Neural Network Models for the Half-Cell Potential of Reinforced Slabs with Magnesium Sacrificial Anodes Subjected to Chloride Ingress

Abstract

This study develops prediction models using Artificial Neural Network (ANN) to describe the long term performance of reinforcements in concrete slabs containing pure Magnesium anodes and subjected to chloride ingress. Ten reinforced concrete slabs of dimensions 1000 mm x 1000 mm x 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x and y axes, temperature, relative humidity (RH), and age of concrete in days were considered input parameters, while the half-cell potential (HCP) values with reference to the Standard Calomel Electrode (SCE) were considered output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model. Various learning heuristics used in supervised learning in feedforward ANN was used (viz. RB, OSS, SCG, GDA, CGP, CGF, GDX, and LM). A two-layer feed-forward network with 10 sigmoid hidden neurons and trained linear output neurons was employed in this work. The network architecture [5-10-1] and 10 neurons in the hidden layer were used for all the prediction models. Out of all the training algorithms used, the overall performance was best with LM in all stages of modelling (>96%). To conclude, the prediction of HCP values through the neural network models based on the available experimental data set was excellent.

Keywords

• half-cell potential
• neural network model
• magnesium sacrificial anodes
• chloride-induced corrosion