Rashed, Azadeh
(2014)
A New Prediction Model for Slope Stability Analysis, [Dissertation thesis], Alma Mater Studiorum Università di Bologna.
Dottorato di ricerca in
Ingegneria civile e ambientale, 26 Ciclo. DOI 10.6092/unibo/amsdottorato/6628.
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Abstract
The instability of river bank can result in considerable human and land losses. The Po river is the most important in Italy, characterized by main banks of significant and constantly increasing height. This study presents multilayer perceptron of artificial neural network (ANN) to construct prediction models for the stability analysis of river banks along the Po River, under various river and groundwater boundary conditions. For this aim, a number of networks of threshold logic unit are tested using different combinations of the input parameters. Factor of safety (FS), as an index of slope stability, is formulated in terms of several influencing geometrical and geotechnical parameters. In order to obtain a comprehensive geotechnical database, several cone penetration tests from the study site have been interpreted. The proposed models are developed upon stability analyses using finite element code over different representative sections of river embankments. For the validity verification, the ANN models are employed to predict the FS values of a part of the database beyond the calibration data domain. The results indicate that the proposed ANN models are effective tools for evaluating the slope stability. The ANN models notably outperform the derived multiple linear regression models.
Abstract
The instability of river bank can result in considerable human and land losses. The Po river is the most important in Italy, characterized by main banks of significant and constantly increasing height. This study presents multilayer perceptron of artificial neural network (ANN) to construct prediction models for the stability analysis of river banks along the Po River, under various river and groundwater boundary conditions. For this aim, a number of networks of threshold logic unit are tested using different combinations of the input parameters. Factor of safety (FS), as an index of slope stability, is formulated in terms of several influencing geometrical and geotechnical parameters. In order to obtain a comprehensive geotechnical database, several cone penetration tests from the study site have been interpreted. The proposed models are developed upon stability analyses using finite element code over different representative sections of river embankments. For the validity verification, the ANN models are employed to predict the FS values of a part of the database beyond the calibration data domain. The results indicate that the proposed ANN models are effective tools for evaluating the slope stability. The ANN models notably outperform the derived multiple linear regression models.
Tipologia del documento
Tesi di dottorato
Autore
Rashed, Azadeh
Supervisore
Co-supervisore
Dottorato di ricerca
Scuola di dottorato
Ingegneria civile ed architettura
Ciclo
26
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Slope Stability, River Banks, Finite Element Method, Cone Penetration test, Artificial Neural Networks, Numerical Modeling
URN:NBN
DOI
10.6092/unibo/amsdottorato/6628
Data di discussione
19 Maggio 2014
URI
Altri metadati
Tipologia del documento
Tesi di dottorato
Autore
Rashed, Azadeh
Supervisore
Co-supervisore
Dottorato di ricerca
Scuola di dottorato
Ingegneria civile ed architettura
Ciclo
26
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Slope Stability, River Banks, Finite Element Method, Cone Penetration test, Artificial Neural Networks, Numerical Modeling
URN:NBN
DOI
10.6092/unibo/amsdottorato/6628
Data di discussione
19 Maggio 2014
URI
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