Chloride-induced corrosion of steel reinforcements is one of the main factors restricting the durability of reinforced concrete structures. Chromium (Cr) alloying is an effective strategy to enhance the corrosion resistance of steel. However, the appropriate Cr content for different environments remains undetermined. In this study, steels with three different Cr contents of 0, 5, and 10 wt.% were prepared. Electrochemical methods and physical characterization techniques were used to investigate the effects of Cr content on the passive film and corrosion behavior of steels in a simulated concrete pore solution under chloride attack. The results show that Cr alloying increases the critical chloride concentration for steel depassivation, passive film resistance, and charge transfer resistance. Specifically, the critical chloride concentrations of 0Cr, 5Cr, and 10Cr are 0.63, 0.81, and 1.56 mol/L, respectively. In a simulated pore solution with 0.6 mol/L chloride, the charge transfer resistances of 0Cr, 5Cr, and 10Cr are 4.1, 5.8, and 63.4 × 10⁵ Ω·cm², respectively, corresponding to corrosion rates that are 1.39- and 15.31-times lower for 5Cr and 10Cr relative to 0Cr. Therefore, in concrete exposed to marine chloride attacks, the use of high Cr alloying is necessary. Although the cost increases and the weldability deteriorates, the improvement in corrosion resistance is far superior to that of medium Cr alloying. The excellent corrosion resistance of high-Cr steel stems from its passive film mainly composed of stable Cr₂O₃ with a lower oxygen vacancy defect density, while that of 5Cr is dominated by less stable Cr(OH)₃, which weakens the corrosion resistance of the passive film.

Measuring the fatigue crack growth rate via the crack growth experiment (a-N curve) is labor-intensive and time-consuming. A machine learning interpolation–extrapolation strategy (MLIES) aimed at enhancing the prediction accuracy of small-data models has been proposed to accelerate fatigue testing. Two specific approaches are designed by transforming a-N curve data from N to ΔN and from a to Δa (S1)/Δa/ΔN (S2) to enrich the data volume and leverage the incremental information. Thus, a simple and fast-responding single-layer neural network model can be trained based on the early-stage data points from fatigue testing and accurately predict the remaining part of an a-N curve, thereby enhancing the experimental efficiency. Through exponential data expansion and data augmentation, the trained neural network model is able to learn the underlying rules governing crack growth directly from the experimental data, requiring no explicit analytical crack growth laws. The proposed MLIES was validated on fatigue tests for aluminum alloy and titanium alloy samples under different experimental parameters. Results demonstrate its effectiveness in reducing testing time/cost by 15–32% while achieving over 30% higher prediction accuracy for the a-N curve compared to a traditional machine learning modeling approach. Our research offers a data-driven recipe for accurate crack growth prediction and accelerated fatigue testing.

The marine atmospheric environment, characterized by a high chloride content, high humidity, and a high corrosion rate of structural steel, urgently demands anticorrosion methods that are compatible with other technologies such as alloy steel and organic coatings. In this study, an inhibitor-loaded conversion film was grown in situ on a steel surface to prevent chloride-induced corrosion. Specifically, a Mg-Fe layered double hydroxide (LDH) conversion film was grown on the steel surface, and p-aminobenzoate (pAB⁻) inhibitor ions were intercalated into the LDH. After half a year of natural corrosion in the actual marine atmospheric environment, the average corrosion rate of steel with the inhibitor@LDH film was 36.20 µm/a, which was 21% lower than that of the steel substrate (45.91 µm/a). The inhibitor@LDH film also effectively suppressed local pitting corrosion, with the density of corrosion pits significantly reduced by 64%. Furthermore, the inhibitor@LDH film promoted the formation of a denser and thinner rust layer on the steel surface, with a smaller crack width, fewer cracks, and an increased α-FeOOH/γ-FeOOH ratio. In summary, the inhibitor@LDH conversion film inhibits general and local corrosion on steel in a marine atmospheric environment.