Search Results (16)

AISI 347H stainless steel is widely used in high-temperature environments due to its excellent creep strength and oxidation resistance; however, its corrosion performance remains highly sensitive to thermal oxidation, and the effects of thermal history on its passive film stability are not yet fully understood. This study addresses this knowledge gap by systematically investigating the influence of solution treatment on the corrosion and oxidation resistance of AISI 347H stainless steel. The specimens were subjected to solution heat treatment at 1050 °C, followed by air cooling, and then evaluated through electrochemical testing, high-temperature oxidation experiments at 550 °C, and multiscale surface characterization techniques. The solution treatment refined the austenitic microstructure by dissolving coarse Nb-rich precipitates, as confirmed by SEM and EBSD, and improved passive film integrity. The stabilizing effect of Nb also played a critical role in suppressing sensitization, thereby enhancing resistance to intergranular attack. Electrochemical measurements and EIS analysis revealed a lower corrosion current density and higher charge transfer resistance in the treated samples, indicating enhanced passivation behavior. ToF-SIMS depth profiling and oxide thickness analysis confirmed a slower parabolic oxide growth rate and reduced oxidation rate constant in the solution-treated condition. At 550 °C, oxidation was suppressed by the formation of compact, Cr-rich scales with dual-distributed Nb oxides, effectively limiting diffusion pathways and stabilizing the protective layer. These findings demonstrate that solution treatment is an effective strategy to improve the long-term corrosion and oxidation performance of AISI 347H stainless steel in harsh service environments. Full article

Corrosion poses a critical challenge to the durability and performance of metals and alloys, particularly steel, with significant economic, environmental, and safety implications. The corrosion susceptibility of steel is influenced by aggressive chemical species, intrinsic material defects, and environmental factors. Understanding the atomic-scale mechanisms governing corrosion is essential for developing advanced corrosion-resistant materials. Density functional theory (DFT) has become a powerful computational tool for investigating these mechanisms, providing insight into the adsorption, diffusion, and reaction of corrosive species on iron surfaces, the formation and stability of metal oxides, and the influence of defects such as vacancies and grain boundaries in localised corrosion. This review presents a comprehensive analysis of recent DFT-based studies on iron and steel surfaces, emphasising the role of solvation effects and van der Waals corrections in improving model accuracy. It also explores defect-driven corrosion mechanisms and the formation of protective and reactive oxide layers under varying oxygen coverages. By establishing accurate DFT modelling approaches, this review provides up-to-date literature insights that support future integration with machine learning and multiscale modelling techniques, enabling reliable atomic-scale predictions. Full article

This study systematically investigates the influence mechanism of the element Cr on the mechanical properties of the heat-affected zone in pipeline steels for supercritical CO₂ transportation. Microstructural evolution in the heat affected-zone was characterized through thermal simulation tests, Charpy impact testing (−10 °C), and microhardness measurements, complemented by multiscale microscopic analyses (optical microscopy, scanning electron microscopy, electron backscatter diffraction, and transmission electron microscopy). The results demonstrate that Cr addition enhances the base metal’s resistance to supercritical CO₂ corrosion but reduces its low-temperature impact toughness from 277 J to 235 J at −10 °C. Notably, the intercritical heat-affected zone exhibits severe embrittlement, with impact energy plummeting from 235 J (base metal) to 77 J. Microstructural analysis reveals that Cr interacts with carbon to form stable carbonitride particles, which reduce the free carbon concentration and diffusion coefficient in austenite, thereby inducing heterogeneous austenitization. Undissolved carbonitrides pin grain boundaries, creating carbon concentration gradients. During rapid cooling, these localized carbon-enriched microregions preferentially transform into core–shell-structured M-A constituent, characterized by a micro-twin containing retained austenite core encapsulated by high hardness lath martensite. The synergistic interaction between micro-twins and interfacial thermal mismatch stress induces localized stress concentration, triggering microcrack nucleation and subsequent toughness degradation. Full article

