Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Machine learning-based stability assessment of unlined circular tunnels under surcharge loading
https://orcid.org/http://orcid.org/0000-0001-6211-9945
https://orcid.org/http://orcid.org/0000-0002-9004-7165
https://orcid.org/http://orcid.org/0000-0002-5706-3219
https://orcid.org/http://orcid.org/0000-0001-9702-5973
This study investigates the stability of an unlined circular tunnel subjected to surcharge loading over the rock mass surface. A plane strain analysis has been performed following the Generalized Hoek–Brown (GHB) failure criterion by adaptive finite element limit analysis (AFELA). An extensive parametric study was conducted to investigate the impact of cover depth (C), diameter of the tunnel (Dt), geological strength index (GSI), GHB material constant for intact rock (mi), and unit weight (γ) on the stability of the tunnel. A dimensionless stability number (N) has been evaluated and presented that the geotechnical engineers in the field can utilize for preliminary investigation. Furthermore, the influence of the abovementioned parameters, along with the location of the tunnel, has been investigated on the development of the critical failure planes. The numerical analysis was performed using a finite element method-based computational tool, and then a machine learning-based technique, Support Vector Machine (SVM), was utilized to obtain a predictive model for stability number (N). The R2 values of the training and testing sets were found to be 0.996 and 0.949, respectively. The SVM model was validated with a tenfold cross-validation method. The sensitivity analysis of the input parameters showed that the GSI of rock mass highly influences the N; whereas the C/Dt ratio has the least influence.
Machine learning-based stability assessment of unlined circular tunnels under surcharge loading
https://orcid.org/http://orcid.org/0000-0001-6211-9945
https://orcid.org/http://orcid.org/0000-0002-9004-7165
https://orcid.org/http://orcid.org/0000-0002-5706-3219
https://orcid.org/http://orcid.org/0000-0001-9702-5973
This study investigates the stability of an unlined circular tunnel subjected to surcharge loading over the rock mass surface. A plane strain analysis has been performed following the Generalized Hoek–Brown (GHB) failure criterion by adaptive finite element limit analysis (AFELA). An extensive parametric study was conducted to investigate the impact of cover depth (C), diameter of the tunnel (Dt), geological strength index (GSI), GHB material constant for intact rock (mi), and unit weight (γ) on the stability of the tunnel. A dimensionless stability number (N) has been evaluated and presented that the geotechnical engineers in the field can utilize for preliminary investigation. Furthermore, the influence of the abovementioned parameters, along with the location of the tunnel, has been investigated on the development of the critical failure planes. The numerical analysis was performed using a finite element method-based computational tool, and then a machine learning-based technique, Support Vector Machine (SVM), was utilized to obtain a predictive model for stability number (N). The R2 values of the training and testing sets were found to be 0.996 and 0.949, respectively. The SVM model was validated with a tenfold cross-validation method. The sensitivity analysis of the input parameters showed that the GSI of rock mass highly influences the N; whereas the C/Dt ratio has the least influence.
Machine learning-based stability assessment of unlined circular tunnels under surcharge loading
https://orcid.org/http://orcid.org/0000-0001-6211-9945
https://orcid.org/http://orcid.org/0000-0002-9004-7165
https://orcid.org/http://orcid.org/0000-0002-5706-3219
https://orcid.org/http://orcid.org/0000-0001-9702-5973
This study investigates the stability of an unlined circular tunnel subjected to surcharge loading over the rock mass surface. A plane strain analysis has been performed following the Generalized Hoek–Brown (GHB) failure criterion by adaptive finite element limit analysis (AFELA). An extensive parametric study was conducted to investigate the impact of cover depth (C), diameter of the tunnel (Dt), geological strength index (GSI), GHB material constant for intact rock (mi), and unit weight (γ) on the stability of the tunnel. A dimensionless stability number (N) has been evaluated and presented that the geotechnical engineers in the field can utilize for preliminary investigation. Furthermore, the influence of the abovementioned parameters, along with the location of the tunnel, has been investigated on the development of the critical failure planes. The numerical analysis was performed using a finite element method-based computational tool, and then a machine learning-based technique, Support Vector Machine (SVM), was utilized to obtain a predictive model for stability number (N). The R2 values of the training and testing sets were found to be 0.996 and 0.949, respectively. The SVM model was validated with a tenfold cross-validation method. The sensitivity analysis of the input parameters showed that the GSI of rock mass highly influences the N; whereas the C/Dt ratio has the least influence.
Machine learning-based stability assessment of unlined circular tunnels under surcharge loading
Asian J Civ Eng
Kashyap, Rishabh (Autor:in) / Chauhan, Vinay Bhushan (Autor:in) / Kumar, Anish (Autor:in) / Jaiswal, Sagar (Autor:in)
Asian Journal of Civil Engineering ; 25 ; 2553-2566
01.04.2024
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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