Modeling of soil shear strength using multiple linear regression (MLR) at Penang, Malaysia

This paper presents multiple linear regression (MLR) soil shear strength models developed from electrical resistivity and seismic refraction tomography data. The MLR technique is used to estimate the value of dependent variables of soil shear strength based on the value of two independent variables, namely, resistivity and velocity. These parameters were regressed using regression statistics technique for generating MLR model. The results of MLR model, which is based on the estimation of model dependent parameters (Log(10) resistivity and Log(10) velocity), calculated forp-value, are less than 0.05 and VIF value less than 10 for cohesion and friction angle models. This result shows that there is a statistically significant relationship between cohesion and friction angle with geophysical parameters (independent variables). The estimation accuracy of the MLR models is also conducted for verification, and the result shows that RMSE value for predicted cohesion and predicted friction angle is 0.77 kN/m2 and 1.73 degrees which is close to zero. Meanwhile, MAPE value was found to be 4.57 % and 7.61 %, indicating highly accurate estimation for the MLR models of predicted cohesion and predicted friction angle. Based on the application of near surface, the study area was successfully classified into two regions, namely, medium and hard clayey sand. Thus, it is concluded that MLR method is suitable in estimating the subsurface characterization that covered more regions compared to the traditional method (laboratory test).