Título:	Machine learning for geomembrane-sand interface analysis
Autor(es):	Tanga, Abenezer Tefera
E-mail do autor:	abenezer.tanga@aluno.unb.br
Orientador(es):	Araújo, Gregório Luís Silva
Coorientador(es):	Evangelista Junior, Francisco
Assunto:	Floresta aleatória Aprendizagem de máquina Geomembrana Ângulo de atrito
Data de publicação:	28-Abr-2023
Data de defesa:	27-Set-2022
Referência:	TANGA, Abenezer Tefera. Machine learning for geomembrane-sand interface analysis. 2022. xiii, 71 f., il. Dissertação (Mestrado em Geotecnia) — Universidade de Brasília, Brasília, 2022.
Abstract:	Evaluation of soil-geosynthetic interaction is important for analyzing the stability of the overall structure. This is because the interaction between the reinforcing geosynthetics and the reinforced soil can be a start for breakage which may cause a structural failure. Several researchers have been studying factors determining the interface shear strength in the laboratory and identified various components which affect the overall strength outcome. Machine Learning has a great potential for the analysis of parameters which are influenced by many variables. This dissertation brings a discussion about the use of Random Forest regression, which is a Machine Learning algorithm, for predicting geomembrane-sand interface friction angle. The interfaces subjected for strength parameter investigation include geomembrane and cohesionless soil, and 495 interfaces from various literature were collected. The acquired interface data is utilized for the overall statistical and Machine Learning analysis. Fourteen parameters were recorded from the referred literatures as the factors determining the interface shear strength. The fourteen parameters are from three main interface components which are; laboratory test type, geomembrane properties and soil properties. The presented data has been studied by using simple linear regression before initializing the Random Forest, to evaluate the interdependence between pairs of influencing parameters and their correlation with interface friction angle. The Pearson's correlation coefficient results are indicating the influence level between the interface components is mostly not strong. These correlation values imply the nonlinear distribution of the database and the importance of a multivariate and nonlinear algorithm for studying the referred types of interfaces. After the data analysis, an inclusive Random Forest has been initialized to predict interface friction angle. It is observed only for 3% of the training set and 6% of the testing set that the friction angle estimation has exceeded ±5° from the laboratory records. The coefficient of determination measures shows strong coherence between friction angles from laboratory studies and Random Forest estimations by resulting R2 = 0.93 and R2 = 0.92; for the training and testing sets respectively. Thus, the Random Forest has forecasted interface friction angle adequately.
Unidade Acadêmica:	Faculdade de Tecnologia (FT) Departamento de Engenharia Civil e Ambiental (FT ENC)
Informações adicionais:	Dissertação (mestrado) — Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2022.
Programa de pós-graduação:	Programa de Pós-Graduação em Geotecnia
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