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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.
Contents
Table of Contents
Preface
Chapter 1. Stress corrosion behavior of high manganese steel in polluted marine atmospheric environments
1.1 Introduction
1.2 Early corrosion initiation behavior of composite inclusions in high manganese steel
1.2.1 Materials and Methods
1.2.1.1 Materials and Solutions
1.2.1.2 Material Microstructure Characterization
1.2.1.3 Electrochemical Testing
1.2.1.4 Microcellular surface potential measurements
1.2.1.5 In-situ immersion test for inclusions
1.2.2 Physicochemical properties and corrosion localized corrosion
1.2.2.1 Physicochemical properties of high manganese steel
1.2.2.2 Typical inclusions morphology and micro-zone electrochemistry in high manganese steel
1.2.2.3 Micro-corrosion of Inclusion Areas
1.3 Corrosion Behavior and Mechanism of High-Manganese Steel in Environments Containing Sulfur and Chloride
1.3.1 Materials and Methods
1.3.1.1 Materials
1.3.1.2 Cyclic immersion acceleration test
1.3.1.3 Analysis of Corrosion Products
1.3.1.4 Rust layer electrochemical testing
1.3.2 Corrosion behaviour and electrochemical characteristics
1.3.2.1 Corrosion weight loss and corrosion rate
1.3.2.2 Analysis of corrosion products
1.3.2.3 Analysis of Corrosion Morphology
1.3.2.4 Electrochemical Analysis of Rust Layer
1.3.3 Corrosion mechanism
1.4 Research on the Stress Corrosion Cracking Behavior and Mechanism of High-Manganese Steel in Sulfur-and Chloride-Containing Environments
1.4.1 Materials and Methods
1.4.1.1 Materials
1.4.1.2 Constant load U-bend circumferential dip test
1.4.1.3 Slow Strain Rate Tensile Test
1.4.2 Stress corrosion behaviour
1.4.2.1 Behavioral analysis of high manganese steel U-bend SCC
1.4.2.2 Analysis of Stress-Strain Curves for High-Manganese Steel
1.4.2.3 Analysis of Fracture Morphology of High-Manganese Steel
1.4.3 Stress Corrosion Cracking Mechanism
1.5 Chapter Summary
Chapter 2. Corrosion fatigue behavior of high manganese steel in atmospheric environment
2.1 Introduction
2.2 Early corrosion budding behavior of high manganese steel in simulated atmospheric environment
2.2.1 Experimental Materials and Methods
2.2.1.1 Materials and solutions
2.2.1.2 Experimental Method
2.2.2 Material Basis Properties and Localized Corrosion Emergence Behaviour
2.2.2.1 Microstructure and mechanical properties
2.2.2.2 Morphology and properties of typical inclusions
2.2.2.3 Inclusions induce corrosion initiation
2.3 Corrosion Laws and Mechanisms of High-Manganese Steel in Simulated Atmospheric Environments
2.3.1 Experimental Materials and Methods
2.3.1.1 Materials
2.3.1.2 Cyclic wetting and drying experiment
2.3.1.3 Electrochemical Testing
2.3.1.4 Analysis of Corrosion Products
2.3.2 Corrosion behaviour and characteristics
2.3.2.1 Corrosion Weight Loss and Corrosion Rate
2.3.2.2 Analysis of Rust Layer Cross-Section
2.3.2.3 Rust Layer Products Characteristics
2.3.2.4 Electrochemical Analysis of Rust Layers
2.3.2.5 Corrosion Morphology
2.3.3 Corrosion Electrochemical Processes of High-Manganese Steel
2.4 Corrosion Fatigue Laws and Mechanisms of High-Manganese Steel in Simulated Atmospheric Environments
2.4.1 Experimental Materials and Methods
2.4.1.1 Materials
2.4.1.2 Axial stress corrosion fatigue experiment
2.4.1.3 Characterization of Corrosion Fatigue Cracks
2.4.2 Electrochemical properties and corrosion fatigue behaviour
2.4.2.1 Electrochemical Testing
2.4.2.2 Corrosion Fatigue Behavior
2.4.2.3 Morphology of Corrosion Fatigue Fracture Surface
