Externally applied electric fields are widely used in rapid chloride migration coefficient tests and electrochemical repair or protection of concrete structures. These techniques accelerate the directional migration of ions in the pore solution and are important for evaluating chloride resistance and controlling reinforcement corrosion risks. However, concrete is a heterogeneous porous material composed of mortar, interfacial transition zones, and aggregates. Under strong electric fields, the distributions of electric-potential, ion concentration and migration flux become strongly coupled. The conventional Poisson-Nernst-Planck model usually assumes a constant ionic mobility, implying that the electromigration flux increases approximately linearly with the electric field intensity. This assumption may be insufficient under strong electric-field conditions because local electric-field concentration, concentration polarization and ion crowding can reduce the effective ionic mobility. As a result, the chloride migration front and migration flux may show nonlinear evolution. To address this issue, this work develops a physics-informed neural network framework for identifying a field-dependent ionic-mobility relation and analyzing multi-ion electromigration in concrete under strong electric fields.

A multi-ion electromigration model was established based on the Poisson-Nernst-Planck equations and the Langmuir chloride-binding relation. Four representative ions in RCM tests, i.e., Na⁺, K⁺, Cl^- and OH^-, were considered. A field-dependent mobility-suppression function was introduced into the electromigration term to describe the nonlinear attenuation of effective ionic mobility under strong electric fields. The concrete specimen was represented as a two-dimensional, three-phase mesoscopic domain consisting of mortar, interfacial transition zones, and aggregates. Aggregates were regarded as impermeable, while ion transport occurred in mortar and interfacial transition zones.

To identify the mobility suppression parameters from limited chloride profiles, an importance-adaptive-training physics-informed neural network was adopted. The governing equations, boundary conditions, initial conditions and experimental chloride-concentration profiles were embedded into the loss function. The neural network took dimensionless coordinates, time and voltage as inputs, and output the normalized electric potential and ion concentrations. The shape parameters of the mobility suppression function were optimized together with the neural network parameters. RCM data reported by Spiesz and Brouwers were used for model validation. The chloride profile at 47.5 V was used for parameter identification, while the profiles at 35 V and 60 V were used to evaluate the cross-voltage prediction capability. Prediction accuracy was evaluated using RMSE and R².

The identified mobility suppression parameters obtained by IAT-PINN were (α, γ) = (0.74, 1.37), while the least-squares (LSQ) method gave (α, γ) = (0.03, 1.04). The LSQ result was closer to the boundary of the search domain, indicating possible parameter nonuniqueness when only discrete chloride profiles were fitted. In contrast, the IAT-PINN introduced governing equations and boundary constraints into the inversion process, which improved the stability of parameter identification. When the proportion of training samples decreased, the IAT-PINN maintained lower RMSE and higher R² than LSQ, showing better data efficiency under sparse observations.

The same mobility parameters identified at 47.5 V were then used to predict chloride profiles at 35 V and 60 V without recalibration. The model captured the voltage-dependent advancement of the chloride migration front and agreed well with the measured profiles. The improvement was more evident at high voltage, indicating that the field-dependent mobility relationship reduced the prediction bias caused by the constant-mobility assumption.

The identified suppression function further revealed a transition from a driving force-controlled regime to a mobility-limited regime. At low electric field intensity, the suppression factor was close to 1 and the migration response was nearly linear. As the electric field increased, the effective mobility decreased, causing the chloride migration contribution to reach a peak and then decline. Multi-ion concentration profiles showed localized migration bands for Cl^-, K⁺, Na⁺ and OH^-, whose fronts gradually slowed with time. The data-assimilation correction was mainly concentrated near the chloride migration front, confirming that experimental data selectively constrained the high-gradient transport region rather than shifting the entire concentration field.

A field-dependent mobility-suppression relation was incorporated into a multi-ion Poisson-Nernst-Planck model and identified using an importance-adaptive physics-informed neural network. The proposed framework enabled the joint reconstruction of ionic concentration fields, electric-potential fields and mobility parameters from limited RCM data. Compared with least-squares (LSQ) fitting, the IAT-PINN improved parameter stability under sparse data by introducing physical constraints. The identified mobility relation showed that chloride migration under strong electric fields is not linearly enhanced with increasing voltage. Instead, effective mobility attenuation can lead to saturation or reduction of the migration contribution. Multi-ion concentration profiles and data assimilation corrections further confirmed the localized and migration-limited characteristics of strong electric-field electromigration. The proposed method provides a theoretical basis for interpreting RCM test results and identifying field-dependent mobility parameters in concrete.