A. Ramach,ra Murthy^1,2, 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 is found that the post… More >

Kyong Pil Jang¹, Je kuk Son², Seung Hee Kwon^1,3

CMC-Computers, Materials & Continua, Vol.38, No.3, pp. 155-173, 2013, DOI:10.3970/cmc.2013.038.155

Abstract In a precast shell pier cap, cracking at the interface between the precast shell and the cast-in-place concrete may happen due to differences between the drying shrinkage of the inner and the outer concrete. The objective of this study is to establish a prediction method for interfacial cracking that will consider the real mechanism of differential drying shrinkage and creep. The main parameters used in the analysis were determined from experiments for a concrete mix that is applied to the manufacturing of pier caps. The variation of internal relative humidity over time was first calculated based on the nonlinear moisture… More >

E. Ferretti¹, A. Di Leo¹

CMC-Computers, Materials & Continua, Vol.7, No.2, pp. 59-80, 2008, DOI:10.3970/cmc.2008.007.059

Abstract The main assumption on the basis of the identifying model of the effective law, developed by the Author, is the impossibility of considering the specimen as a continuum, when an identifying procedure from load-displacement to stress-strain in uniaxial compression is attempted. Actually, a failure mechanism with propagation of a macro-crack was found to activate from the very beginning of the uniaxial compression test forth. This leads to considering the acquired displacements as composed by two quotes: one constitutive, due to the material strain, and one of crack opening. Since the ratio between these two quotes is not constant during the… More >

Fenghua Yuan¹, Qing Zhang^1,*, Xiaozhou Xia¹

CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 777-793, 2019, DOI:10.32604/cmc.2019.04182

Abstract Reinforcement corrosion directly affects the mechanical behavior of reinforced concrete structures. An electric corrosion test was conducted on a reinforced concrete test specimen, and a finite element model of the reinforcement corrosion damage was established. In addition, the damage behavior of reinforced concrete under different corrosion sediment distribution characteristics and different corrosion rates was studied. It was noted that when corrosion sediments are in a “semiellipse+semicircle” distribution, the results of numerical calculation are consistent with those obtained experimentally, reflecting the damage characteristics of reinforced concrete test specimens. Further, the results showed that the distribution characteristics of corrosion sediments greatly influence… 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 >

Subashini I^{1, a}, Smitha Gopinath^{2, *}, Aahrthy R^{3, b}

CMC-Computers, Materials & Continua, Vol.53, No.4, pp. 291-306, 2017, DOI:10.3970/cmc.2017.053.291

Abstract Present paper proposes a methodology by combining finite element method with smoothed particle hydrodynamics to simulate the response of textile reinforced concrete (TRC) slabs under low velocity impact loading. For the constitutive modelling in the finite element method, the concrete damaged plasticity model was employed to the cementitious binder of TRC and Von-Mises criterion was used for the textile reinforcement. Strain dependent smoothed particle hydrodynamics (SPH) was used to assess the damage and failure pattern of TRC slabs. Numerical simulation was carried out on TRC slabs with two different volume fraction of glass textile reinforcement to predict the energy absorption… 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 >

Smitha Gopinath^1,2, S. Meenu³, A. Ramach,ra Murthy¹

CMC-Computers, Materials & Continua, Vol.51, No.2, pp. 81-103, 2016, DOI:10.3970/cmc.2016.051.081

Abstract This paper presents experimental and analytical investigations on concrete beams reinforced with basalt fiber reinforced polymer (BFRP) and steel fibers without stirrups. Independent behaviour of BFRP reinforced beams and steel fiber reinforced beams were evaluated and the effect of combining BFRP bars and steel fiber was investigated in detail. It is found that combining steel fibers with BFRP could change the shear failure of BFRP reinforced beam to flexural failure. Further, the existing analytical models were reviewed and compared to predict the shear strength of both FRP reinforced and steel fiber reinforced beams. Based on the review, the appropriate model… 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 >

Vishal Shreyans Shah¹, Henyl Rakesh Shah², Pijush Samui³, A. Ramachra Murthy⁴

CMC-Computers, Materials & Continua, Vol.44, No.2, pp. 73-84, 2014, DOI:10.3970/cmc.2014.044.073

Abstract This paper deals with the development of models for prediction of facture parameters, namely, fracture energy and ultimate load of high strength and ultra high strength concrete based on Minimax Probability Machine Regression (MPMR) and Extreme Learning Machine (ELM). MPMR is developed based on Minimax Probability Machine Classification (MPMC). ELM is the modified version of Single Hidden Layer Feed Foreword Network (SLFN). MPMR and ELM has been used as regression techniques. Mathematical models have been developed in the form of relation between several input variables such as beam dimensions, water cement ratio, compressive strength, split tensile strength, notch depth, and… More >