• Structural Engineering and Mechanics
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• v.14 no.4
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• pp.435-454
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• 2002

The paper proposes analytical forms able to represent with very good approximation the constitutive law experimentally deducible by means of uniaxial cyclic compressive tests on material having softening post-peak behaviour in compression and negligible tensile strength. The envelope, unloading and reloading curves characterizing the proposed model adequately approach structural responses corresponding to different levels of nonlinearity and ductility, requiring a not very high number of parameters to be calibrated experimentally. The reliability of the model is shown by comparing the results that it is able to provide with the ones analytically deduced from two reference models (one for concrete, another for masonry) available in the literature, and with experimental results obtained by the authors in the framework of a research in progress.

• International Journal of Concrete Structures and Materials
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• v.10 no.2
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• pp.143-147
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• 2016

In recent years, some modifications were introduced in the stress-strain relationship of the steel in order to develop a more efficient shear model for reinforced concrete members. The last contribution in this sense corresponding to the Refined Compression Field Theory (RCFT, 2009); this theory proposed a steel constitutive model that has account the tension stiffening area prescribed by technical codes, what simplifies all the design process. However, under certain design conditions supported by such codes, the RCFT model does not provide a real (non-complex) solution for the steel yield strain when the prescribed tension stiffening area is considered; then the load-strain response cannot be computed. In this technical note, the tension stiffening area is fixed in order to guarantee the application of the embedded steel constitutive model for all the standard design range.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10a
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• pp.279-284
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• 2000

The existing microplane models for concrete ust three-dimensional spherical microplanes even in the analyses for two-dimensional members. Also, they can not describe accurately the post-cracking behavior of reinforced concrete in tension-compression. In this study, a new microplane model that is appropriate for the analyses of reinforced concrete planar members was developed to complement these disadvantages of the existing models. The proposed microplane model uses disk microplanes instead of the existing spherical ones. This new model is effective in numerical analysis because it uses less number of microplanes and two-dimensional stresses. Also, in this microplane model a concept of strain boundary was introduced to describe compressive behavior of reinforced concrete in tension-compression.

• Steel and Composite Structures
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• v.33 no.2
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• pp.181-194
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• 2019

This paper presents an efficient approach to generate a new empirical formula to predict the axial compression capacity (ACC) of circular concrete-filled tube (CCFT) columns using the artificial neural network (ANN). A total of 258 test results extracted from the literature were used to develop the ANN models. The ANN model having the highest correlation coefficient (R) and the lowest mean square error (MSE) was determined as the best model. Stability analysis, sensitivity analysis, and a parametric study were carried out to estimate the stability of the ANN model and to investigate the main contributing factors on the ACC of CCFT columns. Stability analysis revealed that the ANN model was more stable than several existing formulae. Whereas, the sensitivity analysis and parametric study showed that the outer diameter of the steel tube was the most sensitive parameter. Additionally, using the validated ANN model, a new empirical formula was derived for predicting the ACC of CCFT columns. Obviously, a higher accuracy of the proposed empirical formula was achieved compared to the existing formulae.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.29 no.3
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• pp.163-174
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• 2023

Though box compression strength (BCS) is commonly used as a performance criterion for shipping containers, estimating BCS remains a challenge. In this study, artificial neural networks (ANN) are implemented as a new tool, with a focus on building up ANN architectures for BCS estimation. An Artificial Neural Network (ANN) model can be constructed by adjusting four modeling factors: hidden neuron numbers, epochs, number of modeling cycles, and number of data points. The four factors interact with each other to influence model accuracy and can be optimized by minimizing model's Mean Squared Error (MSE). Using both data from the literature and "synthetic" data based on the McKee equation, we find that model estimation accuracy remains limited due to the uncertainty in both the input parameters and the ANN process itself. The population size to build an ANN model has been identified based on different data sets. This study provides a methodology guide for future research exploring the applicability of ANN to address problems and answer questions in the corrugated industry.

• Transactions of Materials Processing
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• v.32 no.6
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• pp.367-375
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• 2023

Composite materials, known for their excellent mechanical properties and lightweight characteristics, are applied in various engineering fields. Recently, efforts have been made to develop an automotive battery protection panel using a plain-woven composite composed of glass fiber and polypropylene to reduce the weight of automobiles. However, excessive warpage occurs during the GF/PP compression molding process, which makes car assembly challenging. This study aims to develop a model that predicts the warpage during the compression molding process. Obtaining out-of-plane properties such as elastic or shear modulus, essential for predicting warpages, is tricky. Existing mechanical methods also have limitations in calculating these properties for woven composite materials. To address this issue, finite element analysis is conducted using representative volume elements (RVE) for woven composite materials. A warpage prediction model is developed based on the estimated physical properties of GF/PP composite materials obtained through representative volume elements. This model is expected to be used for reducing warpages in the compression molding process.

• ETRI Journal
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• v.46 no.3
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• pp.538-549
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• 2024

A conventional codec aims to increase the compression efficiency for transmission and storage while maintaining video quality. However, as the number of platforms using machine vision rapidly increases, a codec that increases the compression efficiency and maintains the accuracy of machine vision tasks must be devised. Hence, the Moving Picture Experts Group created a standardization process for video coding for machines (VCM) to reduce bitrates while maintaining the accuracy of machine vision tasks. In particular, in-loop filters have been developed for improving the subjective quality and machine vision task accuracy. However, the high computational complexity of in-loop filters limits the development of a high-performance VCM architecture. We analyze the effect of an in-loop filter on the VCM performance and propose a suboptimal VCM method based on the selective activation of in-loop filters. The proposed method reduces the computation time for video coding by approximately 5% when using the enhanced compression model and 2% when employing a Versatile Video Coding test model while maintaining the machine vision accuracy and compression efficiency of the VCM architecture.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Transactions of Materials Processing
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• v.5 no.2
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• pp.156-164
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• 1996

To investigate the behavior of plastic deformation in axisymmetric extrusion through square dies, experimental works with the plasticine as a model material are carried out at the room temperature. Some mechanical properties of the model material are determined by compression and ring compression tests. Visioplasticity method using expermental grid distortion in extrusion is introduced to analyze the plastic flow strain rate and strain distribution. In spite of severe deformation during the extrusion through square die the visioplasticity method shows good results for the distribution of effective strain rate and effective strain.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.8
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• pp.1129-1140
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• 2008

Many turbulence models have been developed in order to analyze the flow characteristics in an engine cylinder. Watkins introduced k-${\varepsilon}$ turbulence model for in-cylinder flow, and Reynolds modified turbulence dissipation rate by applying rapid transformation theory, Wu suggested k-${\varepsilon}-{\tau}$ turbulence model in which length scale and time scale are separated to introduce turbulence time scale, and Orszag proposed k-${\varepsilon}$ RNG model. This study applied the models to in-cylinder flow induced by intake valve and piston moving. All models showed similar flow fields during early stage of intake stroke. At the end of compression stroke, ${\kappa}-{\varepsilon}$ Watkins, ${\kappa}-{\varepsilon}$ Reynolds and ${\kappa}-{\varepsilon}$ RNG predicted well second and third vortex, especially ${\kappa}-{\varepsilon}$ RNG produced new forth vortex near central axis at the lower part of cylinder which was not predicted by the other models.