ANN Model for Response Correction of Structural Components Subjected to Near-Field Explosions Based on Single Degree of Freedom Analysis

Deriving the response of structures subjected to blast loads is essential for protecting human lives and ensuring structural safety. Structural safety can be assessed through blast-resistant analysis, and the behavior of blast-resistant structures can be derived using a single-degree-of-freedom numerical analysis method based on reasonable assumptions. Generally, blast loads are treated as uniformly distributed in such analyses; however, in the case of close-in explosions, where the blast source is near the structure, the blast pressure does not act uniformly on the structure. In this study, a single-degree-of-freedom numerical analysis response database considering the effects of close-in explosions was established, and a close-in explosion response correction artificial neural network (ANN) model was developed and validated to adjust the responses derived from the single-degree-of-freedom analysis method, assuming uniformly distributed loads, for the specific effects of close-in explosions.