文献类型：期刊文献

英文题名：Physical constraint-based LSTM for pipeline corrosion prediction

作者：Su, Fanjun Yin, Aijun Qiao, Zhaohui Wang, Xibing

第一作者：苏凡竣;Su, Fanjun

通信作者：Yin, AJ[1]

机构：[1]Guizhou Inst Technol, Guiyang 550002, Guizhou, Peoples R China;[2]Key Lab New Power Syst Operat Control Guizhou Prov, Guiyang 550002, Guizhou, Peoples R China;[3]Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400000, Peoples R China

第一机构：贵州理工学院

通信机构：corresponding author), Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400000, Peoples R China.

年份：2025

卷号：7

期号：4

外文期刊名：ENGINEERING RESEARCH EXPRESS

收录：EI(收录号：20254819584707);Scopus(收录号：2-s2.0-105022655631);WOS:【ESCI(收录号:WOS:001621794200001)】；

语种：英文

外文关键词：physical constraints; long and short-term memory networks; time series analysis

摘要：The complex corrosion evolution of pipelines presents a significant challenge for integrity management, as traditional physical models often fail in long-term prediction and purely data-driven methods struggle with limited, noisy data. To address this, this study proposes a novel hybrid physics-informed long short-term memory (PI-LSTM) network. The framework utilizes a physical model to capture the primary corrosion trend, while an LSTM is trained to learn the remaining nonlinear residuals. To ensure physical plausibility, the model embeds constraints derived from the ordinary differential equation (ODE) governing corrosion kinetics into the composite loss function. The proposed PI-LSTM model was validated on field monitoring data and compared against multiple benchmarks. The experimental results demonstrate its superior performance, achieving a mean Root Mean Square Error (RMSE) of 2.46 +/- 0.28. This result is not only more accurate than the traditional Vel & aacute;zquez model (RMSE: 2.74) but also significantly surpasses other data-driven and time-series models. An ablation study further confirmed that the physical constraint was crucial, improving both accuracy and stability over the standard LSTM model (RMSE: 2.63 +/- 0.51), with a 47% reduction in standard deviation highlighting its powerful regularization effect. The superior performance across all evaluation metrics indicates that the proposed method has high prediction accuracy for the dataset under investigation. While further validation on diverse datasets is required to fully establish its generalizability, this study demonstrates that the hybrid, physics-informed framework offers a promising and robust new approach for pipeline corrosion research.