Hongzhang Wang¹, Jing Guo¹, Shanjun Yang¹, Chaoheng Cheng², Jing Chen^3,*, Zhihao Chen³

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 181-196, 2024, DOI:10.32604/sdhm.2024.044628

Abstract Pitting corrosion is harmful during bridge construction, which will lead to uneven roughness of steel surfaces and reduce the thickness of steel. Hence, the effect of pitting corrosion on the mechanical properties of cold-formed thin-walled steel stub columns is studied, and the empirical formulas are established through regression fitting to predict the ultimate load of web and flange under pitting corrosion. In detail, the failure modes and load-displacement curves of specimens with different locations, area ratios, and depths are obtained through a large number of non-linear finite element analysis. As for the specimens with pitting corrosion on the web, all… More > Graphic Abstract

Hongtao Liu¹, Maoxian Xiong¹, Bo Zhang^2,*, Junfeng Xie¹, Jinrui Deng¹, MifengZhao², Ruijing Jiang¹, Yushan Zheng¹

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.6, pp. 1709-1720, 2023, DOI:10.32604/fdmp.2023.025513

Abstract Pitting corrosion often occurs due to the presence of various corrosive substances, such as CO₂ and H₂S, in the pipe service environment. As a result of this process, the residual strength of oil pipes is reduced and this can compromise the integrity of the entire pipe string. In the present work, a model is introduced on the basis of the API579 standard to determine the so-called stress concentration coefficient. The model accounts for pitting corrosion shapes such as shallow semi-circles, semi-circles, and deep semi-circles. The relationship between the corrosion pit depth and opening diameter and the residual strength of the… More >

Ramana M. Pidaparti^1,2, Evan J. Neblett²

CMC-Computers, Materials & Continua, Vol.5, No.1, pp. 1-10, 2007, DOI:10.3970/cmc.2007.005.001

Abstract A neural network (NN) model is developed for the analysis and prediction of the mapping between degradation of chemical elements and electrochemical parameters during the corrosion process. The input parameters to the neural network model are alloy composition, electrochemical parameters, and corrosion time. The output parameters are the degradation of chemical elements in AA 2024-T3 material. The NN is trained with the data obtained from Energy Dispersive X-ray Spectrometry (EDS) on corroded specimens. A very good performance of the neural network is achieved after training and validation with the experimental data. After validating the NN model, simulations were carried out… More >