硅酸盐学报

基于物理信息神经网络的强电场作用下混凝土多离子电迁移规律的数值研究

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陈一飞 ¹ , 刘清风 ¹^,²

硅酸盐学报 | 第二十八届中国科协年会智能建造关键无机非金属材料–AI驱动水泥基材料创新专题-研究论文 2026,54(10): 3453-3463

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硅酸盐学报 |第二十八届中国科协年会智能建造关键无机非金属材料–AI驱动水泥基材料创新专题-研究论文 2026 , 54 (10) : 3453 -3463

基于物理信息神经网络的强电场作用下混凝土多离子电迁移规律的数值研究

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陈一飞¹, 刘清风¹^,²

作者信息

^1.上海交通大学海洋工程全国重点实验室，上海　200240

^2.上海市公共建筑和基础设施数字化运维重点实验室，上海　200240

通讯作者:

刘清风（1986-），男，博士，教授。

作者简介:

陈一飞（1997-），男，博士研究生。

Numerical Study on Multi-Ion Electromigration in Concrete under Strong Electric Fields Based on Physics-Informed Neural Networks

Yifei CHEN¹, Qingfeng LIU¹^,²

Affiliations

^1.State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

^2.Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai 200240, China

Corresponding Author:

LIU Qingfeng (1986-), male, Ph.D., Professor. E-mail: liuqf@sjtu.edu.cn

CHEN Yifei (1997-), male, Doctoral candidate. E-mail: lygcyfm@126.com

出版时间: 2026-09-02 doi: 10.14062/j.issn.0454-5648.20260339

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摘要

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外加电场加速离子迁移已广泛应用于混凝土快速氯离子迁移系数法和电化学修复防护中，然而强电场作用下多离子耦合传输行为及其场强依赖特征尚不明确，传统常数迁移率模型难以准确表征氯离子迁移前沿的非线性演化。针对这一问题，本工作建立了基于物理信息神经网络的强电场下混凝土多离子电迁移数值分析框架，将Poisson-Nernst-Planck（PNP）方程、朗缪尔（Langmuir）氯离子结合关系和场强相关迁移率关系嵌入统一物理约束框架，实现了有限试验数据条件下多离子浓度场、电势场及迁移率参数的协同识别。研究发现，局部电场集中会诱发离子迁移率衰减，使氯离子通量出现饱和甚至下降趋势。进一步分析表明，强电场下多离子浓度剖面呈现迁移前沿局部化和推进减缓特征，数据同化修正主要集中在氯离子迁移前沿及浓度梯度较大的区域，可为快速氯离子迁移系数法中强电场传输行为解释和迁移率参数反演提供参考。

关键词

多离子电迁移 / 物理信息神经网络 / 氯离子迁移 / 数据同化

Abstract

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Introduction

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.

Methods

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.

Results and discussion

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.

Conclusions

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.

Key words

multi-ion electromigration / physics-informed neural network / rapid chloride migration / data assimilation

引用本文

陈一飞, 刘清风. 基于物理信息神经网络的强电场作用下混凝土多离子电迁移规律的数值研究. 硅酸盐学报, 2026 , 54 (10) : 3453 -3463 . DOI: 10.14062/j.issn.0454-5648.20260339

Yifei CHEN, Qingfeng LIU. Numerical Study on Multi-Ion Electromigration in Concrete under Strong Electric Fields Based on Physics-Informed Neural Networks[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (10) : 3453 -3463 . DOI: 10.14062/j.issn.0454-5648.20260339

基金

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国家重点研发计划项目(2024YFB3715100)
重庆市杰出青年科学基金项目(cstb2024nscq-jqX0007)

参考文献

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2026年第54卷第10期

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文章信息

doi: 10.14062/j.issn.0454-5648.20260339

接收时间：2026-05-17
首发时间：2026-10-22
出版时间：2026-09-02

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出版历史

收稿日期：2026-05-17
修回日期：2026-06-17

基金

国家重点研发计划项目(2024YFB3715100)

重庆市杰出青年科学基金项目(cstb2024nscq-jqX0007)

作者信息

^1.上海交通大学海洋工程全国重点实验室，上海　200240

^2.上海市公共建筑和基础设施数字化运维重点实验室，上海　200240

通讯作者:

刘清风（1986-），男，博士，教授。

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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