Liu / Wang / Li Marine Corrosion of Steels

Mechanisms and AI-Driven Solutions
1. Auflage 2026
ISBN: 978-3-527-85477-6
Verlag: Wiley-VCH
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 930 Seiten, E-Book

Bridges corrosion science and artificial intelligence to advance durable, high-performance marine steels
 
Understanding and controlling the corrosion of steels in marine environments is a critical challenge for modern engineering, with far-reaching implications for safety, durability, and sustainability. Marine corrosion not only threatens the integrity of infrastructure such as bridges, offshore platforms, and railways but also leads to significant economic losses and environmental risks. Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions provides a comprehensive exploration of the mechanistic processes underlying steel degradation and introduces innovative, data-driven approaches to improve corrosion resistance.
 
Offering valuable insights into stress corrosion cracking, fatigue, and general degradation mechanisms in harsh marine conditions, the book systematically investigates corrosion behavior across a range of engineering steels, including high-manganese steel, titanium-steel composites, low-alloy rebar, and ductile iron. Beyond mechanistic analysis, dedicated chapters highlight pioneering applications of big data and artificial intelligence, such as predictive modeling, image recognition for pit analysis, and IoT-enabled real-time monitoring. These AI-driven approaches enable researchers and engineers to accelerate alloy design, optimize material selection, and implement proactive maintenance strategies.
 
Combining deep scientific understanding with cutting-edge computational tools, Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions:
 
* Demonstrates how corrosion science is evolving toward intelligent, sustainable solutions for the most demanding industrial applications
* Explores alloying strategies and material innovations to enhance corrosion resistance
* Introduces novel electrochemical evaluation methods and image-based corrosion quantification
* Presents real-world case studies, including corrosion fatigue in railway components and offshore steel performance
* Discusses multi-modal frameworks combining physics, machine learning, and computer vision
* Offers forward-looking insights on AI-driven alloy design for sustainability and cost reduction
 
Connecting fundamental research to practical engineering solutions across multiple industries, Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions is an essential resource for graduate-level courses in materials science, corrosion engineering, and mechanical engineering, particularly within programs in metallurgy, chemical engineering, and civil infrastructure. It is also a valuable reference for engineers, corrosion specialists, and researchers working in marine, aerospace, and energy industries.

Autoren/Hrsg.

Liu, Chao

Wang, Bingqin

Li, Xiaogang

Zhang, Shasha

Li, Zhong

Inhaltsverzeichnis

Chapter 1. Stress corrosion behavior of high manganese steel in polluted marine atmospheric environments.
Chapter 2.Corrosion fatigue behavior of high manganese steel in atmospheric environment.
Chapter 3.Effect of Microalloying Elements on The Corrosion Resistance of Low-Density Steel.
Chapter 4. Coupling of Multiple Corrosion in the Corrosion Process of Titanium-Steel Composites .
Chapter 5.Effects of Corrosion Inhibitors and Flow rate on the Corrosion Resistance of Ductile Iron Pipes.
Chapter 6. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Rebar Corrosion Resistance Modulation.
Chapter 7.Application of Novel Big Data Intelligent Corrosion Assessment Approach in blast furnace gas pipe steel corrosion resistance Analysis.
Chapter 8. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Corrosion-Resistant Low Alloy Steel Development.
Chapter 9. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Corrosion Prediction and Data Mining Modeling.
Chapter 10. Perspectives on the Application of Artificial Intelligence in Investigating Corrosion Mechanisms of Steel and Designing Corrosion-Resistant Alloys.

Über Autor(innen)

Chao Liu is a professor at the Institute of Advanced Materials and Technology, University of Science and Technology Beijing. After obtaining his PhD degree from University of Science and Technology Beijing, he stayed in the university as a teacher and visited VUB in Brussels, Belgium and Massachusetts Institute of Technology (MIT) in the United States successively. Based on the major national strategies and the practical needs of material corrosion protection, he has been carrying out research work on the theory and application of the frontiers of micro-zone electrochemical technology for localised corrosion of materials, the theory and technology of corrosion big data, and the research and development of corrosion-resistant new materials. He has published more than 60 SCI papers and won several scientific awards.
 
Bingqin Wang is an assistant researcher at the Institute of Advanced Materials snd Technology, University of Science and Technology Beijing. After earning his PhD degree from University of Science and Technology Beijing, he has been engaged in research work centered on corrosion big data technology. He has discovered the dynamic evolution law of weathering steel rust layers and the critical temperature of atmospheric corrosion that affects the protective performance of rust layers. Additionally, he has developed corrosion image big data technology, successfully achieving atmospheric corrosion prediction under multi-modal conditions and quantification of the protective performance of rust layers. He has published more than 20 SCI papers and won several scientific awards.
 
Xiaogang Li is a professor at the University of Science and Technology Beijing. He holds several leadership roles in prominent academic and professional organizations. His research interests include material corrosion theory, development of corrosion-resistant new steel grades, and improving the performance of traditional weathering steel. He has received many scientific and technological awards, including the NACE International Outstanding Engineering Contribution Award and the National Outstanding Engineer Award.
 
Shasha Zhang is an engineer at the School of Advanced Engineering at the University of Science and Technology Beijing. Based on the national artificial intelligence technology development strategy and the actual needs of material corrosion protection, she has been dedicated to research in artificial intelligence and Internet of Things (IoT) technology, the application of artificial intelligence technology in material corrosion detection, and the theory and technology of corrosion big data. Her academic contributions include three published papers, two authored books, and multiple awards in science and technology competitions.
 
Zhong Li is an associate professor in the Institute for advanced Materials and Technology, University of Science and Technology Beijing. Her graduated from Harbin Engineering University for Bachelor of Science, University of Calgary for Master degree and Ohio University for Ph. D degree. She was employed in Institute of corrosion control systems engineering. Her research work focusses on the theory and behavior of microbiologically induced corrosion, theory and behavior of stress corrosion cracking, the theory and technology of corrosion big data and the research and development of advanced materials for microbiologically induced corrosion resistance. She has published more than 20 SCI papers and won several scientific awards.