In order to comprehensively investigate the spatio-temporal dynamics of corrosion evolution under complex pipeline environments and improve the corrosion rate prediction accuracy, a novel framework for corrosion rate prediction based on adaptive multivariate time series graph neural network (MTGNN) multi-feature spatio-temporal correlation analysis is proposed. First, pipeline monitoring points are modeled as graph nodes to construct the pipeline corrosion spatio-temporal information graph, with corrosion rate and auxiliary features (selected through feature correlation analysis) forming node attributes. Then, a dynamic adjacency matrix is adaptively learned to capture hidden spatial dependencies, while temporal convolution modules extract multi-scale temporal patterns, and the node sequences with integrated corrosion features are input into the adaptive MTGNN for prediction. To reduce the accumulation of errors in multi-step prediction, a “chunked progressive” training strategy is adopted, incrementally expanding prediction horizons. Finally, experiments based on real urban drainage pipeline data show that in six-step predictions, the model reduces MAE, RMSE, and MAPE by 6.59–32.16%, 4.38–27.95%, and 5.01–22.22%, respectively, compared to traditional time series methods such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and non-adaptive MTGNN. The results indicate that the adaptive MTGNN, which integrates multi-source node features, has higher prediction accuracy across the three evaluation metrics, highlighting its capability to leverage spatio-temporal synergies for accurate short-term corrosion rate prediction. Full article

This study investigates multi-scale reinforcement of Ultra-High-Performance Concrete through targeted modifications of its mechanical and fracture-resistant properties via carbon microfibers and carbon nanotubes. The research employed comprehensive characterization techniques including workability tests, mercury porosimetry for microscale porosity analysis, and X-ray tomography for macro-scale pore evaluation. Mechanical performance was assessed through compression strength, tensile strength, and fracture energy measurements. Results demonstrated significant performance enhancements testing UHPC samples with 6 mm carbon microfibers (9 kg/m³) and varying carbon nanotubes dosages (0.11–0.54 wt%). The addition of carbon microfibres improved compressive strength by 12%, while incorporating 0.54 wt% carbon nanotubes further increased strength by 24%. Remarkably, the combined reinforcement strategy yielded a 313% increase in tensile strength compared to the reference mixture. The synergistic effect of carbon fibers and carbon nanotubes proved particularly effective in enhancing concrete performance. This multi-scale reinforcement approach presents a promising alternative to traditional steel fiber reinforcement, offering superior mechanical properties and potential advantages in corrosive environments. Full article

Rigid reinforced concrete (RC) frames are generally adopted as stiff elements to make the building structures resistant to seismic forces. However, a method has yet to be fully sought to provide earthquake resistance through optimizing beam and column performance in a rigid frame. Due to its high corrosion resistance, the integration of CFRP offers an opportunity to reduce frequent repairs and increase durability. This paper presents the structural response of CFRP beams integrated into rigid frames when subjected to seismic events. Without any design provision for CFRP systems in extreme events, multiscale simulations and parametric analyses were performed to optimize the residual state and global performance. Macroparameters, represented by the ductility ratio and microfactors, have been analyzed using a customized version of the modified compression field theory (MCFT). The main parameters considered were reinforcement under tension and compression, strength of concrete, height-to-width ratio, section cover, and confinement level, all of which are important to understand their influence on seismic performance. The parametric analysis results highlight the increased ductility and higher load-carrying capacity of the CFRP-reinforced tested component compared to the RC component. These results shed light on the possibility of designing CFRP-reinforced concrete components that could improve ductile frames with increased energy dissipation and be suitable for applications in non-corrosive seismic-resistant buildings. This also shows reduced brittleness and enhancement in the failure mode. Numerical simulations and experimental results showed a strong correlation with a deviation of about 8.3%, underlining the reliability of the proposed approach for designing seismic-resistant CFRP-reinforced structures. Full article

