Research Progress in Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring-Reference-Cited by-同舟云学术

Research Progress in Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring

Published:2024-08-07 Issue:16 Volume:16 Page:2230
ISSN:2073-4441
Container-title:Water
language:en
Short-container-title:Water

Author:

Shao Chenfei¹^ORCID,Gu Hao¹,Xu Yanxin¹,Chen Huixiang²^ORCID,Qin Xiangnan³^ORCID,Yang Guang⁴

Affiliation:

1. College of Water Conservancy& Hydropower Engineering, Hohai University, Nanjing 210098, China

2. College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China

3. Division of Water Conservation and Hydropower Engineering, Zhengzhou University, Zhengzhou 450001, China

4. School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450045, China

Abstract

Ensuring the safety of hydraulic structure engineering is of paramount importance, as these infrastructures play a critical role in water management, flood control, and the provision of clean water for various human and ecological needs [...]

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

MDPI AG

Link

https://www.mdpi.com/2073-4441/16/16/2230/pdf

Reference12 articles.

1. A novel outlier detection method for monitoring data in dam engineering;Shao;Expert Syst. Appl.,2022

2. Enhancing dam safety evaluation using dam digital twins;Zhu;Struct. Infrastruct. Eng.,2023

3. Performance improvement method of support vector machine-based model monitoring dam safety;Su;Struct. Control Health Monit.,2016

4. Geophysical methods and their applications in dam safety monitoring;Adamo;J. Earth Sci. Geotech. Eng.,2020

5. Optimized prediction model for concrete dam displacement based on signal residual amendment;Wei;Appl. Math. Model.,2020