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  • Open Access


    A Rigid-fiber-based Boundary Element Model for Strength Simulation of Carbon Nanotube Reinforced Composites

    H. T. Wang1, Z. H. Yao2

    CMES-Computer Modeling in Engineering & Sciences, Vol.29, No.1, pp. 1-14, 2008, DOI:10.3970/cmes.2008.029.001

    Abstract Carbon nanotubes (CNTs) may provide ultimate enhancement in stiffness and strength for composite materials. This paper presents a rigid-fiber-based boundary integral equation formulation for the numerical simulation of debonding process and the corresponding strength of CNT reinforced composites. The CNT/matrix interfaces are assumed to fail when the interfacial shear force reaches a prescribed threshold, and the CNTs and matrix are considered to be detached in the failed areas. The matrix with one or several tens of originally well-bonded CNTs is subjected to an incremental tensile load and the effective stress-strain relations are readily obtained by… More >

  • Open Access


    Atomistic Measures of Materials Strength

    Ju Li1, Sidney Yip1

    CMES-Computer Modeling in Engineering & Sciences, Vol.3, No.2, pp. 219-228, 2002, DOI:10.3970/cmes.2002.003.219

    Abstract We examine the role of atomistic simulations in multiscale modeling of mechanical behavior of stressed solids. Theoretical strength is defined through modes of structural instability which, in the long wavelength limit, are specified by criteria involving elastic stiffness coefficients and the applied stress; more generally, strength can be characterized by the onset of soft vibrational modes in the deformed lattice. Alternatively, MD simulation of stress-strain response provides a direct measure of the effects of small-scale microstructure on strength, as illustrated by results on SiC in single crystal, amorphous, and nanocrystalline phases. A Hall-Petch type scaling More >

  • Open Access


    Strength Evaluation of Electronic Plastic Packages Using Stress Intensity Factors of V-Notch

    Toru Ikeda1, Isao Arase, Yuya Ueno, Noriyuki Miyazaki

    CMES-Computer Modeling in Engineering & Sciences, Vol.1, No.1, pp. 91-98, 2000, DOI:10.3970/cmes.2000.001.091

    Abstract In electronic devices, the corners of joined dissimilar materials exist between plastic resin and a die pad or a chip. Failure of the plastic resin is often caused from these corners during the assembly process or the operation of products. The strength evaluation of the corner is important to protect the failure of plastic packages. To evaluate the singular stress field around a corner, we utilize the stress intensity factors of the asymptotic solution for a corner of joined dissimilar materials. We show that the accurate stress intensity factor can be analyzed by the displacement More >

  • Open Access


    A Study on the Estimation of Prefabricated Glass Fiber Reinforced Concrete Panel Strength Values with an Artificial Neural Network Model

    S.A. Yıldızel1,2, A.U. Öztürk1

    CMC-Computers, Materials & Continua, Vol.52, No.1, pp. 41-52, 2016, DOI:10.3970/cmc.2016.052.041

    Abstract In this study, artificial neural networks trained with swarm based artificial bee colony optimization algorithm was implemented for prediction of the modulus of rapture values of the fabricated glass fiber reinforced concrete panels. For the application of the ANN models, 143 different four-point bending test results of glass fiber reinforced concrete mixes with the varied parameters of temperature, fiber content and slump values were introduced the artificial bee colony optimization and conventional back propagation algorithms. Training and the testing results of the corresponding models showed that artificial neural networks trained with the artificial bee colony More >

  • Open Access


    Structural Performance of Precast and Cast-in-situ Ultra High Strength Concrete Sandwich Panel

    A. Ramach,ra Murthy1,2, V. Ramesh Kumar1, Smitha Gopinath1, PrabhatRanjan Prem1, Nagesh R. Iyer3, Reshmi Balakrishnan4

    CMC-Computers, Materials & Continua, Vol.44, No.1, pp. 59-72, 2014, DOI:10.3970/cmc.2014.044.059

    Abstract This paper investigates the flexural performance of a sandwich panel made up of ultra high strength concrete (UHSC) as top and bottom skin and cold formed steel as sandwich. A novel sandwich panel has been designed such a way that bottom skin of UHSC is of precast in nature and top skin of UHSC is cast-insitu and cold formed steel (profiled sheet) as sandwich. The connection between top skin of UHSC and cold formed steel is made with self tapping screws. Flexural performance of UHSC sandwich panel has been tested under flexural loading and it… More >

