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In order to ensure the safety of LNG storage tank during operation, the optimization method of structural safety of LNG full-capacity storage tank under explosion load was studied. The finite element model for tank explosion analysis, which had taken into account the liquid sloshing in the tank, was established and the sloshing state of LNG was simulated by the impact and convection spring-mass model. The time-history analysis of the storage tank was conducted to determine the dynamic response of the storage tank structure. The M-N curve was used to analyze the safety of the storage tank, the solutions were proposed for the situations that did not meet the safety requirements, and the anti-explosion capability of LNG full-capacity tank was optimized. The results show that the structural response of LNG full-capacity tank appears as a periodic attenuation under the explosion load. The main impact area of explosion is located at the joining between the tank dome and the outer wall, and the impact on circumferential safety is greater than that on radial safety. Both reducing the peak pressure of explosion load and increasing the reinforcement of components can effectively improve the safety of full-capacity storage tank.

Measuring displacement response is essential in the field of structural health monitoring and seismic engineering. Numerical integration of the acceleration signal is a common measurement method of displacement data. However, due to the circumstances of ground tilt, low-frequency noise caused by instruments, hysteresis of the transducer, etc., it would generate a baseline drift phenomenon in acceleration integration, failing to obtain an actual displacement response. The improved traditional baseline correction methods still have some problems, such as high baseline correction error, poor adaptability, and narrow application scope. This paper proposes a deep neural network model based on empirical mode decomposition (EMD–DNN) to solve baseline correction by removing the drifting trend. The feature of multiple time sequences that EMD obtains is extracted via DNN, achieving the real displacement time history of prediction. In order to verify the effectiveness of the proposed method, two natural waves (EL centro wave, Taft wave) and one Artificial wave are selected to test in a shaking table test. Comparing the traditional methods such as the least squares method, EMD, and DNN method, EMD–DNN has the best baseline correction effect in terms of the evaluation indexes: Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and degree of fit (R-Square).