Prediction of Compaction Parameters of Soil using

Support Vector Regression

Arif Hasnat; Md Mehedi Hasan; Md Rabiul Islam; Md Abdul Alim

Research Article

Prediction of Compaction Parameters of Soil using Support Vector Regression

Arif Hasnat*, Md Mehedi Hasan, Md Rabiul Islam and Md Abdul Alim

Department of Civil Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

Corresponding Author

Received Date: October 08, 2019; Published Date: October 24, 2019

Abstract

Compaction parameters determination in the laboratory require considerable time and effort that can be saved by using empirical correlations at the early stages. This paper introduces some efforts to predict the compaction properties of soils using the index test results. Apart from the experimental result, the result from other literature have also been considered in the analysis here. Different statistical investigation such as histogram, ACF, PACF, kernel function, correlogram and cross correlogram are used to find the relationship of liquid limit, plastic limit and plasticity index with optimum moisture content and maximum dry density. Regression analyses are done with support vector machine algorithm. The best R squared value found from the predicted equation is 0.86 for optimum moisture content and 0.91 for maximum dry density.

Keywords: Prediction; Optimum moisture content; Maximum dry density; Compaction parameters; Support vector regression

Article Details

Citation

Arif Hasnat, Md Mehedi Hasan, Md Rabiul Islam, Md Abdul Alim. Prediction of Compaction Parameters of Soil using Support Vector Regression. Cur Trends Civil & Struct Eng. 4(1): 2019. CTCSE.MS.ID.000580.