Prediction and optimization of the flexural behavior of corroded concrete beams using adaptive neuro fuzzy inference system

Global analysis on the number of faulty bridges, together with continuing corrosion procedure is kept on by deicing chemicals in various climates that create a necessity toward improved analytical procedures for reinforced concrete elements damaged by corrosion. Soft computing approaches could be used to simulate these statues i.e., finite element (FE) is a perfect tool for meeting this demand. Nonetheless, these assessments need a large number of inputs that, due to the extended periods of corrosion occurring, are sometimes too expensive to gather via physical testing. Here, a new statistical method as adaptive neuro fuzzy inference system (ANFIS) including data from 107 concrete members was developed to estimate these inputs. Regression models are created and analyzed which is the main novelty of the work. The resulting graphs from such ANFIS models demonstrate strong correlation that supporting the ANFIS's precision. As a result, the ANFIS is proposed as a method to define the flexural behavior of concrete members damaged by corrosion.