Liquid storage tanks have a pivotal role in supplying of different type of liquids. In addition, they are widely used in disparate industrial plants. Failure of these structures has dire consequences and detrimental impacts on economy of countries. Hence, in this study, the seismic behavior of these structures with an emphasis on their seismic vulnerability under different conditions is analyzed. For this purpose, an oil industry plant in the south of Iran is considered and five different tanks with different dimension are chosen. Three significant vulnerability parameters which are usually taken into account for evaluation of tanks are investigated. These criterions include: 1) Sliding, 2) Elasto-Plastic buckling (Elephant foot buckling), 3) Tank roof damage. Four integral analyses including static, modal, response spectrum and time history is utilized. In time history analyses, multiple records are selected to conduct a comprehensive evaluation of system. Finally, the results show that tanks are safe under Elephant foot buckling fragility parameter. However, this is not always the case for tank's roof vulnerability and sliding. In addition to this, tanks with higher diameter have witnessed higher wave height and are possible to be in danger for tank's roof vulnerability, while these tanks have shown an appropriate behavior against sliding.

No abstract

CiteULike uses cookies, some of which may already have been set. Read about how we use cookies. We will interpret your continued use of this site as your acceptance of our use of cookies. You may hide this x CiteULike: Using generalized regression neural network (GRNN) for ...

In this project, 6 Reinforced Concrete (RC) slabs with various length and thickness of carbon fiber reinforced polymer (CFRP) in comparison with the plain RC slab have been used to generate Artificial Neural Networks (ANNs) for structural behavior prediction. The slab dimension was 1800 × 400 × 120 mm and the length of the CFRP was 700, 1100 and 1500 mm in two different cross section area of 60 and 96 mm 2. The results of this experimental work are noted in each testing process. The general regression neural network (GRNN) was the first practical approach that has applied for structural analysis prediction. The feed forward back-propagation (FFB) was the second method with a per regression method for data collection to increase the number of data for training, verifying and testing. The two used method had minimal error and maximum correlation coefficient. The amounts of MSE and RMSE in GRNN and FFB system were in the acceptable ranges. The correlation coefficient is closed to 1 for output data.