Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
The application of reinforcement learning to NATM tunnel design
The New Austrian Tunnelling Method (NATM) tunnel design is performed by testing support classes against the geological profile. We propose to replace this manual process with reinforcement learning, a generic framework within the realm of artificial intelligence that solves control tasks. Previous studies have demonstrated this possibility, albeit with methodological simplifications. We coupled the Finite Difference Method with a Python script, used the output of the first to train the machine learning model implemented in the latter and improved the choice of the support classes. Through benchmark tests, we demonstrated that our method was capable of choosing the optimal support classes for various geological sets and showed the relation between its performance and the number of training episodes.
The application of reinforcement learning to NATM tunnel design
The New Austrian Tunnelling Method (NATM) tunnel design is performed by testing support classes against the geological profile. We propose to replace this manual process with reinforcement learning, a generic framework within the realm of artificial intelligence that solves control tasks. Previous studies have demonstrated this possibility, albeit with methodological simplifications. We coupled the Finite Difference Method with a Python script, used the output of the first to train the machine learning model implemented in the latter and improved the choice of the support classes. Through benchmark tests, we demonstrated that our method was capable of choosing the optimal support classes for various geological sets and showed the relation between its performance and the number of training episodes.
The application of reinforcement learning to NATM tunnel design
Enrico Soranzo (Autor:in) / Carlotta Guardiani (Autor:in) / Wei Wu (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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