Name: JANAINA SILVA HASTENREITER KÜSTER
Type: MSc dissertation
Publication date: 07/10/2022
Advisor:
Name |
Role |
|---|---|
| KÁTIA VANESSA BICALHO | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| KÁTIA VANESSA BICALHO | Advisor * |
| MARISTELA GOMES DA SILVA | Internal Examiner * |
| WILIAN HIROSHI HISATUGU | External Examiner * |
Summary: This research analyzes and compares values of liquidity limits (LL) obtained by the
Casagrande percussion method, LLc (hard and soft base apparatus), and by the British and
Swedish cones, LLp, for different fine soils and a wide range of values. of LL. For the LLp
estimates, regressions (linear and non-linear) were used between LLc and LLp and feedforward
artificial neural networks (ANNs) (FNN) trained using the multilayer perceptron
backpropagation (MLP) algorithm, with one or two hidden layers. Experimental values of LLc
and LLp previously compiled from the literature (507 samples) were selected for statistical
analysis and ANNs. The experimental results were divided into groups according to the base
hardness of the Casagrande appliance and the type of cone used, and divided into subgroups
to assess the influence of the LL interval. ANNs were trained with LLc, LLp, IP and SUCS
classification of soils as input parameters and compared with networks of input parameters LLc
and LLp (24 networks for each dataset and obtained as LLp output). Through the statistical
analysis was possible to make the selection and treatment of data and eliminate outliers. Data
from each group and its subgroups were submitted to regression analysis to establish linear
and non-linear correlations, and to obtain the coefficient of determination (R²). Linear
correlations were submitted to residual analysis and hypothesis tests to verify the normality of
the model and the independence of the variables. The normality of the models was verified by
the graphic analysis of the residual frequency histograms and the Normal Probability plot and
by the Kolmogorov-Smirnov (KS), Shapiro-Wilk (SW) and Durbin-Watson (DW) tests. Linear
and nonlinear correlations and ANNs were compared using statistical techniques that include
the results obtained for the root mean square error (RMSE), mean absolute error (MAE),
coefficient of determination (R²), minimum and maximum values, mean and standard deviation
() of LLp estimates. Data analysis indicates that the proposed models result in very close
values for the LLp prediction. Statistical tests showed that the linear correlations obtained in
this research, despite the high correlation coefficients (R²>0,74), were not signifficant. The
ANN results show that in addition to the variability of the geotechnical properties of the
experimental results that make up the data sets, the number of samples used in the LLp
prediction also influences the results. The trained ANNs have potential application for LLp
estimates and represent an additional tool for conventional empirical regression methods.
Keywords: Liquidity Limit, Plasticity, Cone, Casagrande, Regression Correlations, Artificial
Neural Networks.
