Content area
Dam monitoring tracks environmental variables (water level, temperature) and structural responses (deformation, seepage, and stress) to assess safety and performance. Structural health monitoring (SHM) refers to the systematic observation and analysis of the structural condition over time, and it is essential in maintaining the safety, functionality, and long-term performance of dams. This review examines monitoring data applications, covering structural health assessment methods, historical motivations, and key challenges. It discusses monitoring components, data acquisition processes, and sensor roles, stressing the need to integrate environmental, operational, and structural data for decision making. Key objectives include risk management, operational efficiency, safety evaluation, environmental impact assessment, and maintenance planning. Methodologies such as numerical modeling, statistical analysis, and machine learning are critically analyzed, highlighting their strengths and limitations and the demand for advanced predictive techniques. This paper also explores future trends in dam monitoring, offering insights for engineers and researchers to enhance infrastructure resilience. By synthesizing current practices and emerging innovations, this review aims to guide improvements in dam safety protocols, ensuring reliable and sustainable dam operations. The findings provide a foundation for the advancement of monitoring technologies and optimization of dam management strategies worldwide.
Details
Data acquisition;
Risk management;
Failure;
Safety;
Water levels;
Dam safety;
Statistical analysis;
Machine learning;
Monitoring;
Monitoring systems;
Statistical models;
Inspections;
Efficiency;
Risk assessment;
Strain gauges;
Structural health monitoring;
Data processing;
Design optimization;
Research methodology;
Infrastructure;
Environmental impact assessment;
Aging;
Numerical models;
Structural response;
Environmental impact;
Environmental stewardship;
Sensors;
Seepage;
Emergency communications systems;
Data collection;
Earthquakes;
Seismic engineering;
Decision making;
Safety management
; Khailah Ebrahim Yahya 2
; Liu Xingyang 3
; Liang Jiaming 3
1 College of Water Resources Science and Engineering, Yangzhou University, Yangzhou 225009, China; [email protected] (X.L.); [email protected] (J.L.), Intelligent Water Conservancy Research Institute, Nanjing Jurise Engineering Technology, Nanjing 211899, China
2 College of Water Resources Science and Engineering, Yangzhou University, Yangzhou 225009, China; [email protected] (X.L.); [email protected] (J.L.), Civil Engineering Department, College of Engineering, Thamar University, Dhamar 504408, Yemen
3 College of Water Resources Science and Engineering, Yangzhou University, Yangzhou 225009, China; [email protected] (X.L.); [email protected] (J.L.)