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

Author(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)
Medium: journal article
Language(s): English
Published in: Computer-Aided Civil and Infrastructure Engineering, , n. 5, v. 39
Page(s): 656-678
DOI: 10.1111/mice.13021
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1111/mice.13021.
  • About this
    data sheet
  • Reference-ID
    10725630
  • Published on:
    30/05/2023
  • Last updated on:
    20/09/2024
 
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