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article

Cables are vital load-bearing components of cable-stayed bridges. Surface defects can lead to internal corrosion and fracturing, significantly impacting the stability of the bridge structure. The detection of surface defects from bridge cable images faces numerous challenges, including shadow disturbances due to uneven lighting and difficulties in addressing multiscale defect features. To address these challenges, this paper proposes a novel and cost-effective deep learning segmentation network, named Trans-DCN, to detect defects in the surface of the bridge cable. The network leverages an efficient Transformer-based encoder and integrates multiscale features to overcome the limitations associated with local feature inadequacy. The decoder implements an atrous Deformable Convolution (DCN) pyramid and dynamically fuses low-level feature information to perceive the complex distribution of defects. The effectiveness of Trans-DCN is evaluated by comparing it with state-of-the-art segmentation baseline models using a dataset comprising cable bridge defect images. Experimental results demonstrate that our network outperforms the state-of-the-art network SegFormer, achieving a 27.1% reduction in GFLOPs, a 1.2% increase in mean Intersection over Union, and a 1.5% increase in the F1 score. Ablation experiments confirmed the effectiveness of each module within our network, further substantiating the significant validity and advantages of Trans-DCN in the task of bridge cable defect segmentation. The network proposed in this paper provides an effective solution for downstream cable bridge image analysis. Full article

Anchor bolt corrosion is a complex and dynamic system, and the prediction and identification of its corrosion degree are of significant importance for engineering safety. Currently, non-destructive testing using ultrasonic guided waves can be employed for its detection. Building upon the analysis of anchor bolt corrosion mechanisms, this paper proposes a method for evaluating the corrosion degree of anchor bolts based on multi-scale convolutional neural networks (MS-CNNs) that address the multi-mode propagation and dispersion effects of ultrasonic guided wave signals in non-destructive testing. Electrochemical experiments were conducted to simulate anchor bolt corrosion, and ultrasonic guided wave non-destructive testing was performed every 12 h to obtain waveform data. An MS-CNN was then utilized to accurately diagnose the corrosion degree of the anchor bolts. The test results demonstrate that this method effectively detects and diagnoses the extent of anchor bolt corrosion, facilitating timely troubleshooting and preventing potential safety accidents. Full article

Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You Only Look Once (YOLO) object detection algorithms and integrates the SimAM attention mechanism for improved feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Moreover, it also employs the Alpha-IoU cost function for enhanced small-scale object detection. YOLOX-Ray’s performance was assessed in three case studies: hotspot detection, infrastructure crack detection and corrosion detection. The architecture outperforms all other configurations, achieving $mA{P}_{50}$ values of 89%, 99.6% and 87.7%, respectively. For the most challenging metric, $mA{P}_{50:95}$, the achieved values were 44.7%, 66.1% and 51.8%, respectively. A comparative analysis demonstrated the importance of combining the SimAM attention mechanism with Alpha-IoU loss function for optimal performance. In conclusion, YOLOX-Ray’s ability to detect and to locate multi-scale objects in industrial environments presents new opportunities for effective, efficient and sustainable inspection processes across various industries, revolutionizing the field of industrial inspections. Full article

From the point of view of material multiscale analysis, the deterioration of the fatigue properties of the cable is due to the micro-damage inside the strand. To describe the damage failure process of the cable as accurately as possible, many micro damage details need to be implanted in the strand model. However, with the increase of the number of wires in the cable body, this modeling method will produce a large number of elements in the finite element model of the stay cable, which makes the calculation cannot be completed iteratively. Based on this, a time-step adaptive simulation method for corrosion fatigue damage evolution of stay cables was proposed in this paper. In this method, the corrosion fatigue damage evolution model was implanted in the local part of the strand model, and a damage variable was used to comprehensively consider the evolution behavior of distributed micro-damage in the strand, which satisfies the calculation accuracy and realizes the simulation of the whole process of fatigue deterioration of the stay cable, greatly improving the calculation accuracy and convenience. Then taking an in-service cable-stayed bridge as an example, the stress of the main beam and the local spatial stress of the main beam under the special condition of the cable damage failure were studied by combining the rod model with the local model, which provides data reference for the replacement of the cable of the cable-stayed bridge with a long construction life. Full article