2.4.2.4 Analysis of Secondary Cracks Due to Corrosion Fatigue
2.4.3 Corrosion Fatigue Mechanism of High-Manganese Steel in Simulated Atmospheric Environments
2.5 Chapter Summary
Chapter 3. Effect of microalloying elements on the corrosion resistance of low density steel
3.1 Introduction
3.2 Effect of Cr and Ni on corrosion resistance of Fe-Mn-Al-C low density high strength steel
3.2.1 Materials and Methods
3.2.1.1 Materials
3.2.1.2 Characterization of Experimental Materials' Microstructure
3.2.1.3 Accelerated Indoor Simulation of Marine Atmospheric Environment Experiments
3.2.1.4 Analysis of Corrosion Products and Morphology of Specimens after Rust Removal
3.2.1.5 Macroelectrochemical Testing at the Initial Stage of Corrosion
3.2.1.6 Macroelectrochemical Experiments for Short-Term Immersion
3.2.1.7 Real-Time Corrosion Monitoring Experiment for Short-Term Immersion
3.2.1.8 Random Forest Modeling Analysis
3.2.2 Basic material properties and corrosion behaviour
3.2.2.1 Microstructure Analysis
3.2.2.2 Density and Mechanical Properties Analysis
3.2.2.3 Corrosion Morphology
3.2.2.4 Corrosion Kinetics Analysis
3.2.2.5 Macroelectrochemical properties
3.2.3 Study on dynamic corrosion process of Fe-Mn-Al-C Low-Density Steel by alloying elements
3.2.3.1 Real-Time Monitoring and Analysis of Short-Term Immersion Corrosion
3.2.3.2 Random Forest Modeling Analysis
3.3 Effect of Cr-Ni Microalloying on the Corrosion Resistance of Fe- Mn-Al-C Low-Density Steel with heat-treatment
3.3.1 Materials and Methods
3.3.1.1 Materials
3.3.1.2 Microstructure characterization
3.3.1.3 Mechanical Performance Testing
3.3.1.4 Immersion test
3.3.1.5 Periodic Immersion Experiment
3.3.1.6 Corrosion Morphology and Corrosion Product Analysis
3.3.1.7 Macroelectrochemical Experiment
3.3.1.8 Thermodynamic Calculations
3.3.1.9 Immersion Corrosion Real-Time Experiment
3.3.1.10 Random Forest Modeling Analysis
3.3.2 Characterisation of basic properties and corrosion behaviour
3.3.2.1 Microstructure
3.3.2.2 Mechanical Properties
3.3.2.3 Corrosion Morphology
3.3.2.4 Corrosion Rate
3.3.2.5 Corrosion Product
3.3.2.6 Electrochemical properties
3.3.2.7 Thermodynamic Calculation Analysis
3.3.3 Corrosion mechanism of heat-treated Low-Density Steel with addiction of Alloying Elements
3.3.4 Analysis of Corrosion Model for Fe-Mn-Al-C Type Low- Density Steel Based on Corrosion Big Data
3.3.4.1 Dynamic corrosion current
3.3.4.2 Random Forest Modeling Analysis
3.3.4.3 Validation of Random Forest Prediction Data
3.3.4.4 Analysis of Feature Variable Correlation
3.4 Chapter Summary
Chapter 4. Interaction of Multiple Corrosion Modes
4.1 Introduction
4.2 Corrosion Mechanism of TA2/Q345B Composite Plate
4.2.1 Materials and Methods
4.2.1.1 Material Preparation
4.2.1.2 Crystallographic Information and Microstructural Analysis
4.2.1.3 Electrochemical Testing
4.2.1.4 Immersion Testing
4.2.1.5 Micro-Region Electrochemical Testing
4.2.1.6 Thermodynamic Calculations
4.2.2 Corrosion Behaviour of TA2/Q345B Composite Plates
4.2.2.1 Microstructure of TA2/Q345B Composite Plate
4.2.2.2 Corrosion Resistance of Titanium-Steel Composite Plates
4.2.2.3 Surface Morphology of Titanium-steel composite Samples After Immersion Test
4.2.2.4 The Localized Electrochemical Properties Associated with the Inclusion of Al2O3•MnS
4.2.3 Corrosion Mechanism
4.3 Degradation Process of TA2/Q345B Composite Sheet in Synthetic Contaminated Seawater Environment
4.3.1 Materials and Methods
4.3.1.2 Analysis of Corrosion Morphology and Corrosion Products
4.3.1.3 Weight Loss Calculation
