Residual Strength of Corroded Reinforced Concrete Beams Using an Adaptive Model Based on ANN

Abstract

Estimation of the residual strength of corroded reinforced concrete beams has been studied from experimental and theoretical perspectives. The former is arduous as it involves casting beams of various sizes, which are then subjected to various degrees of corrosion damage. The latter are static; hence cannot be generalized as new coefficients need to be re-generated for new cases. This calls for dynamic models that are adaptive to new cases and offer efficient generalization capability. Computational intelligence techniques have been applied in Construction Engineering modeling problems. However, these techniques have not been adequately applied to the problem addressed in this paper. This study extends the empirical model proposed by Azad et al. (Mag Concr Res 62(6):405-414, 2010), which considered all the adverse effects of corrosion on steel. We proposed four artificial neural networks (ANN) models to predict the residual flexural strength of corroded RC beams using the same data from Azad et al. (2010). We employed two modes of prediction: through the correction factor (C f ) and through the residual strength (M res ). For each mode, we studied the effect of fixed and random data stratification on the performance of the models. The results of the ANN models were found to be in good agreement with experimental values. When compared with the results of Azad et al. (2010), the ANN model with randomized data stratification gave a C f -based prediction with up to 49 % improvement in correlation coefficient and 92 % error reduction. This confirms the reliability of ANN over the empirical models.

Reliability of Reinforced Concrete Structures Subjected to Corrosion-Fatigue and Climate Change

“…Designing and maintaining these systems for a particular service lifetime have been recognized as critical issues worldwide. RC structures are characterized by high durability; however, during their operational life, they are subjected to internal and external actions that affect performance, serviceability and safety (Imam et al 2015;Kim et al 2016;Marquez-Peñaranda et al 2016;Morga and Marano 2015;Sánchez-Silva and Klutke 2016). Nowadays, many deteriorated structures are evaluated for possible repair and continued service because their replacement would be economically unfeasible.…”

An Efficient Chloride Ingress Model for Long-Term Lifetime Assessment of Reinforced Concrete Structures Under Realistic Climate and Exposure Conditions

“…Chloride ingress is among the principal causes of deterioration of reinforced concrete (RC) structures in chloridecontaminated environments leading to important serviceability and safety reductions as well as increasing repair and maintenance costs Schoefs 2012, 2015;Imam et al 2015;Kim et al 2016). For example, about 173,000 bridges on the interstate system of the United States are structurally deficient or functionally obsolete due in part to corrosion (Bhide 2008;Pritzl et al 2014;Radlińska et al 2014).…”

Evaluation of Bond Properties of Reinforced Concrete with Corroded Reinforcement by Uniaxial Tension Testing

“…In a recent study, Imam et al (2015) used artificial neural networks (ANN) methods to handle the non-linearity in corrosion, coupled with its capability to adaptively learn from hidden patterns in experimental data. But the limited experimental results from Azad et al (2010) were used for the ANN analysis.…”