Optimal placement of smart sensors for RCC dams structural health monitoring using Monte- Carlo

Document Type : Research Paper

Authors

• Ali Shamsai ¹
• Mohsen Ghaemian ²
• Hamid Reza Vosoughifar ³

¹ Assistant Professor of the Department of Civil Engineering, West Tehran branch, Islamic Azad University, Tehran, Iran.

² Professor of the Civil Engineering Department, Sharif University of Technology, Tehran, Iran.

³ Professor of the Department of water Engineering, South of Tehran branch, Islamic Azad University, Tehran, Iran.

Abstract

Building RCC dams involves pouring and compacting concrete in small layers (30 cm), one on top of another, creating a joint between each layer. This joint could potentially become the “ideal” joint depending on the temperature and lag time between each layer. To check the concrete s temperature using smart sensor is undeniable. Furthermore we use these sensors for RCC dam’s structural health monitoring.
The objective of this paper is to introduce a novel model for arranging the smart sensors for RCC dam’s structural health monitoring .The authors suggest a flow chart and prove it with a real case study. This is useful for RCC dams all around the world.
The case study is Zirdan RCC dam (located in Iran), and the verification is upon Kinta RCC dam (raised in Malaysia). Step 1, the Zirdan dam is modeled in Abaqus . The program outputs give the actual temperature, the thermal stresses of each node. Step 2,The ANN (Artificial neural network) converts discontinuous results in a continuous estate. Step 2, we use the Monte Carlo to achieve a point in each layer, representative of others. the chosen coordinates are fed into GA (genetic algorithm). Next, we define the parameter of importance coefficient as the temperature arrived from the site divided by the model temperature. finally, assimilating it with cost function will allow for having fewer sensors and sensors being placed further apart. All of the above result in the optimum placement of the thermal sensors in RCC dams.

Keywords

• Key Words: Artificial neural network
• Genetic Algorithm
• Monte-Carlo
• Abaqus

20.1001.1.20086377.1399.13.46.1.6

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