Dynamic Domino Effect Assessment (D2EA) in tank farms using a machine learning-based approach

Abstract

The current work presents a Dynamic Domino Effect Assessment (D2EA) methodology for chemical storage tank farms. While the application of the proposed approach is focused on atmospheric tanks, it applies as well to pressurized tanks. It utilizes the temperature and time-dependent material strength property (yield strength) as a structural health indicator. The D2EA methodology uses random forest (RF) and feed-forward neural network (FFNN) to predict yield strength during fire exposure and applies the predicted yield strength to dynamic failure assessment. A lumped parameter model generates datasets to train the dynamic failure prediction model. Two case studies have been used to demonstrate how the method can be used. The results suggest that RF and FFNN can predict gamma distribution-aided dynamic failure probability assessment. The RF is a better tool than FFNN due to its lower computational cost and good performance. The current work will be helpful in the design and operation of tank farms – an essential and critical infrastructure.