Long-short-term memory model for quantitative analysis of corrosion products in concrete

A quantitative analysis model is proposed to describe the content evolution of corrosion products in concrete structures subjected to sulfate or chloride attack. The model is inspired by the idea of the Long-Short-Term Memory (LSTM) algorithm in the machine learning field. For establishing the relationship between engineering data and theoretical computations, the training samples of the model are generated by solving the rate equations of the sulfate corrosion reactions in concrete with wide-range initial conditions and validated by the existing experimental data. The Pearson correlation coefficient is employed to determine the model features. The model performance is comprehensively evaluated and discussed. The results show that the proposed model has enough accuracy and feasibility. It could effectively predict the contents of the corrosion products during the sulfate attack by several input values rather than the initial contents of all chemical constituents. The proposed model builds a bridge between experimental methods and theoretical predictions, adequately inheriting their advantages.