Intelligent Monitoring System Based on Spatio-Temporal Data for Underground Space Infrastructure

作者：Du, Bowen; Ye, Junchen; Zhu, Hehua; Sun, Leilei^*; Du, Yanliang

来源：Engineering, 2022, 25: 194-203.

DOI：10.1016/j.eng.2022.07.016

• Structure health monitoring
• Underground space infrastructure
• Machine learning
• Spatio-temporal data

Intelligent sensing, mechanism understanding, and the deterioration forecasting based on spatio- temporal big data not only promote the safety of the infrastructure but also indicate the basic theory and key technology for the infrastructure construction to turn to intelligentization. The advancement of underground space utilization has led to the development of three characteristics (deep, big, and clus-tered) that help shape a tridimensional urban layout. However, compared to buildings and bridges over-ground, the diseases and degradation that occur underground are more insidious and difficult to identify. Numerous challenges during the construction and service periods remain. To address this gap, this paper summarizes the existing methods and evaluates their strong points and weak points based on real-world space safety management. The key scientific issues, as well as solutions, are discussed in a unified intel-ligent monitoring system.(c) 2022 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

• 单位
  北京航空航天大学; 同济大学