Search Results (193)

Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, especially under coatings. This paper critically examines these challenges and explores the potential of Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) to form the basis of improved inspection approaches. We discuss LiDAR’s utility for accurate 3D mapping and providing a spatial framework and HSI’s potential for objective material identification and surface characterization based on spectral signatures along a wavelength range of 400-1000nm (visible and near infrared). Preliminary findings from laboratory tests are presented, demonstrating the basic feasibility of HSI for differentiating surface conditions (corrosion, coatings, bare metal) and relative coating thickness, alongside LiDAR’s capability for detailed geometric capture. Although these results do not represent a deployable system, they highlight how LiDAR and HSI could address key limitations of current practices and suggest promising directions for future research into integrated sensor-based corrosion assessment strategies. Full article

Chloride ion penetration is one of the most aggressive threats to reinforced concrete, as it triggers the electrochemical corrosion of steel reinforcement, compromising structural integrity and durability. Chloride ingress occurs through the porous structure of concrete, making permeability control crucial for enhancing structural longevity. Fiber-reinforced concrete (FRC) is widely used to improve durability; however, the effects of different fiber types on chloride resistance remain unclear. This study examines the influence of glass and polypropylene fibers on concrete’s microstructure and chloride penetration resistance. Cylindrical specimens were prepared, including a reference mix without fibers and mixes with 0.25% and 0.50% fiber content by volume. Both fiber types were tested for chloride resistance. The accelerated non-steady-state migration method was employed to determine the resistance coefficients to chloride ion penetration, while X-ray macrofluorescence (MacroXRF) mapped the chlorine infiltration depth in the samples. Compressive strength decreased in all fiber-reinforced samples, with 0.50% glass fiber leading to a 56% reduction in strength. Nevertheless, the XRF results showed that a 0.25% fiber content significantly reduced chloride penetration, with polypropylene fibers outperforming glass fibers. These findings highlight the critical role of fiber type and volume in improving concrete durability, offering insights for designing long-lasting FRC structures in chloride-rich environments. Full article

The corrosion behavior of an additively manufactured Mg-1Zn alloy was investigated in both the transverse and longitudinal directions relative to the build direction, in the as-built condition and after annealing at 350 °C for 24 h under high vacuum. Microstructural characterization using XRD and SEM revealed the presence of magnesium oxide (MgO) and the absence of intermetallic second-phase particles. Optical microscopy (OM) images and Electron Backscatter Diffraction (EBSD) maps showed a highly complex grain morphology with anomalous, anisotropic shapes and a heterogeneous grain size distribution. The microstructure includes grains with a pronounced columnar morphology aligned along the build direction and is therefore characterized by a strong crystallographic texture. Electrochemical techniques, including PDP and EIS, along with gravimetric H₂ collection, concluded that the transverse plane exhibited greater corrosion resistance compared to the longitudinal plane. Additionally, an increase in cathodic kinetics was observed when comparing as-built with heat-treated samples. 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 paper evaluates the time-dependent fragility and post-earthquake residual seismic performance of existing steel frame columns in offshore atmospheric environments. Based on experimental research, the seismic failure mechanism and deterioration laws of the seismic behavior of corroded steel frame columns were revealed. A finite element analysis (FEA) method for steel frame columns, which considers corrosion damage and ductile metal damage criteria, is developed and validated. A parametric analysis in terms of service age and design parameters is conducted. Considering the impact of environmental erosion and aging, a classification criterion for damage states for existing steel frame columns is proposed, and the theoretical characterization of each damage state is provided based on the moment-rotation skeleton curves. Based on the test and numerical analysis results, probability distributions of the fragility function parameters (median and logarithmic standard deviation) are constructed. The evolution laws of the fragility parameters with increasing service age under each damage state are determined, and a time-dependent fragility model for existing steel frame columns in offshore atmospheric environments is presented through regression analysis. At a drift ratio of 4%, the probability of complete damage to columns with 40, 50, 60, and 70-year service ages increased by 18.1%, 45.3%, 79.2%, and 124.5%, respectively, compared with columns within a 30-year service age. Based on the developed FEA models and the damage class of existing columns, the influence of characteristic variables (service age, design parameters, and damage level) on the residual seismic capacity of earthquake-damaged columns, namely the seismic resistance that can be maintained even after suffering earthquake damage, is revealed. Using the particle swarm optimization back-propagation neural network (PSO-BPNN) model, nonlinear mapping relationships between the characteristic variables and residual seismic capacity are constructed, thereby proposing a residual seismic performance evaluation model for existing multi-aged steel frame columns in an offshore atmospheric environment. Combined with the damage probability matrix of the time-dependent fragility, the expected values of the residual seismic capacity of existing multi-aged steel frame columns at a given drift ratio are obtained directly in a probabilistic sense. The results of this study lay the foundation for resistance to sequential earthquakes and post-earthquake functional recovery and reconstruction, and provide theoretical support for the full life-cycle seismic resilience assessment of existing steel structures in earthquake-prone areas. Full article

