Search Results (498)

The anodic oxidation of titanium implants is a practical, cost-effective method to enhance implant success, especially due to rising hypersensitivity concerns. This study investigated the electrochemical behavior, surface characteristics, and biocompatibility of anodized commercially pure titanium (cpTi, grade IV). Anodization is performed on polished, cleaned cpTi sheet samples in 1 M H₂SO₄ using a constant voltage of 15 V for 15 and 45 min. The color of the oxide layer is evaluated using the CIELab color space, while composition is analyzed by a scanning electron microscope (SEM) equipped with an energy dispersive spectrometer (EDS). Additionally, X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) are performed to identify and monitor the phase transformations of the formed titanium oxides. Corrosion measurements are performed in 9 g L⁻¹ NaCl, pH = 7.4, and show the excellent corrosion stability of the anodized samples in comparison with pure titanium. The biological response is assessed by determining mitochondrial activity and gene expression in human fibroblasts. Anodized surfaces, particularly Ti-45, promote higher mitochondrial activity and the upregulation of adhesion-related genes (N-cadherin and Vimentin) in human gingival fibroblasts, indicating improved biocompatibility and the potential for enhanced early soft tissue integration. Full article

This paper develops a novel design formula to estimate the residual strength of corroded stiffened cylindrical structures. It extends a previously established ultimate strength formulation for intact cylinders by introducing a corrosion-induced strength reduction factor. The foundational formula considers failure mode interactions like yielding, local buckling, overall buckling, and stiffener tripping. This research utilizes recent experimental and numerical investigations on corroded ring-stiffened cylinder models. Experimental results validate the numerical analysis method, showing good agreement in collapse pressures (2–4% difference) and shapes. The validated numerical method is then subject to an extensive parametric study, systematically varying corrosion characteristics. Results indicate a clear relationship between corrosion volume and strength reduction, with overall buckling being more sensitive. Based on these comprehensive results, a new empirical strength reduction factor (${\rho }_c$) is derived as a function of the corrosion volume ratio ($V_{non}$). This factor is integrated into the existing ultimate strength formula, allowing direct residual strength estimation for corroded structures. The proposed formula is rigorously verified against experimental and numerical data, showing excellent agreement (mean 1.00, COV 5.86%). This research provides a practical, accurate design tool for assessing the integrity and service life of corroded stiffened cylindrical structures. Full article

Underwater inspection images of hydraulic structures often suffer from haze, severe color distortion, low contrast, and blurred textures, impairing the accuracy of automated crack, spalling, and corrosion detection. However, many existing enhancement methods fail to preserve structural details and suppress noise in turbid environments. To address these limitations, we propose a compact image enhancement framework called Wavelet Fusion with Sobel-based Weighting (WWSF). This method first corrects global color and luminance distributions using multiscale Retinex and gamma mapping, followed by local contrast enhancement via CLAHE in the L channel of the CIELAB color space. Two preliminarily corrected images are decomposed using discrete wavelet transform (DWT); low-frequency bands are fused based on maximum energy, while high-frequency bands are adaptively weighted by Sobel edge energy to highlight structural features and suppress background noise. The enhanced image is reconstructed via inverse DWT. Experiments on real-world sluice gate datasets demonstrate that WWSF outperforms six state-of-the-art methods, achieving the highest scores on UIQM and AG while remaining competitive on entropy (EN). Moreover, the method retains strong robustness under high turbidity conditions (T ≥ 35 NTU), producing sharper edges, more faithful color representation, and improved texture clarity. These results indicate that WWSF is an effective preprocessing tool for downstream tasks such as segmentation, defect classification, and condition assessment of hydraulic infrastructure in complex underwater environments. Full article

