Abstract

MOLLER-ACUNA, Patricia-Andrea  and  PINEDA-NALLI, Patricio-Andrés. Artificial Intelligence Applied to the Backward Seismic Analysis Method. Rev. Cient. [online]. 2022, n.45, pp.369-377.  Epub July 07, 2022. ISSN 0124-2253.  https://doi.org/10.14483/23448350.18556.

This work presents applications of the Backward Seismic Analysis (BSA) method for steel storage tanks using a data base of more than 382 steel storage tanks in operation during large subductive earthquakes: Valdivia 1960, Central Chile 1985, Tocopilla 2007, El Maule 2010, Alaska 1964, and others in the United States between 1933 and 1995 (subductive and cortical). It has been recorded that most of the steel storage tanks without anchor systems have failed during large earthquakes. These have been designed with the standards API 650-E, AWWA-D100, and NZSEE, which propose similar procedures for estimating seismic forces, but with different design methods. During different conferences, the causes of the failures were evaluated, concluding that the tanks were designed mainly with the API 650-E code and were unanchored. Moreover, the design codes employed do not consider relevant aspects that condition the seismic response of steel storage tanks. This work develops a prediction model based on the historical information already described, which is capable of efficiently predicting if a steel storage tank will suffer any failures during an earthquake. Various algorithms were evaluated, finding that the Random Forest method exhibits the best results. The results obtained in the prediction of steel storage tank failures reach more than 90% efficiency in most of the evaluated scenarios.

Keywords : anchored; Backward Seismic Analysis; machine learning; Random Forest; subduction; tank..

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