• Computers and Concrete
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• v.18 no.2
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• pp.201-214
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• 2016

In this paper, a multilaminate based model have been developed and presented to predict the strain hardening behavior of rock. In this multilaminate model, the stress-strain behavior of a material is obtained by integrating the mechanical response of an infinite number of predefined oriented planes passing through a material point. Essential features such as the variable deformations hypothesis and multilaminate model are discussed. The methodology to be discussed here is modeling of strains on the 13 laminates passing through a point in each loading step. Upon the presented methodology, more attention has been given to hardening in non-linear behaviour of rock in going from the peak to residual strengths. The predictions of the derived stress-strain model are compared to experimental results for marble, sandstone and dense Cambria sand. The comparisons demonstrate the ability of this model to reproduce accurately the mechanical behavior of rocks.

• Advances in concrete construction
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• v.13 no.5
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• pp.361-365
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• 2022

The objective of this study is to simulate the two-phase flow in pipes with various two-fluid models and determinate the shear stress. A hyperbolic shear deformation theory is used for modelling of the pipe. Two-fluid models are solved by using the conservative shock capturing method. Energy relations are used for deriving the motion equations. When the initial conditions of problem satisfied the Kelvin Helmholtz instability conditions, the free-pressure two-fluid model could accurately predict discontinuities in the solution field. A numerical solution is applied for computing the shear stress. The two-pressure two-fluid model produces more numerical diffusion compared to the free-pressure two-fluid and single-pressure two-fluid models. Results show that with increasing the two-phase percent, the shear stress is reduced.

• Coupled systems mechanics
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• v.12 no.6
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• pp.539-555
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• 2023

Although some prediction models have successfully developed for ultra-high performance concrete (UHPC), they do not provide insights and explicit relations between all constituents and its consistency, and compressive strength. In the present study, based on the experimental results, several mathematical models have been evaluated to predict the consistency and the 28-day compressive strength of UHPC. The models used were Linear, Logarithmic, Inverse, Power, Compound, Quadratic, Cubic, Mixed, Sinusoidal and Cosine equations. The applicability and accuracy of these models were investigated using experimental data, which were collected from literature. The comparisons between the models and the experimental results confirm that the majority of models give acceptable prediction with a high accuracy and trivial error rates, except Linear, Mixed, Sinusoidal and Cosine equations. The assessment of the models using numerical methods revealed that the Quadratic and Inverse equations based models provide the highest predictability of the compressive strength at 28 days and consistency, respectively. Hence, they can be used as a reliable tool in mixture design of the UHPC.

• Earthquakes and Structures
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• v.27 no.2
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• pp.113-126
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• 2024

In this study, the failure mechanisms of masonry-infilled frames, commonly employed in modern construction, are analyzed at the mesoscale. An equation has been formulated to predict various failure modes of masonry-infilled frames by examining 1392 frames. The equation takes into account variables such as the height-to-width ratio, compressive strength of the masonry prism, and plastic moment capacity of the frame section. The study reveals that the compressive strength of the masonry prism and the height-to-width ratio exert the most significant influence on the lateral strength of masonry-infilled frames with a height-to-width ratio ranging from 0.2 to 1.2. The developed equation demonstrates substantial agreement with previously reported relationships, indicating high accuracy. These findings provide valuable insights into the lateral strength of infill masonry frames, which can contribute to their improved evaluation and design.

• Steel and Composite Structures
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• v.53 no.1
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• pp.115-121
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• 2024

The present paper deals with a comprehensive study about dynamic performance and educational economic assessment of nanocomposite structures, while it focuses on truncated conical shells. Advanced structure dynamic behavior has been analyzed by means of AI techniques, which allow one to predict and optimize their performances with good accuracy for different loading and environmental conditions. The incorporation of the AI method significantly enhances the computational efficiency and is a powerful tool in designing nanocomposites and for their structural analysis. Further, an educational assessment is provided in the context of cost and practicality related to such structures in engineering education. This study showcases the capabilities of AI-enabled methods with regard to cost reduction, improvement of structural efficiency, and enhancement of learning engagement for students through certain practical examples on state-of-the-art nanocomposite technology. The results also confirm a remarkable capability of artificial intelligence regarding the optimization of both dynamic and economic aspects, which could be highly valued for further development of nanocomposite structures.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.125-133
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• 2014