This study explores the use of embedded piezo sensor (EPS) employing the Electro-Mechanical Impedance (EMI) technique for real-time corrosion monitoring in reinforced E-waste concrete exposed to chloride-laden environments. With the growing environmental concerns over electronic waste (E-waste) and the demand for sustainable construction practices, printed circuit board (PCB) materials were incorporated as partial replacements for coarse aggregates in concrete. The experiment utilized M30-grade concrete mixes, substituting 15% of natural coarse aggregates with E-waste, aiming to assess both sustainability and structural performance without compromising durability. EPS configured with Lead Zirconate Titanate (PZT) patches were embedded into both conventional and E-waste concrete specimens. The EPS monitored the changes in the form of conductance and susceptance signatures across a 100–400 kHz frequency range during accelerated corrosion exposure over a 60-day period in a 3.5% NaCl solution. The corrosion progression was evaluated qualitatively through electrical impedance signatures, visually via rust formation and cracking, and quantitatively using the Root Mean Square Deviation (RMSD) of EMI signatures. The results showed that the EMI technique effectively captured the initiation and propagation stages of corrosion. E-waste concrete exhibited earlier and more severe signs of corrosion compared to conventional concrete, indicated by faster increases and subsequent declines in conductance and susceptance and higher RMSD values during the initiation phase. The EMI-based system demonstrated its capability to detect microstructural changes at early stages, making it a promising method for Structural Health Monitoring (SHM) of sustainable concretes. The study concludes that while the use of E-waste in concrete contributes positively to sustainability, it may compromise long-term durability in aggressive environments. However, the integration of EPS and EMI offers a reliable, non-destructive, and sensitive technique for real-time corrosion monitoring, supporting preventive maintenance and improved infrastructure longevity. Full article

Corrosion, combined with cyclic loading, is inevitable and becomes a challenging problem, even when inherently corrosion-protected materials have been selected and applied based on established in-house experience. Aero engine mount structures are exposed to dusty and salty environmental conditions during both operational and non-operational periods. It is becoming tough to predict the remaining useful corrosion fatigue life due to the unascertainable material strength degradations under service conditions. As such, a rationalized approach is currently being used to assess their structural integrity, which produces more wastages of the flying parts. This paper presents a novel approach for predicting corrosion fatigue by proposing a random-parameter model in combination with validated experimental data. The two-random-parameter model is employed here with the probability method to determine the time-independent corrosion fatigue life of a magnesium structural casting, which is used heavily in engine front-mount aircraft systems. This is also correlated with experimental data from the literature, validating the proposed stochastic corrosion fatigue model that addresses the technical variances that occur during service to increase optimal mount structure usage using an automated data system. Full article

Background and Objectives: Magnesium (Mg)-based materials, such as the WE43 alloy, show potential in biomedical applications owing to their advantageous mechanical properties and biodegradability; however, their quick corrosion rate and hydrogen release restrict their general clinical utilization. This study aimed to develop a novel Mg-Zn-Ca alloy system based on WE43 alloy, evaluating the influence of Zn and Ca additions on microstructure, mechanical properties, cytocompatibility, and electrochemical behavior for potential use in biodegradable orthopedic applications. Materials and Methods: The WE43-Zn-Ca alloy system was developed by alloying standard WE43 (Mg–Y–Zr–RE) with 1.5% Zn and Ca concentrations of 0.2% (WE43_0.2Ca alloy) and 0.3% (WE43_0.3Ca alloy). Microstructural analysis was performed utilizing scanning electron microscopy (SEM) in conjunction with energy-dispersive X-ray spectroscopy (EDS), while the chemical composition was validated through optical emission spectroscopy and X-ray diffraction (XRD). Mechanical properties were assessed through tribological tests. Electrochemical corrosion behavior was evaluated using potentiodynamic polarization in a 3.5% NaCl solution. Cytocompatibility was assessed in vitro on MG63 cells using cell viability assays (MTT). Results: Alloys WE43_0.2Ca and WE43_0.3Ca exhibited refined, homogeneous microstructures with grain sizes between 70 and 100 µm, without significant structural defects. Mechanical testing indicated reduced stiffness and an elastic modulus similar to human bone (19.2–20.3 GPa), lowering the risk of stress shielding. Cytocompatibility tests confirmed non-cytotoxic behavior for alloys WE43_0.2Ca and WE43_0.3Ca, with increased cell viability and unaffected cellular morphology. Conclusions: The study validates the potential of Mg-Zn-Ca alloys (especially WE43_0.3Ca) as biodegradable biomaterials for orthopedic implants due to their favorable combination of mechanical properties, corrosion resistance, and cytocompatibility. The optimization of these alloys contributed to obtaining an improved microstructure with a reduced degradation rate and a non-cytotoxic in vitro outcome, which supports efficient bone tissue regeneration and its integration into the body for complex biomedical applications. Full article

