• Computers and Concrete
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• 제11권6호
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• pp.587-602
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• 2013

In the current study, a method for concrete compressive strength prediction (based on cement strength class), incorporated in a software package developed by the authors for the estimation of concrete service life under harmful environments, is presented and validated. Prediction of concrete compressive strength, prior to real experimentation, can be a very useful tool for a first mix screening. Given the fact that lower limitations in strength have been set in standards, to attain a minimum of service life, a strength approach is a necessity. Furthermore, considering the number of theoretical attempts on strength predictions so far, it can be seen that although they lack widespread accepted validity, certain empirical expressions are still widely used. The method elaborated in this study, it offers a simple and accurate, compressive strength estimation, in very good agreement with experimental results. A modified version of the Feret's formula is used, since it contains only one adjustable parameter, predicted by knowing the cement strength class. The approach presented in this study can be applied on any cement type, including active additions (fly ash, silica fume) and age.

• Computers and Concrete
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• 제10권4호
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• pp.391-407
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• 2012

Utilization of supplementary cementing materials (SCM) by the cement industry, as a highly promising solution of sustainable cement development aiming to reduce carbon dioxide emissions, necessitates a more thorough evaluation of these types of materials on concrete durability. In this study a comparative assessment of the effect of SCM on concrete durability, of every cement type as defined in the European Standard EN 197-1 is taking place, using a software tool, based on proven predictive models (according to performance-related methods for assessing durability) developed and wide-validated for the estimation of concrete service life when designing for durability under harsh environments. The effect of Type II additives (fly ash, silica fume) on CEM I type of cement, as well as the effect of every Portland-composite type of cement (and others) are evaluated in terms of their performance in carbonation and chloride exposure, for a service life of 50 years. The main aim is to portray a unified and comprehensive evaluation of the efficiency of SCM in order to create the basis for future consideration of more types of cement to enter the production line in industry.

• Annals of Clinical Neurophysiology
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• 제9권2호
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• pp.69-74
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• 2007

Electroencephalogram (EEG) is a representative diagnostic tool in epilepsy. However, there are several points of debate on the role of EEG in diagnosis and management of epilepsy. We suggest that EEG has some limitations for differential diagnosis from nonepileptic episodic diseases, classification of epilepsy, prediction of recurrence, and evaluation of treatment response. Interictal EEG cannot diagnose or exclude epilepsy because interictal epileptic discharge (IED) is frequently absent in epilepsy and can appear in nonepileptic conditions. Although EEG is helpful in classification of epilepsy, focal spikes in generalized epilepsy and secondary bilateral synchrony in localization related epilepsy cause interrater disagreement. It is controversial whether EEG predicts recurrence after the first seizure in adults. The predictive value of EEG in antiepileptic drug (AED) withdrawal is not absolute. The prognosis after AED withdrawal depends on epilepsy syndrome. Many studies could not confirm the value of EEG in assessing the treatment response. After all, epilepsy is clinically diagnosed and assessed. Interictal EEG alone does not provide decisive information and routine follow-up of EEG is not recommended.

• Tribology and Lubricants
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• 제30권6호
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• pp.378-383
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• 2014

This study predicts the of the hysteretic friction of a rubber block sliding on an SMA asphalt road. The friction of filled rubber on a rough surface is primarily determined by two elements:the viscoelasticity of the rubber and the multi-scale perspective asperities of the road. The surface asperities of the substrate exert osillating forces on the rubber surface leading to energy dissipation via the internal friction of the rubber when rubber slides on a hard and rough substrate. This study defines the power spectra at different length scales by using a high-resolution surface profilometer, and uses rubber and road surface samples to conduct friction tests. I consider in detail the case when the substrate surface has a self affine fractal structure. The theory developed by Persson is applied to describe these tests through comparison with the hysteretic friction coefficient relevant to the energy dissipation of the viscoelastic rubber attributable to cyclic deformation. The results showed differences in the absolute values of predicted and measured friction, but with high correlation between these values. Hence, the friction prediction model is an appropriate tool for separating the effects of each factor. Therefore, this model will contribute to clearer understanding of the fundamental principles of rubber friction.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Structural Engineering and Mechanics
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• 제18권2호
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• pp.211-229
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• 2004

The main purpose of this paper is the numerical analysis of the non-linear seismic response of a RC building mock-up. The mock-up is subjected to different synthetic horizontal seismic excitations. The numerical approach is based on a 3D-model involving multilayered shell elements. These elements are composed of several single-layer membranes with various eccentricities. Bending effects are included through these eccentricities. Basic equations are first written for a single membrane element with its own eccentricity and then generalised to the multilayered shell element by superposition. The multilayered shell is considered as a classical shell element : all information about non-linear constitutive relations are investigated at the local scale of each layer, whereas balance and kinematics are checked afterwards at global scale. The non-linear dynamic response of the building is computed with Newmark algorithm. The numerical dynamic results (blind simulations) are considered in the linear and non linear cases and compared with experimental results from shaking table tests. Multilayered shell elements are found to be a promising tool for predictive computations of RC structures behaviour under 3D seismic loadings. This study was part of the CAMUS International Benchmark.

