Health Monitoring of Storage Tanks Subject to Near-Field and Far-Field Earthquakes

Abstract

Corrigendum Note

Hossein Mirzaaghabeik, Rafael Holdorf Lopez, Marcos Souza Lenzi, Hamidreza Vosoughifar

Corrigendum to “Health Monitoring of Storage Tanks Subject to Near-Field and Far-Field Earthquakes” [Engineering Science & Technology, Volume 2 Issue 2 (2021) 116-130]

Engineering Science & Technology, Volume 2 Issue 2, 2021, Page 131

Structural Health Monitoring (SHM) is a method to conserve the structures and monitor their stress and strain situation. Natural disasters, significantly earthquake could damage the water supply systems, including water tanks. The earthquake could conclude cavitation and water sloshing inside the underground water tank. On the other hand, it can cause human tragedy economically, socially, and ecologically. Therefore, useful and essential measures for repairing and utilizing the underground water tanks after the earthquake should be considered. This research aims to monitor the underground storage tanks subject to near-field and far-field earthquakes, considering the cavitation effect. In this article, the effect of earthquakes on the underground water tanks, considering the seismic behavior and cavitation effect of the underground tank, will be considered. For considering seismic behavior on the storage tanks and their reaction, the ANSYS software has been used to simulate and model them via the finite element method. After that, the prone places to the cavitation wherever the pressures are minus will be detected by the Monte Carlo method. The cavitation effect statistics were examined, and their placement is compared with the results obtained from the Monte Carlo method. The MATLAB codes have been used to make decisions for optimal smart sensor placement via the Monte-Carlo method. Moreover, to decrease the analysis time, the comparison method is taken into account. Finally, underground water tanks were loaded subjected to near-field and far-field earthquakes. The finite element result will be analyzed via the Monte Carlo method, and the best places for installing the smart sensors will be proposed.