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  • Open Access

    ARTICLE

    Estimating the Properties of Ground-Waste-Brick Mortars Using DNN and ANN

    Abdulkadir Karaci1,*, Hasbi Yaprak2, Osman Ozkaraca3, Ilhami Demir4, Osman Simsek5

    CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.1, pp. 207-228, 2019, DOI:10.31614/cmes.2019.04216

    Abstract In this study, deep-neural-network (DNN)- and artificial-neural-network (ANN)-based models along with regression models have been developed to estimate the pressure, bending and elongation values of ground-brick (GB)-added mortar samples. This study is aimed at utilizing GB as a mineral additive in concrete in the ratios 0.0%, 2.5%, 5.0%, 7.5%, 10.0%, 12.5% and 15.0%. In this study, 756 mortar samples were produced for 84 different series and were cured in tap water (W), 5% sodium sulphate solution (SS5) and 5% ammonium nitrate solution (AN5) for 7 days, 28 days, 90 days and 180 days. The developed DNN models have three inputs… More >

  • Open Access

    ARTICLE

    A Computer-Aided Tuning Method for Microwave Filters by Combing T-S Fuzzy Neural Networks and Improved Space Mapping

    Shengbiao Wu1,2,3, Weihua Cao1,3,*, Can Liu1,3, Min Wu1,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.116, No.3, pp. 433-453, 2018, DOI: 10.31614/cmes.2018.03309

    Abstract A computer-aided tuning method that combines T-S fuzzy neural network (T-S FNN) and offers improved space mapping (SM) is presented in this study. This method consists of three main aspects. First, the coupling matrix is effectively extracted under the influence of phase shift and cavity loss after the initial tuning. Second, the surrogate model is realized by using a T-S FNN based on subspace clustering. Third, the mapping relationship between the actual and the surrogate models is established by the improved space mapping algorithm, and the optimal position of the tuning screws are found by updating the input and output… More >

  • Open Access

    ARTICLE

    Neural Network-Based Second Order Reliability Method (NNBSORM) for Laminated Composite Plates in Free Vibration

    Mena E. Tawfik1, 2, Peter L. Bishay3, *, Edward A. Sadek1

    CMES-Computer Modeling in Engineering & Sciences, Vol.115, No.1, pp. 105-129, 2018, DOI:10.3970/cmes.2018.115.105

    Abstract Monte Carlo Simulations (MCS), commonly used for reliability analysis, require a large amount of data points to obtain acceptable accuracy, even if the Subset Simulation with Importance Sampling (SS/IS) methods are used. The Second Order Reliability Method (SORM) has proved to be an excellent rapid tool in the stochastic analysis of laminated composite structures, when compared to the slower MCS techniques. However, SORM requires differentiating the performance function with respect to each of the random variables involved in the simulation. The most suitable approach to do this is to use a symbolic solver, which renders the simulations very slow, although… More >

  • Open Access

    ARTICLE

    An Ensemble Based Hand Vein Pattern Authentication System

    M. Rajalakshmi*

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.2, pp. 209-220, 2018, DOI:10.3970/cmes.2018.114.209

    Abstract Amongst several biometric traits, Vein pattern biometric has drawn much attention among researchers and diverse users. It gains its importance due to its difficulty in reproduction and inherent security advantages. Many research papers have dealt with the topic of new generation biometric solutions such as iris and vein biometrics. However, most implementations have been based on small datasets due to the difficulties in obtaining samples. In this paper, a deeper study has been conducted on previously suggested methods based on Convolutional Neural Networks (CNN) using a larger dataset. Also, modifications are suggested for implementation using ensemble methods. Ensembles were used… More >

  • Open Access

    ARTICLE

    Three Dimensional Natural Frequency Analysis of Sandwich Plates with Functionally Graded Core Using Hybrid Meshless Local Petrov-Galerkin Method and Artificial Neural Network

    Foad Nazari1, Mohammad Hossein Abolbashari1,2, Seyed Mahmoud Hosseini3

    CMES-Computer Modeling in Engineering & Sciences, Vol.105, No.4, pp. 271-299, 2015, DOI:10.3970/cmes.2015.105.271

    Abstract Present study is concerned with three dimensional natural frequency analysis of functionally graded sandwich rectangular plates using Meshless Local Petrov-Galerkin (MLPG) method and Artificial Neural Networks (ANNs).The plate consists of two homogeneous face sheets and a power-law FGM core. Natural frequencies of the plate are obtained by 3D MLPG method and are verified with available references. Convergence study of the first four natural frequencies for different node numbers is the next step. Also, effects of two parameters of “FG core to plate thickness ratio” and “volume fraction index” on natural frequencies of plate are investigated. Then, four distinct ANNs are… More >

