FORECAST OF COMPRESSION INDEX OF FINE GRAINED SOIL BY UTILIZING ADAPTIVE NEURO FUZZY INFERENCE SYSTE

Page 1

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:02/Issue:09/September-2020

Impact Factor- 5.354

www.irjmets.com

FORECAST OF COMPRESSION INDEX OF FINE GRAINED SOIL BY UTILIZING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM Mr. CH. Naga bharath*1, A. Adiseshu*2, B. Sai kumar*3, M. Anil kumar*4, A.V. Anil babu*5, B.V. Chandra Sekhar*6 *1M.Tech,

Assistant Professor, Civil Engineering, Gudlavalleru Engineering College, Gudlavalleru, India.

*2,3,4,5,6Student,

Civil Engineering, Gudlavalleru Engineering College, Gudlavalleru, India.

ABSTRACT The compression index is one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined via carrying out lab oedometer test on undisturbed examples. Nonetheless, the test is very tedious and expensive. Subsequently, many exact recipes dependent on regression analysis have been presented to gauge the compressibility parameters using soil index properties. We are making an endeavor on Adaptive Neuro Fuzzy Inference System (ANFIS) model for prediction of compressibility parameters To manage the difficulties involved, in this task an endeavor has been made to show property of soil for example Compression index parameter in terms of Depth of foundation (Df), liquid limit (WL), and N value, Fine fraction (ff). The Neuro Fuzzy back propagation network is constructed to show the Compression index. For all the models chosen, Depth of foundation, liquid limit, N value and Fine fraction are taken as input parameters and compression index of soil as yield parameter from essential soil properties. For this reasons 1226 soils test information was gathered from the consultancy cell of Geotechnical Designing Lab, the test outcomes relating a wide scope of soil properties. Among the all out 979 datasets are utilized for training, 183 datasets for checking and staying 62 datasets for testing. Distinctive fluffy models were attempted with Df, N,WL, ff as input boundaries and Cc as output boundary. The architectures developed by varying membership functions for four input parameters are [5 4 4 5] (Gbellmf), [6 4 3 4] (Trapmf), [4 4 3 4] (Gbellmf), [3 3 3 4] (Gbellmf , trapmf), [3 3 4 4] (Gbellmf, trapmf). The best Neuro Fuzzy model is identified by analyzing the performance of different models developed. Adaptive Neuro Fuzzy Inference System model with [3 3 4 4] (gbellmf) architecture was found to be quite reasonable in predicting desired output Pictorial presentation of results gives a better idea than quantitative assessment. In this line another attempt has been made for checking the predicted values of compression index of soils with the observed values graphically. A graph is plotted between the predicted values and observed values of output for training and testing process which shows predicted values are close to observed values for all output parameters. Keywords: Adaptive Neuro Fuzzy Inference System, Compression index, Depth of foundation, Liquid limit, N, Fine fraction, Gbellmf, Trapmf.

I.

INTRODUCTION

Compression index of a dirt is the most unpredictable property to understand considering the large number of elements known to influence it. A great deal of maturity and ability might be required with respect to the specialist in deciphering the consequences of the laboratory tests for application to the conditions in the field. So as to adapt to the above complexities, customary types of building displaying approaches are reasonably streamlined. An elective methodology, which has given some guarantee in the field of geotechnical building, is Adaptive Neuro Fuzzy Inference Framework (ANFIS).In these examination Compression index (cc) for soils are predicted utilizing Adaptive Neuro Fuzzy Inference Framework (ANFIS). ANFIS model is created utilizing NN device in MATLAB programming (7.5.0). In the paper an endeavor has been made to demonstrate Compression index as far as Profundity of establishment (Df) Fine Division (FF), Fluid Cutoff (WL), and N esteem. An Adaptive Neuro Fuzzy

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[493]


Turn static files into dynamic content formats.

Create a flipbook
Issuu converts static files into: digital portfolios, online yearbooks, online catalogs, digital photo albums and more. Sign up and create your flipbook.