• Advances in concrete construction
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• 제1권3호
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• pp.201-213
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• 2013

While recognizing the problem of reinforcement corrosion and premature structural deterioration of reinforced concrete (RC) structures as a combined effect of mechanical and environmental actions (carbonation, ingress of chlorides), emphasis is given on the effect of the latter, as most severe and unpredictable action. In this study, a simulation tool, based on proven predictive models utilizing principles of chemical and material engineering, for the estimation of concrete service life is applied on an existing reinforced concrete bridge (${\O}$resund Link) located in a chloride environment. After a brief introduction to the structure of the models used, emphasis is given on the physicochemical processes in concrete leading to chloride induced corrosion of the embedded reinforcement. By taking under consideration the concrete, structural and environmental properties of the bridge investigated, an accurate prediction of its service life is taking place. It was observed that the proposed, and already used, relationship of service lifetime- cover is almost identical with a mean line between the lines derived from the minimum and maximum critical values considered for corrosion initiation. Thus, an excellent agreement with the project specifications is observed despite the different ways used to approach the problem. Furthermore, different scenarios of concrete cover failure, in the case when a coating is utilized, and extreme deicing salts attack are also investigated.

• Geomechanics and Engineering
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• 제18권5호
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• pp.493-502
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• 2019

Internal stability is an important safety issue for levees, embankments, and other earthen structures. Since a large part of the world's population lives near oceans, lakes and rivers, floods resulting from breaching of dams can lead to devastating disasters with tremendous loss of life and property, especially in densely populated areas. There are some main factors that affect the internal stability of dams, levees and other earthen structures, such as the erodibility of the soil, the water velocity inside the soil mass and the geometry of the earthen structure, etc. Thus, the mechanism of internal erosion and stability of soils is very complicated and it is vital to investigate the assessment methods of internal stability of soils in embankment dams and their foundations. This paper presents an improved support vector machine (SVM) model to predict the internal stability of soils. The grid search algorithm (GSA) is employed to find the optimal parameters of SVM firstly, and then the cross - validation (CV) method is employed to estimate the classification accuracy of the GSA-SVM model. Two examples of internal stability of soils are presented to validate the predictive capability of the proposed GSA-SVM model. In addition to verify the effectiveness of the proposed GSA-SVM model, the predictions from the proposed GSA-SVM model were compared with those from the traditional back propagation neural network (BPNN) model. The results showed that the proposed GSA-SVM model is a feasible and efficient tool for assessing the internal stability of soils with high accuracy.

• 한국기계가공학회지
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• 제18권2호
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• pp.58-64
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• 2019

This paper describes the analysis of dynamic characteristics and prediction of the stiffness for the joint between structural members. In the process of deriving the governing equations, the stiffness values responsible for the moment and shear force were modelled by using linear and torsional springs in the middle of a clamped-clamped beam. The sensitivities of the natural frequency and modal assurance criterion were investigated as a function of the dimensionless linear and torsional spring stiffness. The reliability of the predictions for the linear and torsional stiffness values was verified by the inverse computations of the stiffness matrix. The predictive and exact theoretical stiffness values were compared for the stiffness element in the finite element formulation, and their results show an excellent correlation. It is strongly anticipated that although the proposed methodology is currently limited to the analytical utilization, it will provide a useful tool to estimate unknown joint stiffness values based on the experimental natural frequency and mode shape.

• Clinical and Experimental Reproductive Medicine
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• 제46권2호
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• pp.50-59
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• 2019

Anti-$M{\ddot{u}}llerian$ hormone (AMH), a peptide growth factor of the transforming growth $factor-{\beta}$ family, is a reliable marker of ovarian reserve. Regarding assisted reproductive technology, AMH has been efficiently used as a marker to predict ovarian response to stimulation. The clinical use of AMH has recently been extended and emphasized. The uses of AMH as a predictive marker of menopause onset, diagnostic tool for polycystic ovary syndrome, and assessment of ovarian function before and after gynecologic surgeries or gonadotoxic agents such as chemotherapy have been investigated. Serum AMH levels can also be affected by environmental and genetic factors; thus, the effects of factors that may alter AMH test results should be considered. This review summarizes the findings of recent studies focusing on the clinical application of AMH and factors that influence the AMH level and opinions on the use of the AMH level to assess the probability of conception before reproductive life planning as a "fertility test."