Steel corrosion prediction in concrete infrastructure remains a critical challenge for durability assessment and maintenance planning. This study presents a comprehensive framework integrating domain expertise with advanced machine learning for carbonation-induced corrosion prediction. Four Gaussian Process Regression (GPR) variants were systematically developed: Baseline GPR with manual optimization, Expert Knowledge GPR employing domain-driven dual-kernel architecture, GPR with Automatic Relevance Determination (GPR-ARD) for feature selection, and GPR-OptCorrosion featuring specialized multi-component composite kernels. The models were trained and validated using 180 carbonated mortar specimens with 15 systematically categorized variables spanning mixture, material, environmental, and electrochemical parameters. GPR-OptCorrosion achieved superior performance (R² = 0.9820, RMSE = 1.3311 μA/cm²), representing 44.7% relative improvement in explained variance over baseline methods, while Expert Knowledge GPR and GPR-ARD demonstrated comparable performance (R² = 0.9636 and 0.9810, respectively). Contrary to conventional approaches emphasizing electrochemical indicators, automatic relevance determination revealed supplementary cementitious materials (silica fume and fly ash) as dominant predictive factors. All advanced models exhibited excellent generalization (gaps < 0.02) and real-time efficiency (<0.006 s), with probabilistic uncertainty quantification enabling risk-informed infrastructure management. This research contributes to advancing machine learning applications in corrosion engineering and provides a foundation for predictive maintenance strategies in concrete infrastructure. Full article

To achieve rapid detection of stainless steel passivation quality, a time-domain impedance method was investigated based on a potential step transient with a portable three-electrode probe. A comparison of the effects of signal analysis and transient parameters was conducted, and the results were compared with those obtained in a bulk solution with a general three-electrode system. The measured transient current with the probe offered a higher signal-to-noise ratio, with minimal deviation from the frequency-domain impedance observed at a step amplitude of 0–100 mV. Measurements using the three-electrode probe under different stabilization times indicated that, after 30 s of stabilization, the measurement deviation was less than 1%, enabling a rapid assessment. Comparative testing of surfaces with varying passivation quality revealed that the pitting potential increases with increasing time-domain impedance, demonstrating the method’s capability to distinguish passivated surfaces with different corrosion resistances. Full article

This study systematically investigated the effects of biologically relevant microalloying elements—calcium (Ca), strontium (Sr), manganese (Mn), and zirconium (Zr)—on the electrochemical behavior of Mg-Y-Zn alloys containing long-period stacking ordered (LPSO) phases. The alloys were prepared by casting and characterized using X-ray diffraction (XRD), optical microscopy (OM), and scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS). Electrochemical properties were assessed through potentiodynamic polarization in Hank’s solution, and corrosion rates were determined by hydrogen evolution and weight loss methods. Microalloying significantly enhanced the corrosion resistance of the base Mg-Y-Zn alloy, with corrosion rates decreasing from 2.67 mm/year (unalloyed) to 1.65 mm/year (Ca), 1.36 mm/year (Sr), 1.18 mm/year (Zr), and 1.02 mm/year (Mn). Ca and Sr additions introduced Mg₂Ca and Mg₁₇Sr₂, while Mn and Zr refined the existing LPSO structure without new phases. Sr refined the LPSO phase and formed a uniformly distributed Mg₁₇Sr₂ network, promoting uniform corrosion and suppressing deep localized attacks. Ca-induced Mg₂Ca acted as a temporary sacrificial phase, with corrosion eventually propagating along LPSO interfaces. The Mn-containing alloy exhibited the lowest corrosion rate; this is attributed to the suppression of both anodic and cathodic reaction kinetics and the formation of a stable protective surface film. Zr improved general corrosion resistance but increased susceptibility to localized attacks due to dislocation-rich zones. These findings elucidate the corrosion mechanisms in LPSO-containing Mg alloys and offer an effective strategy to enhance the electrochemical stability of biodegradable Mg-based implants. Full article

