corrosion data
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Author(s):  
Tushar Bansal ◽  
Visalakshi Talakokula ◽  
Prabhakar Sathujoda

Abstract The application of the electro-mechanical impedance (EMI) technique using piezo sensors for structural health monitoring (SHM) is based on baseline/healthy signature data, which poses serious limitations when it needs to be applied to existing structures. Therefore, the present research utilizes autoregressive integrated moving average (ARIMA), an effective time series forecasting machine learning (ML) algorithm to predict the baseline/healthy EMI data and futuristic data of reinforced concrete (RC) corroded specimens. The EMI data from the ARIMA model is validated with the experimental data, and the results obtained prove that the model could be utilized to predict the baseline and forecast the EMI corrosion data effectively. These results will aid the researchers to predict the baseline data for the existing structures and utilize the EMI technique for SHM purposes.


Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6954
Author(s):  
Jintao Meng ◽  
Hao Zhang ◽  
Xue Wang ◽  
Yue Zhao

An electrical resistance sensor-based atmospheric corrosion monitor was employed to study the carbon steel corrosion in outdoor atmospheric environments by recording dynamic corrosion data in real-time. Data mining of collected data contributes to uncovering the underlying mechanism of atmospheric corrosion. In this study, it was found that most statistical correlation coefficients do not adapt to outdoor coupled corrosion data. In order to deal with online coupled data, a new machine learning model is proposed from the viewpoint of information fusion. It aims to quantify the contribution of different environmental factors to atmospheric corrosion in different exposure periods. Compared to the commonly used machine learning models of artificial neural networks and support vector machines in the corrosion research field, the experimental results demonstrated the efficiency and superiority of the proposed model on online corrosion data in terms of measuring the importance of atmospheric factors and corrosion prediction accuracy.


2021 ◽  
Vol 8 ◽  
Author(s):  
Zhihao Qu ◽  
Dezhi Tang ◽  
Zhu Wang ◽  
Xiaqiao Li ◽  
Hongjian Chen ◽  
...  

Pitting corrosion seriously harms the service life of oil field gathering and transportation pipelines, which is an important subject of corrosion prevention. In this study, we collected the corrosion data of pipeline steel immersion experiment and established a pitting judgment model based on machine learning algorithm. Feature reduction methods, including feature importance calculation and pearson correlation analysis, were first adopted to find the important factors affecting pitting. Then, the best input feature set for pitting judgment was constructed by combining feature combination and feature creation. Through receiver operating characteristic (ROC) curve and area under curve (AUC) calculation, random forest algorithm was selected as the modeling algorithm. As a result, the pitting judgment model based on machine learning and high dimensional feature parameters (i.e., material factors, solution factors, environment factors) showed good prediction accuracy. This study provided an effective means for processing high-dimensional and complex corrosion data, and proved the feasibility of machine learning in solving material corrosion problems.


Materials ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4755
Author(s):  
Stanley Udochukwu Ofoegbu

Food contamination due to metal corrosion and the consequent leakage of metals into foods is a problem. Understanding the mechanism(s) of metal corrosion in food media is vital to evaluating, mitigating, and predicting contamination levels. Fruit juices have been employed as model corrosive media to study the corrosion behaviour of metallic material in food media. Carbon steel corrosion in fresh juices of tomato, orange, pineapple, and lemon, as well as dilute hydrochloric acid solutions at varied pH, was studied using scanning electron microscopy, gravimetric and spectrophotometric techniques, and comparisons made between the corrosivity of these juices and mineral acids of comparable pH. The corrosion of carbon steel in fruit juices and HCl solutions manifests as a combination of uniform and pitting corrosion. Gravimetric data acquired after one hour of immersion at ambient temperature (22 °C) indicated corrosion rates of 0.86 mm yr−1 in tomato juice (pH ≈ 4.24), 1.81 mm yr−1 in pineapple juice (pH ≈ 3.94), 1.52 mm yr−1 in orange juice (pH ≈ 3.58), and 2.89 mm yr−1 in lemon juice (pH ≈ 2.22), compared to 2.19 mm yr−1 in 10−2 M HCl (pH ≈ 2.04), 0.38 mm yr−1 in 10−3 M HCl (pH ≈ 2.95), 0.17 mm yr−1 in 10−4 M HCl (pH ≈ 3.95), and 0.04 mm yr−1 in 10−5 M HCl (pH ≈ 4.98). The correlation of gravimetrically acquired corrosion data with post-exposure spectrophotometric analysis of fruit juices enabled de-convolution of iron contamination rates from carbon steel corrosion rates in fruit juices. Elemental iron contamination after 50 h of exposure to steel samples was much less than the values predicted from corrosion data (≈40%, 4.02%, 8.37%, and 9.55% for tomato, pineapple, orange, and lemon juices, respectively, relative to expected values from corrosion (weight loss) data). Tomato juice (pH ≈ 4.24) was the least corrosive to carbon steel compared to orange juice (pH ≈ 3.58) and pineapple juice (pH ≈ 3.94). The results confirm that though the fruit juices are acidic, they are generally much less corrosive to carbon steel compared to hydrochloric acid solutions of comparable pH. Differences in the corrosion behaviour of carbon steel in the juices and in the different mineral acid solutions are attributed to differences in the compositions and pH of the test media, the nature of the corrosion products formed, and their dissolution kinetics in the respective media. The observation of corrosion products (iron oxide/hydroxide) in some of the fruit juices (tomato, pineapple, and lemon juices) in the form of apparently hollow microspheres indicates the feasibility of using fruit juices and related wastes as “green solutions” for the room-temperature and hydrothermal synthesis of metal oxide/hydroxide particles.


