A Hybrid Particle Swarm Optimization with Dragonfly for Adaptive ANFIS to Model the Corrosion Rate in Concrete Structures

Abstract

The use of reinforced concrete is common in marine structures. Failure of reinforcement due to corrosion has detrimental impacts on nearly all of these structures. Hence, proposing an accurate and reliable model was imperative. The goal of this paper is to develop a new hybrid model by combining Particle Swarm Optimization (PSO) with Dragonfly Algorithm (DA) for Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the corrosion current density (C11) of marine reinforced concrete. The neuro-fuzzy-based methods have emerged as suitable techniques for encountering uncertainties associated with the corrosion phenomenon in marine structures. To the best of our knowledge, this is the first research that predicts the C11 through a model integrating fuzzy learning, neural learning rules, and meta-heuristics. 2460 data are collected from 37 regions in Persian Gulf. The input parameters are age, concrete repairing history, height above the sea level, distance from sea, concrete compressive strength, rebar diameter, concrete cover depth, concrete electrical resistivity, chloride ion concentration and pH. The proposed rules for the estimation of C11 based on collected dataset are assessed based on the several metrics such as R2, efficiency, mean absolute percentage error (MAPE), and median of absolute error (MEDAE). According to the results, ANFIS-PSO–DA enables to predict C11 by R2 (0.92), MAPE (1.67), MEDAE (0.14), and EF (0.97). The results of sensitivity analysis revealed that concrete compressive strength and pH are the most effective parameters on the corrosion current density of reinforced concrete.