The modeling of the corrosion rate of Alloy 600 in primary water stress corrosion cracking conditions (PWSCC) is a challenging task for existing as well as new structures due to the wide deviation of its composition across the worldwide PWSCC environment. The major parameters influencing the rate are temperature, stress intensity factor, pH, conductivity, ECP, Yield strength, B₃(OH)₃, and LiOH. The individual effects of these parameters on corrosion are known to some extent; however, the combined effect of these parameters together is complex, nonlinear, and unpredictable. Herein, we developed an Artificial Neural Network to predict the corrosion crack growth rate for any combination of the above five parameters and to better understand the effects of these parameters jointly on corrosion behavior. Three-dimensional mappings clearly reveal the complex interrelationship between the temperature and stress intensity factor at different variables, and the effect of the variables rather than a single variable on the corrosion rate of Inconel alloy 600 in PWSCC conditions. Moreover, the index of relative importance for these variables has also been presented providing deep insights for anti-corrosion coating designs in PWSCC environments. Full article

Corrosion, a naturally occurring process leading to the deterioration of metallic materials, demands diligent detection for quality control and the preservation of metal-based objects, especially within industrial contexts. Traditional techniques for corrosion identification, including ultrasonic testing, radiographic testing, and magnetic flux leakage, necessitate the deployment of expensive and bulky equipment on-site for effective data acquisition. An unexplored alternative involves employing lightweight, conventional camera systems and state-of-the-art computer vision methods for its identification. In this work, we propose a complete system for semi-automated corrosion identification and mapping in industrial environments. We leverage recent advances in three-dimensional (3D) point-cloud-based methods for localization and mapping, with vision-based semantic segmentation deep learning techniques, in order to build semantic–geometric maps of industrial environments. Unlike the previous corrosion identification systems available in the literature, which are either intrusive (e.g., electrochemical testing) or based on costly equipment (e.g., ultrasonic sensors), our designed multi-modal vision-based system is low cost, portable, and semi-autonomous and allows the collection of large datasets by untrained personnel. A set of experiments performed in relevant test environments demonstrated quantitatively the high accuracy of the employed 3D mapping and localization system, using a light detection and ranging (LiDAR) device, with less than $0.05$ m and $0.02$ m average absolute and relative pose errors. Also, our data-driven semantic segmentation model was shown to achieve $70\%$ precision in corrosion detection when trained with our pixel-wise manually annotated dataset. Full article

The maritime transport of liquefied gases poses significant safety and environmental hazards such as fire, explosion, toxic gas emissions, and air pollution. The main objective of this study was to systematically identify, analyze, and prioritise the potential risks associated with the operation of liquefied gas tankers using a hybrid methodological framework. This framework integrates Fuzzy Delphi, Fuzzy DEMATEL, and Fault Tree Analysis (FTA) techniques to provide a comprehensive risk assessment. Initially, 20 key risk factors were identified through expert consensus using the Fuzzy Delphi method. The causal relationships between these factors were then assessed using Fuzzy DEMATEL to understand their interdependencies. Based on these results, accident probabilities were further analyzed using FTA modelling. The results show that fires, explosions, and large gas leaks are the most serious threats. Equipment failures—often caused by corrosion and operational errors by crew members—are also significant contributors. In contrast, cyber-related risks were found to be of lower criticality. The study highlights the need for improved crew training, rigorous inspection mechanisms, and the implementation of robust preventive risk controls. It also suggests that the prioritisation of these risks may need to be reevaluated as autonomous ship technologies become more widespread. By mapping the interrelated structure of operational hazards, this research contributes to a more integrated and strategic approach to risk management in the LNG/LPG shipping industry. Full article