  • Open Access

    ARTICLE

    Particle-based Simulations of Flows with Free Surfaces Using Hyperbolic-typeWeighting Functions

    K. Kakuda1, Y. Hayashi1, J. Toyotani1

    CMES-Computer Modeling in Engineering & Sciences, Vol.103, No.4, pp. 229-249, 2014, DOI:10.3970/cmes.2014.103.229

    Abstract In this paper, we present the application of the particle-based simulations to complicated fluid flow problem with free surfaces. The particle approach is based on the MPS (Moving Particle Simulation) method using hyperbolic-type weighting function to stabilize the spurious oscillatory solutions for solving the Poisson equation with respect to the pressure fields. The hyperbolic-type weighting function is constructed by differentiating the characteristic function based on neural network framework. The weighting function proposed herein is collaterally applied to the kernel function in the SPH-framework. Numerical results demonstrate the workability and validity of the present MPS approach through the dambreaking flow problem. More >

  • Open Access

    ARTICLE

    The Application of a Hybrid Inverse Boundary Element Problem Engine for the Solution of Potential Problems

    S. Noroozi1, P. Sewell1, J. Vinney1

    CMES-Computer Modeling in Engineering & Sciences, Vol.14, No.3, pp. 171-180, 2006, DOI:10.3970/cmes.2006.014.171

    Abstract A method that combines a modified back propagation Artificial Neural Network (ANN) and Boundary Element Analysis (BEA) was introduced and discussed in the author's previous papers. This paper discusses the development of an automated inverse boundary element problem engine. This inverse problem engine can be applied to both potential and elastostatic problems.
    In this study, BEA solutions of a two-dimensional potential problem is utilised to test the system and to train a back propagation Artificial Neural Network (ANN). Once training is completed and the transfer function is created, the solution to any subsequent or new problems can be obtained… More >

  • Open Access

    ARTICLE

    Numerical Phenomenology: Virtual Testing of the Hierarchical Structure of a Bundle of Strands

    D.P. Boso1, M. Lefik2

    CMES-Computer Modeling in Engineering & Sciences, Vol.55, No.3, pp. 319-338, 2010, DOI:10.3970/cmes.2010.055.319

    Abstract In this paper we study numerically the mechanical behaviour of wire ropes, particularly the influence of the geometrical configuration on the overall stiffness of the cables. Modelling the behaviour of a cable is a difficult problem, given the complexity of the geometrical layout, contact phenomena occurring among wires and possible yielding of the material. For this reason we pursue a "hierarchical beam approach", to substitute recursively, at each cabling stage, the bundle of wires with an equivalent single strand, having the characteristics computed from the previous level. We consider the first two levels of the bundle hierarchy and investigate the… More >

  • Open Access

    ARTICLE

    Estimation of thermo-elasto-plastic properties of thin-film mechanical properties using MD nanoindentation simulations and an inverse FEM/ANN computational scheme

    D. S. Liu1, C.Y. Tsai1

    CMES-Computer Modeling in Engineering & Sciences, Vol.39, No.1, pp. 29-48, 2009, DOI:10.3970/cmes.2009.039.029

    Abstract Utilizing a thin copper substrate for illustration purposes, this study presents a novel numerical method for extracting the thermo-mechanical properties of a thin-film. In the proposed approach, molecular dynamics (MD) simulations are performed to establish the load-displacement response of a thin copper substrate nanoindented at temperatures ranging from 300~1400 K. The load data are then input to an artificial neural network (ANN), trained using a finite element model (FEM), in order to extract the material constants of the copper substrate. The material constants are then used to construct the corresponding stress-strain curve, from which the elastic modulus and the plastic… More >

  • Open Access

    ARTICLE

    Evaluation of Seismic Design Values in the Taiwan Building Code by Using Artificial Neural Network

    Tienfuan Kerh1,2, J.S. Lai1, D. Gunaratnam2, R. Saunders2

    CMES-Computer Modeling in Engineering & Sciences, Vol.26, No.1, pp. 1-12, 2008, DOI:10.3970/cmes.2008.026.001

    Abstract Taiwan frequently suffers from strong ground motion, and the current building code is essentially based on two seismic zones, A and B. The design value of horizontal acceleration for zone A is 0.33g, and the value for zone B is 0.23g. To check the suitability of these values, a series of actual earthquake records are considered for evaluating peak ground acceleration (PGA) for each of the zones by using neural network models. The input parameters are magnitude, epicenter distance, and focal depth for each of the checking stations, and the peak ground acceleration is calculated as the output with the… More >

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