Estimation of standard penetration test value on cohesive soil using artificial neural network without data normalization

Abstract

Artificial neural networks (ANNs) are often used recently by researchers to solve complex and nonlinear problems. Standard penetration test (SPT) and cone penetration test (CPT) are field tests that are often used to obtain soil parameters. There have been many previous studies that examined the value obtained through the SPT test with the CPT test, but the research carried out still uses equations that are linear. This research will conduct an estimated value of SPT on cohesive soil using CPT data in the form of end resistance and blanket resistance, and laboratory test data such as effective overburden pressure, liquid limit, plastic limit and percentage of sand, silt and clay. This study used 242 data with testing areas in several cities on the island of Sumatra, Indonesia. The developed artificial neural network will be created without data normalization. The final results of this study are in the form of root mean square error (RMSE) values 3.441, mean absolute error (MAE) 2.318 and R2 0.9451 for training data and RMSE 2.785, MAE 2.085, R2 0.9792 for test data. The RMSE, MAE and R2 values in this study indicate that the ANN that has been developed is considered quite good and efficient in estimating the SPT value.

Analyzing Geotechnical Characteristics of Soils in Erbil via GIS and ANNs

“…Ref. [20] presents the estimation of standard penetration test values on cohesive soil using an artificial neural network without data normalization. Some previous studies investigated the assessment of geotechnical properties and determination of shear strength parameters by unitized ANN [21][22][23].…”

Density, load and fly ash effect on stabilization of high plasticity soil with lime

“…One-way cyclic loading did not cause sample degradation, considerable degradation occurred when the sample was subjected to two-way cyclic loading (Lo & Wardani, 1999). Soil strength and bearing capacity of the soil depend on the shear strength of the soil and are influenced by the physical and mechanical properties of the soil such as unit weight, plasticity, and grain type (Fernando et al, 2021;Nugroho et al, 2022;Nugroho and Fernando, et al 2021) One-dimensional consolidation test (1-D consolidation), can be used to study various soil characteristics (Nakai et al, 2012). The increase in the bearing capacity of the fly ash stabilized soil (Ozdemir, 2016) and the improvement in the expansion and shrinkage properties of the expansive soil (Mohanty, 2015) can also be studied from the 1-D consolidation test.…”