Steel pipes, while essential for modern infrastructure due to their high strength and load-bearing capacity, are prone to corrosion in the marine environment, leading to material degradation, compromised structural integrity, and elevated safety risks and economic losses. In this study, distributed fiber-optic sensors were deployed on steel pipe surfaces to monitor corrosion in the splash zone (a region particularly vulnerable to cyclic wet–dry conditions). The sensors were engineered to withstand aggressive marine exposure. Strain variations induced by expansive corrosion products were detected via the fiber-optic array and used to calculate localized mass loss. Color-coded corrosion severity maps were generated to visualize the non-uniform corrosion distribution. Experimental results demonstrate that sensor-derived mass loss values align with 3D laser scanning measurements, validating the operational efficacy of distributed fiber-optic sensing for marine corrosion monitoring. This approach provides quantitative insights into the field applicability of optical sensing in structural health monitoring. Full article

The biocompatibility and long-term stability of stainless steel orthodontic devices are critically influenced by their corrosion resistance in the oral environment. This study evaluates the effect of three artificial saliva formulations—Afnor (pH 7.64), Fletcher (pH 8.07, fluoride-containing), and Fusayama/Meyer (pH 6.34, acidic)—on the surface integrity and chemical behavior of 316L stainless steel over 7 and 28 days. A multi-technique approach was employed, including SEM imaging, EDX elemental mapping, XRF analysis, microhardness testing (Vickers), and the monitoring of key physico-chemical parameters (pH, conductivity, salinity, and TDS). The results indicate that Afnor saliva maintains alloy stability with minimal surface damage while Fusayama/Meyer promotes pitting corrosion and selective leaching of Fe and Ni. Fletcher saliva led to the formation of crystalline corrosion products and significant surface hardening, likely due to the interaction of fluoride with the passive layer. Microhardness values increased across all samples after 28 days, most notably in the Fletcher condition (from 191.3 HV to 256.9 HV). These findings provide valuable insights into the time-dependent degradation mechanisms of orthodontic stainless steel in varied salivary environments, emphasizing the importance of simulating realistic oral conditions in corrosion testing. The study contributes to the optimization of material selection and surface treatment strategies for improved biocompatibility in dental applications. Full article

Cracks due to stress corrosion cracking in stainless steels are becoming a problem not only in boiling water reactors but also in pressurized water reactor nuclear plants. Stress improvement measures have been implemented mainly for large-bore welded piping, but in the case of small-bore welded piping, post-welding stress improvement measures are often not possible due to dimensional restrictions, etc. Therefore, knowing the actual welding residual stresses of small-bore welded piping regardless of reactor type is essential for the safe and stable operation of nuclear power stations, but there are only a limited number of examples of measuring the residual stresses. In this study, austenitic stainless steel pipes with an outer diameter of 100 mm and a wall thickness of 11.1 mm were butt-welded. The residual stresses were measured by the strain scanning method using neutrons. Furthermore, to obtain detailed residual stresses near the penetration bead where the maximum stress is generated, the residual stresses near the inner surface of the weld were measured using the double-exposure method (DEM) with hard X-rays of synchrotron radiation. A method using a cross-correlation algorithm was proposed to determine the accurate diffraction angle from the complex diffraction patterns from the coarse grains, dendritic structures, and plastic zones. A quantum beam hybrid method (QBHM) was proposed that uses the circumferential residual stresses obtained by neutrons and the residual stresses obtained by the double-exposure method in a complementary use. The residual stress map of welded piping measured using the QBHM showed an area where the axial tensile residual stress exists from the neighborhood of the penetration bead toward the inside of the welded metal. This result could explain the occurrence of stress corrosion cracking in the butt-welded piping. A finite element analysis of the same butt-welded piping was performed and its results were compared. There is also a difference between the simulation results of residual stress using the finite element method and the measurement results using the QBHM. This difference is because the measured residual stress map also includes the effect of the stress of each crystal grain based on elastic anisotropy, that is, residual micro-stress. Full article

Typical metal equipment in substations is exposed to high-temperature, high-humidity, and high-salt environments for a long time, and surface corrosion is a serious threat to operational safety. Traditional manual inspection is limited by the complexity of the environment and subjective assessment errors, and there is an urgent need for a method that can quickly and accurately locate the corrosion area and assess the degree of corrosion. In this paper, based on YOLOv8, the feature extraction ability is improved by introducing the attention mechanism; a mixed-mixed-sample data augmentation algorithm is designed to increase the diversity of data; and a cosine annealing learning rate adjustment is adopted to improve the training efficiency. The corrosion process of metal materials is accelerated by a neutral salt spray test in order to collect corrosion samples at different stages and establish a dataset, and a model of a corrosion-state recognition algorithm for typical equipment in substations based on an improved YOLOv8 model is established. Finally, based on ablation experiments and comparison experiments, performance analyses of multiple algorithmic models are conducted for horizontal and vertical comparisons in order to verify the effectiveness of the improved method and the superiority of the models in this paper. The experiments verify that the improved model is comprehensively leading in multi-dimensional indicators: the mAP reaches 96.3% and the F1 score reaches 93.6%, which is significantly better than mainstream models such as Faster R-CNN, and provides a reliable technical solution for the intelligent inspection of substation equipment. Full article

