https://orcid.org/0000-0003-3833-3495
Abstract
In designing protective concrete structures, optimum response against projectile impact is achieved by minimizing damage and, more importantly, controlling its failure mode to ensure life safety. Recently, steel fiber–reinforced concrete (SFRC) has gained attention for its superior toughness and damage control against impact loads. However, in designing SFRC panels to protect against projectile impact, a knowledge gap exists in the selection of optimum parameters, such as steel fiber volumetric fraction, maximum aggregate size, and panel thickness. To fill this gap, this article proposes a model that is capable of predicting SFRC response to impact loading using an artificial neural network. This model has been introduced to a multiobjective genetic algorithm to optimize the SFRC panel design against a given impact energy. Simple guidelines for the selection of optimum thickness, steel fiber volumetric fraction, and aggregate size are developed.
Issue Section:
Technical Papers
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