Hyun Kyu Shin¹, Yong Han Ahn², Sang Hyo Lee³, Ha Young Kim^4,*

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 911-928, 2019, DOI:10.32604/cmc.2019.08269

Abstract Compressive strength of concrete is a significant factor to assess building structure health and safety. Therefore, various methods have been developed to evaluate the compressive strength of concrete structures. However, previous methods have several challenges in costly, time-consuming, and unsafety. To address these drawbacks, this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network (DCNN). The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy. The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.… More >

Nadezda Stevulova^1,*, Viola Hospodarova¹, Adriana Estokova¹, Eva Singovszka¹, Marian Holub¹, Stefan Demcak¹, Jaroslav Briancin², Anton Geffert³, Frantisek Kacik³, Vojtech Vaclavik⁴, Tomas Dvorsky⁴

Journal of Renewable Materials, Vol.7, No.11, pp. 1121-1145, 2019, DOI:10.32604/jrm.2019.07556

Abstract The aim of this study was to characterize two types of cellulosic fibers obtained from bleached wood pulp and unbleached recycled waste paper with different cellulose content (from 47.4 percent up to 82 percent), to compare and to analyze the potential use of the recycled fibers for building application, such as plastering mortar. Changes in the chemical composition, cellulose crystallinity and degree of polymerization of the fibers were found. The recycled fibers of lower quality showed heterogeneity in the fiber sizes (width and length), and they had greater surface roughness in comparison to high purity wood pulp samples. The high… More >

Ch,ra S. Yerramalli¹, Anthony M. Waas²

CMES-Computer Modeling in Engineering & Sciences, Vol.6, No.1, pp. 1-16, 2004, DOI:10.3970/cmes.2004.006.001

Abstract Results from a 3D finite element based study of the compression response of unidirectional fiber reinforced polymer matrix composites (FRPC) are presented in this paper. The micromechanics based study was used to simulate the compressive response of glass and carbon fiber reinforced polymer matrix composites, with a view to understanding the effect of fiber diameter on compression strength. Results from the modeling and simulation indicate the presence of a complex three dimensional stress state in the matrix of the FRPC. Results from the simulation highlight the role of fiber diameter on the compressive response of FRPC. In particular, it is… More >

M. Pankow¹, A.M. Waas², C.F. Yen³

CMC-Computers, Materials & Continua, Vol.32, No.2, pp. 81-106, 2012, DOI:10.3970/cmc.2012.032.081

Abstract The compression response of 3D woven textile composites (3DWC) that consist of glass fiber tows and a polymer matrix material is studied using a combination of experiments and finite element based analyses. A previous study reported by the authors consisted of an experimental investigation of 3DWC under high strain rate loading, Pankow, Salvi, Waas, Yen, and Ghiorse (2011). Those experimental results were explained by using the finite element method to analyze the high rate deformation response of representative volume elements (RVEs) of the 3DWC, Pankow, Waas, Yen, and Ghiorse (2012). In this paper, the same modeling strategy is used to… More >

G. Jayaprakash¹, M. P. Muthuraj^2,*

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 linear model with an appropriate… More >

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 have been taken as inputs… More >

Q.W. Yang¹, S.G. Du^1,2

CMC-Computers, Materials & Continua, Vol.47, No.3, pp. 217-236, 2015, DOI:10.3970/cmc.2015.047.217

Abstract Size effect is a major issue in concrete structures and occurs in concrete in any loading conditions. In this study, size effect on concrete cubic compressive strength is modeled with a back-propagation neural network. The main advantage in using an artificial neural network (ANN) technique is that the network is built directly from experimental data without any simplifying assumptions via the self-organizing capabilities of the neural network. The proposed ANN model is verified by using 27 experimental data sets collected from the literature. For the large specimens, a modified ANN is developed in the paper to further improve the forecast… More >