Considering the harsh marine environment characterized by dry–wet cycles, freeze–thaw action, chloride penetration, and sulfate attack, four optimized ultra-high-performance concrete (UHPC) mix designs were developed. Durability was assessed via electric flux, dry–wet cycles, and rapid freeze–thaw tests to evaluate the effects of curing methods, aggregate types, and mineral admixtures on key durability indicators, including chloride ion permeability, compressive strength loss, and mass loss. Scanning electron microscopy (SEM) examined microstructural changes under various conditions. Results showed that curing method significantly affected chloride ion permeability and sulfate resistance. High-temperature curing (70 ± 2 °C) reduced 28-day chloride ion electric flux by about 50%, and the compressive strength loss rate of specimens subjected to sulfate attack decreased by 2.7% to 45.7% compared to standard curing. Aggregate type had minimal impact on corrosion resistance, while mineral admixtures improved durability more effectively. Frost resistance was excellent, with mass loss below 0.87% after 500 freeze–thaw cycles. SEM analysis revealed that high-temperature curing decreased free cement particles, and mineral admixtures refined pore structure, enhancing matrix compactness. Among all mixtures, Mix Proportion 4 demonstrated the best overall durability. This study offers valuable insights for UHPC design in aggressive marine conditions. Full article

The utilization of geothermal resources as renewable energy is a subject of interest for the regions that possess these resources. The exploitation of geothermal energy must consider local geological conditions and an integrated approach, which should include practical studies on the chemistry of geothermal waters and their effect on thermal installations. Geothermal waters from Bihor County, Romania, have a variable composition, depending on the crossed geological layers, but also on pressure and temperature. Obviously, water transport and heat transfer are involved in all applications of geothermal waters. This article aims to characterize certain geothermal waters from the point of view of composition and corrosion if used as a thermal agent. Atomic absorption spectroscopy (AAS) and UV–Vis spectroscopy were employed to analyze water specimens. Chemical composition includes calcite (CaCO₃), chalcedony (SiO₂), goethite (FeO(OH)), and magnetite (Fe₃O₄), which confirms the corrosion and scale potential of these waters. Corrosion resistance of mild carbon steel, commonly used as pipe material, was studied by the gravimetric method and through electrochemical methodologies, including chronoamperometry, electrochemical impedance spectroscopy (EIS), potentiodynamic polarization method, and open circuit potential measurement (OCP). Statistical analysis shows that the medium corrosion rate of S235 steel, expressed as penetration rate, is between 0.136 mm/year to 0.615 mm/year. The OCP, EIS, and chronoamperometry experiments explain corrosion resistance through the formation of a passive layer on the surface of the metal. This study proposes an innovative methodology and a systematic algorithm for analyzing chemical processes and corrosion phenomena in geothermal installations, emphasizing the necessity of individualized assessments for each aquifer to optimize operational parameters and ensure sustainable resource utilization. Full article