4.3.1.4 Electrochemical Testing
4.3.2 Corrosion Behaviour of TA2/Q345B Composite Plates in Polluted Marine Solutions
4.3.2.1 Surface Morphology Observation After Immersion Experiments
4.3.2.2 Corrosion Morphology of Point Defects in Titanium-Steel Composite Plates
4.3.2.3 Influence of Point Defects on the Corrosion Rate of Titanium-Steel Composite Plates in Simulated Marine Solution
4.3.3 Galvanic Current and Galvanic Potential in Simulated Polluted Marine Solution
4.3.4 Effect of Linear Defects on the Corrosion Rate of Titanium-Steel Composite Plates
4.3.4.1 Corrosion Product Analysis After Immersion Experiments
4.3.4.2 The Corrosion Kinetics of Titanium-Steel Composite Plates in a Marine Environment
4.3.4.3 Corrosion Resistance of Titanium-Steel Composite Plates in a Polluted Marine Environment
4.3.5 Corrosion Mechanism
4.4 Chapter Summary
Chapter 5. Effects of Corrosion Inhibitors and Flow rate on the Corrosion Resistance of Ductile Iron Pipes
5.1 Introduction
5.2 Study on the Difference of Microstructure and Corrosion Resistance
5.2.1 Experimental Materials and Methods
5.2.2 Material Structure Characterization Analysis
5.3 Corrosion Resistance Difference of Materials in Simulated Solution
5.3.1 Experimental Materials and Methods
5.3.1.1 Materials and Solutions
5.3.1.2 Electrochemical Test
5.3.2 Study on the Difference of Corrosion Resistance of Materials in the Environment without Corrosion Inhibitor
5.3.2.1 Open Circuit Potential Analysis
5.3.2.2 Polarization Curve Analysis
5.3.2.3 Electrochemical Impedance Spectroscopy Analysis
5.3.3 Effect of Environmental Factors on Corrosion Kinetics of Ball- milled Cast Iron in Corrosion Inhibitor-free Solution
5.3.4 Corrosion Resistance of Materials in Corrosion Inhibitor Environment
5.3.4.1 Corrosion Resistance of Three Materials under the Environment of Ethanolamine
5.3.4.2 Corrosion Resistance of Three Materials in the Environment of Sodium Hexametaphosphate Corrosion Inhibitor
5.4 Analysis of Corrosion Kinetics Process of Materials
5.4.1 Experimental Materials and Methods
5.4.1.1 Materials and Solutions
5.4.1.2 Immersion Test
5.4.1.3 Rust Layer Structure Characterization
5.4.2 Flow Rate Immersion Experiment
5.4.2.1 Flow Rate Immersion Experiment in Corrosion Inhibitor-free Environment
5.4.2.2 Flow Rate Immersion Experiment in Corrosion Inhibitor Environment
5.4.2.3 Rust Layer Analysis
5.5 Charter Summary
Chapter.6. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Rebar Corrosion Resistance Modulation
6.1 Introduction
6.2 Mechanism of the effect of Cr/RE modulation on the corrosion resistance of rebars in Cl- containing environments
6.2.1 Materials and methods
6.2.1.1 Experimental materials and solutions
6.2.1.2 Electrochemical testing
6.2.1.3 Corrosion immersion test
6.2.1.4 Surface corrosion morphology and oxide film analysis
6.2.2 Electrochemical properties of rebar in chlorine-containing environments
6.2.2.1 Potential polarization curves
6.2.2.2 Electrochemical impedance spectra
6.2.2.3 Mott-Schottky curves
6.2.3 Corrosion behavior of rebar in simulated concrete pore solution containing different concentrations of NaCl
6.2.3.1 Corrosion rate analysis
6.2.3.2 Corrosion morphology analysis
6.2.3.3 Localized corrosion behavior and Characteristics
6.2.3.4 Evolution of surface oxide film of Cr/RE modified rebar
6.2.4 Mechanism of the effect of Cr/RE modulation on the corrosion resistance of rebars in Cl-containing environments
6.3 Service performance characterization of low alloy rebar based on corrosion online monitoring technology
