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A probabilistic deep reinforcement learning approach for optimal monitoring of a building adjacent to deep excavation

Auteur(s): (State Key Laboratory of Ocean Engineering Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure School of Naval Architecture, Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai China)
(State Key Laboratory of Ocean Engineering Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure School of Naval Architecture, Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai China)
(School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan Hubei China)
(Shanghai Tunnel Engineering Co., Ltd. Shanghai China)
(State Key Laboratory of Ocean Engineering Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure School of Naval Architecture, Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai China)
Médium: article de revue
Langue(s): anglais
Publié dans: Computer-Aided Civil and Infrastructure Engineering, , n. 5, v. 39
DOI: 10.1111/mice.13021
Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1111/mice.13021.
  • Informations
    sur cette fiche
  • Reference-ID
    10725630
  • Publié(e) le:
    30.05.2023
  • Modifié(e) le:
    15.03.2024
 
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