3D finite element modeling of fiber-reinforced pervious concrete with novel artificial intelligence supported image processing method

Abstract

Pervious concrete is still being developed for stormwater management and flood risk reduction through experimental research and numerical modeling techniques. Due to its unique void structure, standard finite element solid modeling techniques need to be improved to represent this type of concrete, which requires special modeling techniques. This study presents a novel artificial intelligence-supported image processing method to model pervious concrete with varying fiber types and proportions realistically. The artificial intelligence-supported system initially reads the image sections obtained from the pervious concrete samples and processes these images. Afterward, many realistic three-dimensional (3D) finite element (FE) models were obtained, considering the void structure of the pervious concrete and the fiber types it contains with the developed coding. Laboratory tests were performed on specimens to evaluate the compressive and flexural strengths and to verify model adequacy. In addition, a constant-level permeability test was conducted to determine the permeability of the concrete. The accuracy of the models was confirmed by comparing the experimental results with 3D finite element analyses. The validation of this novel realistic modeling technique with laboratory experiments demonstrated that successful outcomes can be obtained using this method, particularly for other types of concrete with void or fiber-reinforced concretes that are complex to model.