6.3.1 Materials and methods
6.3.1.1 Outdoor exposure test
6.3.1.2 Phase analysis of the rust layer
6.3.1.3 Corrosion big data online monitoring technology
6.3.2 Corrosion behaviour in outdoor service environments
6.3.2.1 Corrosion morphology
6.3.2.2 Outdoor corrosion data monitoring and analysis based on online corrosion monitoring
6.3.2.3 Corrosion mechanism of low alloy rebar
6.3.2.4 Corrosion resistance evaluation and design of steel reinforcement driven by online corrosion monitoring
6.4 Chapter summary
Chapter 7. Application of Novel Big Data Intelligent Corrosion Assessment Approach in blast furnace gas pipe steel corrosion resistance Analysis
7.1 Introduction
7.2 Thermodynamic analysis of corrosion resistance of blast furnace gas in complex environment
7.2.1 Materials and Methods
7.2.2 Degrees of freedom for mixed gas systems
7.2.3 Thermodynamic deduction and analysis of acid dew point temperature in blast furnace gas system
7.2.4 Corrosion thermodynamic calculation of pipe network materials
7.3 Electrochemical behaviour of Q235 carbon steel in Complex blast furnace gas Environments
7.3.1 Materials and Methods
7.3.1.1 Materials and Solution
7.3.1.2 Methods
7.3.2 Electrochemical analysis of Q235 in different media
7.3.2.1 Different Concentrations of Neutral NaCl Solutions
7.3.2.2 Different pH Values of Acidic 3.5% NaCl Solutions
7.3.2.3 Solution with different NaNO3 concentrations and 3.5 wt% NaCl solution with pH=1
7.3.2.4 Solution with different NaPO4 concentrations and 3.5 wt% NaCl solution with pH=1
7.3.2.5 Solution with different Na2SO3 concentrations and 3.5 wt% NaCl solution with pH=1
7.3.2.6 Solution with different Na2SO4 concentrations and 3.5 wt% NaCl solution with pH=1
7.3.2.7 Solution with different pH and different NaCl concentrations
7.3.2.8 Na2SO3 solutions at different pH+ concentrations
7.3.3 Correlation analysis of environmental factors to Q235
7.4 Comprehensive analysis of Q235 carbon steel corrosion failure of blast furnace gas pipeline
7.4.1 Materials and Methods
7.4.1.1 Materials
7.4.1.2 Methods
7.4.2 Material Corrosion failure Characteristics
7.4.2.1 Morphology analysis of failed pipe
7.4.2.2 Analysis of tissue components
7.4.2.3 Thickness loss rate
7.4.2.4 Analysis of corrosion products
7.4.2.5 Corrosion morphology and corrosion pit distribution characteristics
7.4.2.6 Physical and chemical analysis of field condensate
7.4.3 Failure cause and mechanism analysis
7.5 Application of corrosion big data techniques in determining failure factors of gas pipelines
7.5.1 Materials and Methods
7.5.1.1 Materials
7.5.1.2 Corrosion sensor test principle
7.5.1.3 In-pipe monitoring
7.5.1.4 Corrosion clock diagram
7.5.1.5 Cumulative charge quantity method
7.5.1.6 F index method
7.5.1.7 Machine learning method
7.5.2 On-line monitoring of corrosion patterns
7.5.2.1 Dynamic corrosion results
7.5.2.2 Dose response of corrosion
7.5.2.3. Accelerated corrosion by interaction
7.5.2.4 Simulated condensate water corrosion testing
7.5.2.5 Temperature driving effect
7.5.2.6 Corrosion monitoring via Bigdata technology
7.6 Chapter Summary
Chapter 8. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Corrosion-Resistant Low Alloy Steel Development
8.1 Introduction
8.2 3Cr Steel corrosion behavior in Tropical Marine Atmosphere: Effects of Mo and Sn Microalloying
8.2.1 Materials
8.2.1.1 Experimental Samples
8.2.1.2 Microstructure Characterization and Corrosion Test Methods
8.2.1.3 Work Function Calculation
8.2.2 Basic properties and corrosion behavior
8.2.2.1 Microstructure
