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RISS 인기검색어
- 검색키워드
- 저자 : Rupali Satavalekar (검색결과 2 건)
- 무료
- 기관 내 무료
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Numerical modelling of contaminant transport using FEM and meshfree method
Satavalekar, Rupali S.,Sawant, Vishwas A. Techno-Press 2014 Advances in environmental research Vol.3 No.2
Groundwater contamination is seeking a lot of attention due to constant degradation of water by landfills and waste lagoons. In many cases heterogeneous soil system is encountered and hence, a finite element model is developed to solve the advection-dispersion equation for layered soil system as FEM is a robust tool for modelling problems of heterogeneity and complex geometries. Recently developed Meshfree methods have advantage of eliminating the mesh and construct approximate solutions and are observed that they perform effectively as compared to conventional FEM. In the present study, both FEM and Meshfree method are used to simulate phenomenon of contaminant transport in one dimension. The results obtained are agreeing with the values in literature and hence the model is further used for predicting the transport of contaminants. Parametric study is done by changing the dispersion coefficient, average velocity, geochemical reactions, height of leachate and height of liner for obtaining suitability.
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Application of Adaptive Neuro-Fuzzy Inference System for Evaluating Compressive Strength of Concrete
Deepak Kumar Sinha,Rupali Satavalekar,Senthil Kasilingam 한국지능시스템학회 2021 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.21 No.2
The objectives of this study are to develop a model for predicting the compressive strength of concrete using an adaptive neuro-fuzzy inference system (ANFIS) and validate the mix proportion using artificial neural networks (ANNs) and by experimentation. A model was developed, and the compressive strength was predicted using the ANFIS (with the subtractive clustering method of the fuzzy inference system) by MATLAB programming. In the present study, two ANFIS models were considered: ANFIS models-1 and -2. ANFIS model-1 was developed to predict the 3-day compressive strength, whereas ANFIS model-2 predicts the 28-day compressive strength by considering the 3-day compressive strength data obtained using ANFIS model-1. It was observed that the errors in the 3- and 28-day compressive strengths were 6.33%, and 17.07%, respectively. Furthermore, experiments were performed for selective mixes-M40, M50, and M60-to verify the compressive strength obtained using the ANFIS model. The model results were verified against the experimental ones based on the mixes selected from the model, and the results were found to agree with the predicted ones, with a maximum deviation of 18%. Furthermore, an ANN model was developed to predict the compressive strength to verify the accuracy of the ANFIS model. The results predicted by the ANFIS and the ANN were compared with the original results available in the literature. A significant deviation was found between the ANN model results and the original results, however, the ANN model results presented the same trend as the original results. It was concluded that the ANFIS model results were highly consistent with the original results.
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