Corrosion is a critical issue in civil structures, significantly affecting their durability and functionality. Detecting corrosion at an early stage is essential to prevent structural failures and ensure safety. This study proposes an expert system based on a novel methodology for corrosion detection using vibration signal analysis. The approach employs graphical empirical mode decomposition (GEMD) to decompose vibration signals into their intrinsic mode functions, extracting relevant structural features. These features are then transformed into grayscale images and classified using a Convolutional Neural Network (CNN) to automatically differentiate between a healthy structure and one affected by corrosion. To enhance the computational efficiency of the method without compromising accuracy, different CNN architectures and image sizes are tested to propose a low-complexity model. The proposed approach is validated using a 3D nine-bay truss-type bridge model encountered in the Vibrations Laboratory at the Autonomous University of Querétaro, Mexico. The evaluation considers three different corrosion levels: (1) incipient, (2) moderate, and (3) severe, along with a healthy condition. The combination of GEMD and CNN provides a highly accurate corrosion detection framework that achieves 100% classification accuracy while remaining effective regardless of the damage location and severity, making it a reliable tool for early-stage corrosion assessment that enables timely maintenance and enhances structural health monitoring to improve the long life and safety of civil structures. Full article

Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there is growing demand for accurate and early corrosion prediction methods. Traditional standards for assessing atmospheric corrosivity depend on long-term empirical data, limiting their usefulness during the design stage of infrastructure projects. To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. Our models were trained on a comprehensive dataset that included variables such as land coverage, wind speed, and orientation. Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). This approach not only highlights the most influential environmental variables driving chloride deposition but also offers a scalable and cost-effective solution to support corrosion monitoring and structural life assessment in coastal infrastructure. Full article

This study systematically investigates the degradation and failure prediction of pipeline materials in sulfur-containing environments, with a particular focus on X52 pipeline steel exposed to high-sulfur environments. Through uniaxial tensile tests to assess mechanical properties, it was found that despite surface corrosion and a reduction in overall structural load-bearing capacity, the intrinsic mechanical properties of X52 steel did not exhibit significant degradation and remained within standard ranges. The Johnson–Cook constitutive model was developed to accurately capture the material’s plastic behavior. Subsequently, a genetic algorithm-optimized backpropagation (GABP) neural network was employed to predict the failure pressure of defective pipelines and the corrosion rate in acidic environments, with prediction errors controlled within 5%. By integrating the GABP model with NACE standard methods, a framework for predicting the remaining service life for in-service pipelines operating in sour environments was established. This method provides a novel and reliable approach for pipeline integrity assessment, demonstrating significantly higher accuracy than traditional empirical models and finite element analysis. Full article

Polyethylene storage tanks are widely used for storing water and chemicals due to their lightweight and corrosion-resistant properties. Despite these advantages, their structural performance under seismic conditions remains a concern, mainly because of their low mechanical strength and weak bonding characteristics. In this study, a method of external strengthening using fiber-reinforced polymer (FRP) laminates is proposed and explored. The research involves a combination of laboratory testing on carbon fiber-reinforced polymer (CFRP)-strengthened polyethylene strips and finite element simulations aimed at assessing bond strength, anchorage length, and structural behavior. Results from tensile tests indicate that slippage tends to occur unless the anchorage length exceeds approximately 450 mm. To evaluate surface preparation, grayscale image analysis was used, showing that mechanical sanding increased intensity variation by over 127%, pointing to better bonding potential. Simulation results show that unreinforced tanks under seismic loads display stress levels beyond their elastic limit, along with signs of elephant foot buckling—common in thin-walled cylindrical structures. Applying CFRPs in a full-wrap setup notably reduced these effects. This approach offers a viable alternative to full tank replacement, especially in regions where cost, access, or operational constraints make replacement impractical. The applicability is particularly valuable in seismically active and densely populated areas, where rapid, non-invasive retrofitting is essential. Based on the experimental findings, a simple formula is proposed to estimate the anchorage length required for effective crack repair. Overall, the study demonstrates that CFRP retrofitting, paired with proper surface treatment, can significantly enhance the seismic performance of polyethylene tanks while avoiding costly and disruptive replacement strategies. Full article