8.2.2.2 Electrochemical Behavior
8.2.2.3 Corrosion Behavior in Simulated Tropical Marine Atmosphere
8.2.3 Mo and Sn on corrosion mechanisms
8.2.3.1 Influence of the Work Function
8.2.3.2 Role of Mo on Corrosion Resistance
8.2.3.3 Sn Alone on Corrosion Behavior of Low-Alloy Steel
8.2.3.4 Mo and Sn alloying on Corrosion Resistance of Low-Alloy Steel
8.3 Influence of Microstructure Differences on Corrosion Resistance
8.3.1 Materials
8.3.1.1 Experimental Materials
8.3.1.2 Wet/dry Cyclic Immersion Test Based on Corrosion Big Data
8.3.1.3. AFM Analysis
8.3.2 Basic performance and corrosion big data characteristics
8.3.2.1 Microstructure
8.3.2.2 Electrochemical Behavior
8.3.2.3 Corrosion Rates Analysis
8.3.2.4 AFM Analysis
8.3.3 Influence of microstructure changes on corrosion mechanisms
8.3.3.1 Role of Microstructure on Corrosion Resistance
8.3.3.2 Effect of Microstructural Differences on Rust Layer Evolution
8.4 Study on Corrosion Resistance Evaluation Model
8.4.1 Experimental Methods
8.4.1.1 Machine Learning Methods
8.4.1.2 Validation Test Methods
8.4.2 Corrosion big data modelling and corrosion resistant alloy development strategy
8.4.2.1 Pearson Correlation Analysis
8.4.2.2 Corrosion Rate Prediction Model and Importance Analysis
8.4.2.3 Pitting Depth Prediction Model and Importance Analysis
8.4.2.4 Analysis of Validation Test Results
8.5 Chapter Summary
Chapter 9. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Corrosion Prediction and Data Mining Modeling
9.1 Introduction
9.2 The Dose-response prediction function modeling method for corrosion, driven by big data technology
9.2.1 Materials and methods
9.2.2 Results
9.3 Method for corrosion prediction and data mining modeling driven by big data technology and machine learning for corrosion
9.3.1 Experiment
9.3.1.1 Materials
9.3.1.2 CCT Test
9.3.1.3 Data collection
9.3.1.4 Modeling
9.3.1.5 Partial dependence analysis
9.3.1.6 Evaluation methodology
9.3.2 Results
9.3.2.1 Test results
9.3.2.2 Corrosion decision model
9.3.2.3 Intricate relationships between environmental variables
9.3.2.4 Generalization and applicability of the CDM
9.3.2.5 Data-based examination of corrosion
9.4 Big Data-Powered Picture Recognition Technique for Predicting Atmospheric Corrosion
9.4.1 Experiment
9.4.1.1 Corrosion Acceleration Experiment
9.4.1.2 Data acquisition
9.4.1.3 Experimental characterization
9.4.2 Algorithm
9.4.2.1 LBP
9.4.3 Modeling
9.4.4 Results
9.4.4.1 Outcomes of picture segmentation
9.4.4.2 Correlation analysis
9.4.4.3 Corrosion Prediction by ICPM
9.5 Chapter Summary
Chapter 10. Perspectives on the Application of Artificial Intelligence in Investigating Corrosion Mechanisms of Steel and Designing Corrosion Resistant Alloys
10.1 Introduction
10.1.1 Economic losses caused by steel corrosion
10.1.2 Industrial Challenges
10.1.3 Industrial Challenges: Limitations of Traditional Corrosion Resistant Alloy Steel Research and Development
10.2 Key directions for AI driven corrosion-resistant steel alloy design
10.2.1 Feature extraction of cross scale representation data
10.2.2 Revealing the main controlling factors of corrosion at different levels
10.2.3 Integration of Molecular Dynamics Simulation and Machine Learning
10.2.4 Dynamic Monitoring and Intelligent Analysis of Corrosion Process
10.3 Challenges and Countermeasures
10.3.1 Multi source data fusion and compatibility
10.3.2 Analysis of Key Factors
10.3.3 Computing resources adapted to bottlenecks
10.3.4 Data Quality and Model Reliability
10.4 Conclusion
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