This paper develops a bankruptcy prediction model on an Artificial Neural Network for hotel management. A bankruptcy prediction model has a specific feature to predict a bankruptcy of the whole hotel business after evaluate bankruptcy possibility on the basis of business performance data of each branch. here are many traditional statistical models for bankruptcy prediction such as Multivariate Discriminant Analysis or Logit Analysis. However, we chose Artificial Neural Network because the method has accuracy rates of prediction better than those of other methods. We first selected 100 good enterprises and 100 bankrupt enterprises as experimental data and set up a bankruptcy prediction model by use of a tool for Artificial Neural Network, NeuroShell. The model and its experiments, which demonstrated high efficiency, can certainly provide great help in decision making in the field of hotel management and in deciding on the bankruptcy or financial solidity of each branch of serviced residence hotel.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.5
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• pp.391-399
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• 2011

The purpose of this paper is to predict the shock absorbing characteristics of the aluminum honeycomb in a lunar lander. Aluminum honeycomb has been used for shock absorbers of lunar lander due to its characteristics such as light weight, high energy absorption efficiency and applicability under severe space environments. Crush strength of the honeycomb should have strength to endure during shock energy absorbing process. In this paper, the crush strength, which depends on the shape of honeycomb and impact velocity, is estimated using FEM. Ls-dyna is used for finite element analysis of the honeycomb shock absorber. The unit cells of the honeycomb shape are modeled and used for the finite element analysis. Energy absorption characteristics are decided considering several conditions such as impact velocity, foil thickness and branch angle of the honeycomb.

• Journal of Korean Neurosurgical Society
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• v.60 no.4
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• pp.456-464
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• 2017

Objective : Although little is known about its origins, neck pain may be related to several associated anatomical pathologies. We aimed to characterize the incidence and features of chronic neck pain and analyze the relationship between neck pain severity and its affecting factors. Methods : Between March 2012 and July 2013, we studied 216 patients with chronic neck pain. Initially, combined tramadol (37.5 mg) plus acetaminophen (325 mg) was administered orally twice daily (b.i.d.) to all patients over a 2-week period. After two weeks, patients were evaluated for neck pain during an outpatient clinic visit. If the numeric rating scale of the patient had not decreased to 5 or lower, a cervical medial branch block (MBB) was recommended after double-dosed previous medication trial. We classified all patients into two groups (mild vs. severe neck pain group), based on medication efficacy. Logistic regression tests were used to evaluate the factors associated with neck pain severity. Results : A total of 198 patients were included in the analyses, due to follow-up loss in 18 patients. While medication was successful in reducing pain in 68.2% patients with chronic neck pain, the remaining patients required cervical MBB. Lateral cervical curvature, such as a straight or sigmoid type curve, was found to be significantly associated with the severity of neck pain. Conclusion : We managed chronic neck pain with a simple pharmacological management protocol followed by MBB. We should keep in mind that it may be difficult to manage the patient with straight or sigmoid lateral curvature only with oral medication.

• Steel and Composite Structures
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• v.33 no.3
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• pp.319-332
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• 2019

This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model.

• Computers and Concrete
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• v.23 no.1
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• pp.61-68
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• 2019

This paper presents a computational rational model to predict the ultimate and optimized load capacity of reinforced concrete (RC) beams strengthened by a combination of longitudinal and transverse fiber reinforced polymer (FRP) composite plates/sheets (flexure and shear strengthening system). Several experimental and analytical studies on the confinement effect and failure mechanisms of fiber reinforced polymer (FRP) wrapped columns have been conducted over recent years. Although typical axial members are large-scale square/rectangular reinforced concrete (RC) columns in practice, the majority of such studies have concentrated on the behavior of small-scale circular concrete specimens. A high performance concrete, known as polymer concrete, made up of natural aggregates and an orthophthalic polyester binder, reinforced with non-metallic bars (glass reinforced polymer) has been studied. The material is described at micro and macro level, presenting the key physical and mechanical properties using different experimental techniques. Furthermore, a full description of non-metallic bars is presented to evaluate its structural expectancies, embedded in the polymer concrete matrix. In this paper, the mechanism of mechanical interaction of smooth and lugged FRP rods with concrete is presented. A general modeling and application of various elements are demonstrated. The contact parameters are defined and the procedures of calculation and evaluation of contact parameters are introduced. The method of calibration of the calculated parameters is presented. Finally, the numerical results are obtained for different bond parameters which show a good agreement with experimental results reported in literature.