pharmacokinetic data along with understanding of the mechanism of drug interactions in dosage adjustment is warranted.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.61-70
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• 2011

This study developed a permanent deformation model for asphalt concrete based on shear properties. Repeated load triaxial compression (RLTC), triaxial compressive strength, and indirect tension strength tests were performed for the three types of asphalt mixtures at various loading and temperature conditions to correlate shear properties of asphalt mixtures to rutting performance. For the given mixtures, as testing temperature increased, cohesion decreased, but friction angle was insensitive to temperature at $40^{\circ}C$ or higher. It was observed that deviatoric stress, confining pressure, temperature, and load frequency affected the permanent deformation of asphalt mixtures significantly. The permanent deformation model based on shear stress to strength ratio and loading time was developed using the laboratory test results and calibrated using accelerated pavement test data. The proposed model was able to predict the permanent deformation of the asphalt mixtures in a wide range of loading and temperature conditions with constant model coefficients.

• Geomechanics and Engineering
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• v.22 no.5
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• pp.397-414
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• 2020

In this paper, practical predictive models for soil shear strength parameters are proposed. As cohesion and internal friction angle are of essential shear strength parameters in any geotechnical studies, we try to predict them via artificial neural network (ANN) and neuro-imperialism approaches. The proposed models was based on the result of a series of consolidated undrained triaxial tests were conducted on reinforced sandy soil. The experimental program surveys the increase in internal friction angle of sandy soil due to addition of polypropylene fibers with different lengths and percentages. According to the result of the experimental study, the most important parameters impact on internal friction angle i.e., fiber percentage, fiber length, deviator stress, and pore water pressure were selected as predictive model inputs. The inputs were used to construct several ANN and neuro-imperialism models and a series of statistical indices were calculated to evaluate the prediction accuracy of the developed models. Both simulation results and the values of computed indices confirm that the newly-proposed neuro-imperialism model performs noticeably better comparing to the proposed ANN model. While neuro-imperialism model has training and test error values of 0.068 and 0.094, respectively, ANN model give error values of 0.083 for training sets and 0.26 for testing sets. Therefore, the neuro-imperialism can provide a new applicable model to effectively predict the internal friction angle of fiber-reinforced sandy soil.

• Journal of the Korean Geotechnical Society
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• v.28 no.2
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• pp.23-31
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• 2012

The purpose of this study is to analyze earth pressure acting on a circular shaft-tunnel considering arching effect by centrifuge modeling test on sands. The centrifuge testing method provides a way to model an in-situ stress state condition with a stress gradient within a laboratory specimen. A small-scale model of circular shaft-tunnel, which has a real diameter of 6.0 m and height of 15.0 m, was designed and tested twice under 75g-level. Additionally, an effect of excavation was presented by separating two segments of circular shaft wall to find behavioral properties and strength of earth pressure along with excavating ground. The test results were compared with those of the proposed earth pressure equation. The test results showed that earth pressure decreased by about 70% in comparison with existing two-dimensional earth pressure. This fact might be attributed to three-dimensional arching effects.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5963-5973
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• 2011

Current design and analyzing methods about soil nailing structures, developed on the basis of results obtained from experiments in laboratory or in field and numerical analyses, have applied different interaction mechanisms between the reinforced nails and the surrounding ground, and different safety factors against failure have been obtained. They might be proper approaches if the assumptions about rigidity of nails and ground conditions are met with actual conditions occurred in field. Otherwise, they would result in designing on analyzing in inappropriate ways so that it is needed to evaluate the validity of them. Therefore, in this research using the Centrifugal Model Testing, numerical parameters experiments about soil nailing structures' behavior and failure mechanism were performed. In the numerical parameters experiments, transmuted nail's length, setting angle, nail's front panel, stiffness variously, and increased the level of gravity until wall model was destroyed. Based on experimental results, we compared the effect, failure mechanism caused from parameters changes. By reviewing and comparing centrifugal model test results and methods currently in use, verified validity of existing methods.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.2
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• pp.123-136
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• 2004

Terzaghi's one-dimensional consolidation theory has some important assumption, which can't be applicable to predict the behavior of soft clay ground. Especially, predictions using infinitesimal strain and linear material function related with permeability can give rise to mistake in comparison with the result of real behavior in site. For this reason, Gibson et al. established a rigorous formulation for the one-dimensional nonlinear finite strain consolidation theory, which can consider non-linearity of material function. But it is difficult to apply this theory to predict the behavior of common soft clay ground with vertical drain. In this study, consolidation model which can consider the vertical and horizontal flow of a fully saturated clay layer, self-weight of soil and nonlinear characteristics of compressibility and permeability are derived. Numerical analysis scheme, which can be applied to consolidation analysis by derived consolidation model in this study was developed. The characteristics of material function were examined using laboratory testing such as standard consolidation test, Rowe-cell test and modified consolidation test.

• Smart Structures and Systems
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• v.26 no.4
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• pp.495-506
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• 2020

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.1729-1732
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• 2016

In the patients with metastatic colorectal cancer (mCRC), RAS testing is the first step to identify those that could benefit from anti-EGFR therapy. This study examined associations between KRAS mutations and clinicopathological and survival data in Iranian patients with mCRC. Between 2008 to2015 in a retrospective study, 83 cases of mCRC were referred to the Clinic of Medical Oncology. The mean follow-up was 45 months that there were 27 deaths. The 3 patients that did not complete follow-up were censored from the study. KRAS and NRAS were analyzed using allele-specific PCR primers and pyrosequencing in exons 2, 3 and 4. Multivariate survival analysis using Cox's regression model was used for affecting of variables on overall survival (OS). The mean age at diagnosis for patients was 57.7 (range, 18 to 80 years) and 61.4% were male. There was no significant different between prognostic factors and KRAS mutation with wild-type. Also, There was no significant different between KRAS mutation and KRAS wild-type for survival, but there was a significant different between KRAS 12 and 13 mutations for survival (HR 0.13, 95% CI 0.03-0.66, P=0.01). In conclusion, the prevalence of KRAS mutations in CRC patients was below 50% but higher than in other studies in Iran. As in many studies, patients with KRAS 12 mutations had better OS thn those with KRAS 13 mutation. In addition to KRAS testing, other biomarkers are needed to determine the best treatment for patients with mCRC.