CONVERTER ◽  
2021 ◽  
pp. 342-346
Author(s):  
Xiaolin Li, Et al.

Power grounding is a key aspectofensuringsafety in power facilities.However, the corrosion of grounding materials can cause accidentsduringpower facility operation. Therefore, monitoring the corrosion status of grounding materials can eliminate hidden risks in the grounding network and ensure safe poweroperation. In this paper, electromagnetic ultrasonic thickness measurement technology was used to develop an online corrosion monitoring system for grounding materials via the installation of electromagnetic ultrasonic measurement probes on in-service power grounding materials. The results from a substation grounding networkdemonstrate that the online corrosion monitoring system can obtain more precise grounding corrosion data and has more extensive application prospects compared with other monitoring methods.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhuolin Li ◽  
Dongmei Fu ◽  
Zibo Pei

Purpose This paper aims to discover the mathematical model for Q235 carbon steel corrosion date acquired in the initial stage of atmospheric corrosion using electrical resistance probe. Design/methodology/approach In this paper, mathematical approaches are used to construct a classification model for atmospheric environmental elements and material corrosion rates. Findings Results of the experiment show that the corrosion data can be converted into corrosion depth for calculating corrosion rate to obtain corrosion kinetics model and conform corrosion acceleration phase. Combined with corresponding atmospheric environmental elements, a real time grade subdivision model for corrosion rate can be constructed. Originality/value These mathematical models constructed by real time corrosion data can be well used to research the characteristics about initial atmospheric corrosion of Q235 carbon steel.


2021 ◽  
Vol 68 (1) ◽  
pp. 17-28
Author(s):  
Liang Zhao ◽  
Wen Tao ◽  
Guangwen Wang ◽  
Lida Wang ◽  
Guichang Liu

Purpose The paper aims to develop an intelligent anti-corrosion expert system based on browser/server (B/S) architecture to realize an intelligent corrosion management system. Design/methodology/approach The system is based on Java EE technology platform and model view controller (MVC) three-tier architecture development model. The authors used an extended three-dimensional interpolation model to predict corrosion rate, and the model is verified by cross-validation method. Additionally, MySQL is used to realize comprehensive data management. Findings The proposed anti-corrosion system thoroughly considers a full use of corrosion data, relevant corrosion prediction and efficient corrosion management in one system. Therefore, this system can achieve an accurate prediction of corrosion rate, risk evaluation, risk alert and expert suggestion for equipment in petrochemical plants. Originality/value Collectively, this present study has important ramifications for the more efficient and scientific management of corrosion data in enterprises and experts’ guidance in controlling corrosion status. At the same time, the digital management of corrosion data can provide a data support for related theoretical researches in corrosion field, and the intelligent system also offers examples in other fields to improve system by adding intelligence means.


Materials ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3266 ◽  
Author(s):  
Luchun Yan ◽  
Yupeng Diao ◽  
Kewei Gao

As one of the factors (e.g., material properties, surface quality, etc.) influencing the corrosion processes, researchers have always been exploring the role of environmental factors to understand the mechanism of atmospheric corrosion. This study proposes a random forest algorithm-based modeling method that successfully maps both the steel’s chemical composition and environmental factors to the corrosion rate of low-alloy steel under the corresponding environmental conditions. Using the random forest models based on the corrosion data of three different atmospheric environments, the environmental factors were proved to have different importance sequence in determining the environmental corrosivity of open and sheltered exposure test conditions. For each exposure test site, the importance of environmental features to the corrosion rate is also ranked and analyzed. Additionally, the feasibility of the random forest model to predict the corrosion rate of steel samples in the new environment is also demonstrated. The volume and representativeness of the corrosion data in the training data are considered to be the critical factors in determining its prediction performance. The above results prove that machine learning provides a useful tool for the analysis of atmospheric corrosion mechanisms and the evaluation of corrosion resistance.


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