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...

Full description

Bibliographic Details

Published in:	Computer-aided civil and infrastructure engineering Vol. 39; no. 5; pp. 656 - 678
Main Authors:	Pan, Yue, Qin, Jianjun, Zhang, Limao, Pan, Weiqiang, Chen, Jin‐Jian
Format:	Journal Article
Language:	English
Published:	Hoboken Wiley Subscription Services, Inc 01.03.2024
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!