Numerical modelling of electrochemical deposition techniques for healing concrete damaged by alkali silica reaction

Alkali silica reaction (ASR) is a long-term factor that causes concrete cracking, and the ingress of harmful agents such as chloride can then be promoted by the ASR-induced cracks. Electro-chemical deposition method (EDM) is a promising nondestructive rehabilitation technique which has two-fold advantages of crack repair and chloride removal. In this study, the entire process from ASR-induced cracking to crack repair by EDM is studied for the first time by coupling three sub-models involving different disciplines: (1) multi-ionic transport model, (2) ASR cracking model, and (3) crack repair model. The consumptions and interactions among various ionic species during ASR and electrochemical deposition are quantitively reflected in multi-ionic transport model. The ASR cracking model is developed considering the local mechanical variances of concrete composites. The crack repair model can successfully visualize the crack closure status, and the time-dependent porosity and diffusion coefficients during the treatment have also been well reflected. The proposed model is calibrated and validated against experimental data to ensure the prediction accuracy. A subsequent parameter analysis shows that increase in alkali silica aggregates volume fraction can facilitate cracking process. Besides, for electrochemical deposition treatment on ASR-induced cracks, setting all exposed surfaces as anode can effectively improve the repair rate, and adoption of pulse current can ensure the continuous supply of magnesium ions from external anolyte. Other findings which have not been reported in existing studies are also highlighted, which is hoped to better guide the application in practical engineering.

Automated summary

In this study, the entire process from alkali silica reaction (ASR)-induced concrete cracking to crack repair by electro-chemical deposition method (EDM) is studied for the first time. The proposed model accurately predicts the consumption and interactions of different ionic species, as well as the crack closure status and time-dependent porosity and diffusion coefficients during the treatment.