  • Open Access


    Identification of Materials Properties with the Help of Miniature Shear Punch Test Using Finite Element Method and Neural Networks

    Asif Husain1, M. Guniganti2, D. K. Sehgal2, R. K. Pandey2

    CMC-Computers, Materials & Continua, Vol.8, No.3, pp. 133-150, 2008, DOI:10.3970/cmc.2008.008.133

    Abstract This paper describes an approach to identify the mechanical properties i.e. fracture and yield strength of steels. The study involves the FE simulation of shear punch test for various miniature specimens thickness ranging from 0.20mm to 0.80mm for four different steels using ABAQUS code. The experimental method of the miniature shear punch test is used to determine the material response under quasi-static loading. The load vs. displacement curves obtained from the FE simulation miniature disk specimens are compared with the experimental data obtained and found in good agreement. The resulting data from the load vs.… More >

  • Open Access


    Leveraging Logical Anchor into Topology Optimization for Indoor Wireless Fingerprinting

    Lin Wang1,*, Huixiang Liu1, Wenyuan Liu1,2, Nan Jing1, Ahmad Adnan1, Chenshu Wu3

    CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 437-449, 2019, DOI:10.32604/cmc.2019.03814

    Abstract The indoor subarea localization has wide application space in dynamic hot zone identification, indoor layout optimization, store dynamic pricing and crowd flow trend prediction. The ubiquitous mobile devices provide the opportunity for wireless fingerprinting-based indoor localization services. However, there are two short board where the existing methods have been criticized. One is that a tagging approach requires a large number of professional surveys for wireless fingerprint construction, which weakens the scalability of the methods. The other is that the crowdsourcing-based methods encounter the cold boot problem in the system initial stage. To address these issues,… More >

  • Open Access


    Localization Algorithm of Indoor Wi-Fi Access Points Based on Signal Strength Relative Relationship and Region Division

    Wenyan Liu1, Xiangyang Luo1,*, Yimin Liu1, Jianqiang Liu2, Minghao Liu1, Yun Q. Shi3

    CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 71-93, 2018, DOI:10.3970/cmc.2018.055.071

    Abstract Precise localization techniques for indoor Wi-Fi access points (APs) have important application in the security inspection. However, due to the interference of environment factors such as multipath propagation and NLOS (Non-Line-of-Sight), the existing methods for localization indoor Wi-Fi access points based on RSS ranging tend to have lower accuracy as the RSS (Received Signal Strength) is difficult to accurately measure. Therefore, the localization algorithm of indoor Wi-Fi access points based on the signal strength relative relationship and region division is proposed in this paper. The algorithm hierarchically divide the room where the target Wi-Fi AP… More >

  • Open Access


    Prediction of Compressive Strength of Various SCC Mixes Using Relevance Vector Machine

    G. Jayaprakash1, M. P. Muthuraj2,*

    CMC-Computers, Materials & Continua, Vol.54, No.1, pp. 83-102, 2018, DOI:10.3970/cmc.2018.054.083

    Abstract This paper discusses the applicability of relevance vector machine (RVM) based regression to predict the compressive strength of various self compacting concrete (SCC) mixes. Compressive strength data various SCC mixes has been consolidated by considering the effect of water cement ratio, water binder ratio and steel fibres. Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification and regression. RVM is based on a Bayesian formulation of a More >

  • Open Access


    Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

    Susom Dutta1, A. Ramach,ra Murthy2, Dookie Kim3, Pijush Samui4

    CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 157-174, 2017, DOI:10.3970/cmc.2017.053.167

    Abstract In the present scenario, computational modeling has gained much importance for the prediction of the properties of concrete. This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete (SCC). Three models, namely, Extreme Learning Machine (ELM), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multi Adaptive Regression Spline (MARS) have been employed in the present study for the prediction of compressive strength of self compacting concrete. The contents of cement (c), sand (s), coarse aggregate (a), fly ash (f), water/powder (w/p) ratio and superplasticizer (sp) dosage More >

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