Timber has been commonly used in the field of civil engineering, and the health condition of timber is of great significance for the whole structure in practical scenarios. However, due to mechanical load and environmental impact, timber-based constructions are vulnerable to termite attack, microbial corrosion and fractures within their service lives. Thus, the damage monitoring of timber structures is very challenging under real situations. This paper presents an intelligent timber damage monitoring approach using Lead Zirconate Titanate (PZT)-enabled active sensing and intrinsic multiscale entropy analysis. The proposed approach adopts PZT-enabled active sensing to collect the signals depicting dynamic characteristics of the timber structure. The proposed intrinsic multiscale entropy analysis utilizes variational mode decomposition (VMD) to deal with the collected response signals. Decomposition of the response signals into a set of band-limited intrinsic mode functions (BLIMFs) denoting nonlinear and nonstationary characteristics. Then multiscale sample entropy (MSE) is employed to extract quantitative features, which are adopted as health condition indicators of timber structures. Finally, the convolutional neural network (CNN) fulfills the intelligent timber damage monitoring by using the quantitative features as the effective input. The research findings reveal the efficacy and superiority of the proposed method. Full article

Marine engineering materials are prone to serious corrosion damage, which affects the efficiency and reliability of marine equipment. The diversity of corrosion morphology makes it difficult to achieve the quantification and standardization of the microscopic local information on the corroded surface, which is of great significance to reveal the multi-scale corrosion mechanism. In this paper, an image intelligent recognition method for the corrosion damage of Q420 steel in seawater is established, which is based on the gray level co-occurrence matrix, binary image method and fractal model. Through the feature extraction of corrosion morphology, the quantitative analysis of corrosion morphology and the microscopic evaluation of corrosion characteristics are achieved. The image recognition data are consistent with the electrochemical result for most cases, which confirms the validity of this image intelligent recognition method. The average gray value and energy value of corrosion morphology reduces with the Cl⁻ concentration, indicating that the corrosion damage aggravates gradually. The increasing standard deviation and entropy reflects that the randomness of the pit distribution increases. The pitting ratio increases from 20.19% to 51.64% as the Cl⁻ concentration increases from 50% to 200% of the standard solution. However, there exists a discrepancy for high Cl⁻ concentration because of the irregular corrosion morphology and various pit depth. The fractal dimension increases with the complexity of the corroded surface at low Cl⁻ concentration, but the fractal dimension decreases at high Cl⁻ concentration because the corrosion complexity is interfered by the interconnection of corrosion holes due to the accelerated pit evolution. Full article

In this work, a mechanically durable anticorrosion superhydrophobic coating is developed using a nanocomposite coating solution composed of silica nanoparticles and epoxy resin. The nanocomposite coating developed was tested for its superhydrophobic behavior using goniometry; surface morphology using scanning electron microscopy and atomic force microscopy; elemental composition using energy dispersive X-ray spectroscopy; corrosion resistance using atomic force microscopy; and potentiodynamic polarization measurements. The nanocomposite coating possesses hierarchical micro/nanostructures, according to the scanning electron microscopy images, and the presence of such structures was further confirmed by the atomic force microscopy images. The developed nanocomposite coating was found to be highly superhydrophobic as well as corrosion resistant, according to the results from static contact angle measurement and potentiodynamic polarization measurement, respectively. The abrasion resistance and mechanical durability of the nanocomposite coating were studied by abrasion tests, and the mechanical properties such as reduced modulus and Berkovich hardness were evaluated with the aid of nanoindentation tests. Full article

In the present study, a new multiscale method is proposed for the statistical analysis of spatial distribution of massive corrosion pits, based on the image recognition of high resolution and large field-of-view (montage) optical images. Pitting corrosion for high strength pipeline steel exposed to sodium chloride solution was observed using an optical microscope. Montage images of the corrosion pits were obtained, with a single image containing a large number of corrosion pits. The diameters and locations of all the pits were determined simultaneously using an image recognition algorithm, followed by statistical analysis of the two-dimensional spatial point pattern. The multiscale spatial distributions of pits were analyzed by dividing the montage image into a number of different windows. The results indicate the clear dependence of distribution features on the spatial scales. The proposed method can provide a better understanding of the pit growth from the perspective of multiscale spatial evolution. Full article