Small defects on the surface of copper strips have a significant impact on key properties such as electrical conductivity and corrosion resistance, and existing inspection techniques struggle to meet the demand in terms of accuracy and generalisability. Although there have been some studies on metal surface defect detection, there is a relative lack of research on highly reflective copper strips. In this paper, a lightweight and efficient copper strip defect detection algorithm, SC-AttentiveNet, is proposed, aiming to solve the problems of the large model size, slow speed, insufficient accuracy and poor generalisability of existing models. The algorithm is based on ConvNeXt V2, and combines the SCDown module and group normalisation to design the SCGNNet feature extraction network, which significantly reduces the computational overhead while maintaining excellent feature extraction capability. In addition, the algorithm introduces the SPPF-PSA module to enhance the multi-scale feature extraction capability, and constructs a new neck feature fusion network via the HD-CF Fusion Block module, which further enhances the feature diversity and fine granularity. The experimental results show that SC-AttentiveNet has a mAP of 90.11% and 64.14% on the KUST-DET and VOC datasets, respectively, with a parameter count of only 6.365 MB and a computational complexity of 14.442 GFLOPs. Tests on the NEU-DET dataset show that the algorithm has an excellent generalisation performance, with a mAP of 76.41% and a detection speed of 78 FPS, demonstrating a wide range of practical application potential. Full article

Mycenaean glass artifacts, such as beads and relief plaques, are highly susceptible to degradation, which can significantly modify their visual attributes and pose classification challenges. Corrosion on glass and faience artifacts has often led to misinterpretation, since the visual manifestations of degradation can be similar for both materials, impacting research conclusions. This paper presents a segment of a broader study conducted within the Myc-MVP project, utilizing advanced scientific methods to analyze the compositional changes in corroded vitreous artifacts. Through Macro-X-ray Fluorescence (MA-XRF) and LED microscopy, we aim to understand the correlation between compositional alterations and visual degradation manifestations. The use of MA- XRF was particularly crucial for non-destructively mapping the elemental distribution over large surfaces, allowing for a more comprehensive analysis of corrosion patterns. The results presented in this study are from a subset of artifacts examined using MA- XRF, highlighting critical insights into the spatial compositional shifts that contribute to visible deterioration. This paper discusses the first real-life contribution of Macro X-ray Fluorescence (MA-XRF) imaging to mapping the spatial compositional changes that occur when Mycenaean vitreous materials undergo degradation, yielding visible deterioration. MA-XRF scanning offers a fully non-invasive and non-destructive method for recording compositional data across the entire surface of an object. The results can be visualized as distribution images, which are more accessible and interpretable for a broader audience compared to the spectra generated by traditional spectrometric techniques. These findings aspire to inform strategies for the accurate classification, effective management, appropriate conservation treatment, and long-term preservation of vitreous artifacts. Full article

The hot deformation behavior of 4Cr16MoCu martensitic stainless steel alloyed with 1% Cu was investigated through hot compression tests at temperatures ranging from 900 to 1150 °C and strain rates of 0.001 to 1 s⁻¹. The addition of Cu is strategically employed to synergistically enhance precipitation hardening and corrosion resistance, yet its complex interplay with hot deformation mechanisms remains poorly understood, demanding systematic investigation. The results revealed a narrow forging temperature range and significant strain rate sensitivity, with deformation resistance increasing markedly at higher strain rates. An Arrhenius constitutive model incorporating a seventh-degree polynomial for strain compensation was developed to describe the flow stress dependence on deformation temperature and strain rate. The model demonstrated high accuracy, with a correlation coefficient (R²) of 0.9917 and an average absolute relative error (AARE) of 3.8%, providing a reliable theoretical foundation for practical production applications. Furthermore, a hot processing map was constructed based on the dynamic material model (DMM), and the optimal hot working parameters were determined through microstructural analysis: an initial forging temperature of 1125 °C, a final forging temperature of 980 °C, and a strain rate of 0.1 s⁻¹. These conditions resulted in a fine and uniform grain structure, while strain rates above 0.18 s⁻¹ were identified as unfavorable due to the risk of uneven deformation. Full article