Structural health monitoring of concrete dams using modern methods (Case study: Baghan Jam Dam)

Subject Areas : Analysis, design and construction of water structures

Seyed Shahab Emamzadeh ¹ , Mostafa Heidari ²

1 - Department of Civil Engineering, Faculty of Engineering and Technology, Kharazmi University, Tehran, Iran.
2 - Kangan Branch, Islamic Azad University, Bushehr, Iran.

Received: 2024-10-27 Accepted : 2025-04-29 Published : 2025-05-02

Keywords: Structural health monitoring (SHM), Concrete Dams, Non-destructive methods, Modal analysis, Smart sensors, Baghan Jam Dam,

Abstract :

This study explores modern Structural Health Monitoring (SHM) methods, focusing on concrete dams, and analyzes the application of these techniques in detecting both visible and hidden structural damages. The discussed methods include modal analysis, neural networks, pattern recognition, Kalman filtering, statistical approaches, and signal processing, each evaluated in terms of accuracy, advantages, and limitations. The results indicate that combining these methods can reduce maintenance and repair costs, provide early hazard warnings, and minimize human and financial losses. As a case study, the SHM system for the Baghan Jam Roller-Compacted Concrete (RCC) Dam was designed using the finite element method. To assess the site effect, three different soil-layer models (single-layer, two-layer, and three-layer) were examined. Sensor placement criteria were based on von Mises stress (exceeding 1.5 MPa) and displacement (exceeding 1 cm). The findings for three seismic hazard levels (high, moderate, and low) revealed that the required number of sensors varies with hazard intensity: High hazard level (PGA > 0.7): 18 sensors (8 stress sensors and 10 displacement sensors); Moderate hazard level (0.4 < PGA < 0.7): 14 sensors (6 stress sensors and 8 displacement sensors); Low hazard level (PGA < 0.4): 9 sensors (4 stress sensors and 5 displacement sensors). This research highlights the importance of continuous dam monitoring, particularly in seismic-prone regions, and demonstrates that intelligent monitoring systems can significantly enhance safety and extend the service life of critical infrastructure.

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