{"title":"利用机器学习评估砂浆中钢材的腐蚀概率","authors":"Haodong Ji, Yuhui Lyu, Zushi Tian, Hailong Ye","doi":"10.1016/j.ress.2024.110535","DOIUrl":null,"url":null,"abstract":"<div><div>Corrosion assessment enables engineers to quickly discern the corrosion status of steel in concrete structures. However, existing assessment methods mainly rely on a single-factor and exhibit poor adaptability to various corrosion scenarios. Moreover, most methods are traditional deterministic approach, which ignores the uncertainties in corrosion assessments. In this work, machine learning (ML) is employed to develop a multifactor classification model for multi-level corrosion status assessment, together with corresponding corrosion probability maps. First, a comprehensive corrosion dataset was collected, including relative humidity (RH), electrical resistivity (ER), corrosion potential (CP), and corrosion rate (CR). The CR was used to subdivide different corrosion levels, and ML classification models were established for three-factor and two-factor scenarios. The optimal model was then used to create corrosion probability maps for various corrosion levels. The results indicated that the poor reliability and accuracies in current corrosion assessment methods originated from the inconsistent corrosion behaviors induced by carbonation and chloride in concrete. Moreover, when using the corrosion probability maps to assess corrosion status of steel in mortars, CP and ER should first be used to determine if the steel is in an active state, followed by RH and CP to evaluate whether it is in a severe-corrosion state.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4000,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessment of corrosion probability of steel in mortars using machine learning\",\"authors\":\"Haodong Ji, Yuhui Lyu, Zushi Tian, Hailong Ye\",\"doi\":\"10.1016/j.ress.2024.110535\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Corrosion assessment enables engineers to quickly discern the corrosion status of steel in concrete structures. However, existing assessment methods mainly rely on a single-factor and exhibit poor adaptability to various corrosion scenarios. Moreover, most methods are traditional deterministic approach, which ignores the uncertainties in corrosion assessments. In this work, machine learning (ML) is employed to develop a multifactor classification model for multi-level corrosion status assessment, together with corresponding corrosion probability maps. First, a comprehensive corrosion dataset was collected, including relative humidity (RH), electrical resistivity (ER), corrosion potential (CP), and corrosion rate (CR). The CR was used to subdivide different corrosion levels, and ML classification models were established for three-factor and two-factor scenarios. The optimal model was then used to create corrosion probability maps for various corrosion levels. The results indicated that the poor reliability and accuracies in current corrosion assessment methods originated from the inconsistent corrosion behaviors induced by carbonation and chloride in concrete. Moreover, when using the corrosion probability maps to assess corrosion status of steel in mortars, CP and ER should first be used to determine if the steel is in an active state, followed by RH and CP to evaluate whether it is in a severe-corrosion state.</div></div>\",\"PeriodicalId\":54500,\"journal\":{\"name\":\"Reliability Engineering & System Safety\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.4000,\"publicationDate\":\"2024-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Reliability Engineering & System Safety\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0951832024006070\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reliability Engineering & System Safety","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0951832024006070","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
摘要
锈蚀评估可使工程师快速判别混凝土结构中钢材的锈蚀状况。然而,现有的评估方法主要依赖于单一因素,对各种腐蚀情况的适应性较差。此外,大多数方法都是传统的确定性方法,忽略了腐蚀评估中的不确定性。在这项工作中,采用机器学习(ML)技术开发了一种用于多级腐蚀状态评估的多因素分类模型,以及相应的腐蚀概率图。首先,收集了全面的腐蚀数据集,包括相对湿度 (RH)、电阻率 (ER)、腐蚀电位 (CP) 和腐蚀速率 (CR)。CR 用于细分不同的腐蚀等级,并为三因素和二因素情况建立了 ML 分类模型。然后使用最优模型绘制不同腐蚀等级的腐蚀概率图。结果表明,当前腐蚀评估方法的可靠性和准确性较差,原因是混凝土中的碳化和氯化物诱发了不一致的腐蚀行为。此外,在使用腐蚀概率图评估砂浆中钢筋的腐蚀状态时,应首先使用 CP 和 ER 来确定钢筋是否处于活跃状态,然后再使用 RH 和 CP 来评估钢筋是否处于严重腐蚀状态。
Assessment of corrosion probability of steel in mortars using machine learning
Corrosion assessment enables engineers to quickly discern the corrosion status of steel in concrete structures. However, existing assessment methods mainly rely on a single-factor and exhibit poor adaptability to various corrosion scenarios. Moreover, most methods are traditional deterministic approach, which ignores the uncertainties in corrosion assessments. In this work, machine learning (ML) is employed to develop a multifactor classification model for multi-level corrosion status assessment, together with corresponding corrosion probability maps. First, a comprehensive corrosion dataset was collected, including relative humidity (RH), electrical resistivity (ER), corrosion potential (CP), and corrosion rate (CR). The CR was used to subdivide different corrosion levels, and ML classification models were established for three-factor and two-factor scenarios. The optimal model was then used to create corrosion probability maps for various corrosion levels. The results indicated that the poor reliability and accuracies in current corrosion assessment methods originated from the inconsistent corrosion behaviors induced by carbonation and chloride in concrete. Moreover, when using the corrosion probability maps to assess corrosion status of steel in mortars, CP and ER should first be used to determine if the steel is in an active state, followed by RH and CP to evaluate whether it is in a severe-corrosion state.
期刊介绍:
Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.