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

During a deep excavation project, monitoring the structural health of the adjacent buildings is crucial to ensure safety. Therefore, this study proposes a novel probabilistic deep reinforcement learning (PDRL) framework to optimize the monitoring plan to minimize the cost and excavation‐induced risk...

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Vydané v:	Computer-aided civil and infrastructure engineering Ročník 39; číslo 5; s. 656 - 678
Hlavní autori:	Pan, Yue, Qin, Jianjun, Zhang, Limao, Pan, Weiqiang, Chen, Jin‐Jian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken Wiley Subscription Services, Inc 01.03.2024
Predmet:	Buildings Deep learning Excavation Foundation settlement General regression neural networks Optimization Probabilistic models Safety Statistical analysis Structural health monitoring Uncertainty
ISSN:	1093-9687, 1467-8667
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