Steel bar corrosion monitoring with long-period fiber grating sensors coated with nano iron/silica particles and polyurethane

Author:

Huang Ying1,Tang Fujian2,Liang Xiao1,Chen Genda2,Xiao Hai3,Azarmi Fardad4

Affiliation:

1. Department of Civil and Environmental Engineering, North Dakota State University, Fargo, ND, USA

2. Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO, USA

3. The Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA

4. Department of Mechanical Engineering, North Dakota State University, Fargo, ND, USA

Abstract

In this article, a recently proposed long-period fiber grating sensor coated with a thin layer of polyurethane and nano iron/silica particles is further developed and applied to monitor the corrosion process of deformed steel bars. Once calibrated, one coated long-period fiber grating sensor and one uncoated long-period fiber grating sensor for environmental compensation were attached to each of three steel bar samples that were tested in 3.5 wt% NaCl solution for 512 h. The resonant wavelength in long-period fiber grating spectra increased exponentially with immersion time due to corrosion of iron particles and thus reduction in coating thickness. The mass loss rate of steel bar #1 at the completion of corrosion tests (512 h of corrosion time) was correlated with that of sparse iron particles on long-period fiber grating sensor #1 after 130.5 h of immersion. The corrosion rates of long-period fiber grating sensors #2 and #3 were evaluated at 130.5 h and then used as a prediction of the corrosion rates of steel bars #2 and #3. The predicted corrosion rates by the long-period fiber grating sensors #2 and #3 were finally compared with those by potentiodynamic tests. The maximum mass loss prediction error by the long-period fiber grating sensors #2 and #3 is 26%. The coefficients of variation of three corrosion rate measurements are 0.049 by the long-period fiber grating sensors and 0.115 by the potentiodynamic tests, indicating more consistent and reliable measurements with the proposed technology.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Biophysics

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3