A.P.S. Selvadurai¹, H. Ghiabi²

CMES-Computer Modeling in Engineering & Sciences, Vol.23, No.1, pp. 53-74, 2008, DOI:10.3970/cmes.2008.023.053

Abstract This paper deals with the problem of the consolidation of a composite consisting of alternate layers of soft clay and a granular material. A series of experiments were conducted on components to develop the constitutive models that can be implemented in a computational approach. The constitutive response of the soft clay is represented by a poro-elasto-plastic Cam clay-based model and the granular medium by an elasto-plastic model with a Drucker-Prager type failure criterion and a non-associated flow rule. The computational poro-elasto-plastic model is used to calibrate the experimental results derived from the one-dimensional tests and to establish the influence of… More >

H. T. Liu¹, Z. D. Han¹, A. M. Rajendran², S. N. Atluri³

CMC-Computers, Materials & Continua, Vol.4, No.1, pp. 43-54, 2006, DOI:10.3970/cmc.2006.004.043

Abstract The Rajendran-Grove (RG) ceramic damage model is a three-dimensional internal variable based constitutive model for ceramic materials, with the considerations of micro-crack extension and void collapse. In the present paper, the RG ceramic model is implemented into the newly developed computational framework based on the Meshless Local Petrov-Galerkin (MLPG) method, for solving high-speed impact and penetration problems. The ability of the RG model to describe the internal damage evolution and the effective material response is investigated. Several numerical examples are presented, including the rod-on-rod impact, plate-on-plate impact, and ballistic penetration. The computational results are compared with available experiments, as well… 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 >