A Strain Transfer Model for Detection of Pitting Corrosion and Loading Force of Steel Rebar with Distributed Fiber Optic Sensor.

Steel rebar corrosion is one of the predominant factors influencing the durability of marine and offshore reinforced concrete structures, resulting in economic loss and the potential threat to human safety. Distributed fiber optic sensors (DFOSs) have gradually become an effective method for structural health monitoring over the past two decades. In this work, a strain transfer model is developed between a steel rebar and a DFOS, considering pitting-corrosion-induced strain variation in the steel rebar. The Gaussian function is first adopted to describe the strain distribution near the corrosion pit of the steel rebar and then is substituted into the governing equation of the strain transfer model, and the strain distribution in the DFOS is analytically obtained. Tensile tests are also conducted on steel rebars with artificially simulated corrosion pits, which are used to validate the developed model. The results show that the Gaussian function can be used to describe the strain variation near a corrosion pit with a depth less than 50% of the steel rebar diameter, and the strain distribution in the DFOS analytically determined based on the developed strain transfer model agrees well with the tensile test results. The corrosion pit depth and loading force in the steel rebars estimated based on the proposed model agree well with the actual values, and therefore, the developed strain transfer model is effective in detecting pitting corrosion and loading force in steel rebars.

CNN-LSTM Hybrid Model to Promote Signal Processing of Ultrasonic Guided Lamb Waves for Damage Detection in Metallic Pipelines.

The ultrasonic guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects in metallic pipelines. The signal processing of guided waves is often challenging due to the complexity of the operational conditions and environment in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for the signal processing and data classification of complex systems, thus showing great potential for damage detection in critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model was utilized for decoding ultrasonic guided waves for damage detection in metallic pipelines, and twenty-nine features were extracted as input to classify different types of defects in metallic pipes. The prediction capacity of the CNN-LSTM model was assessed by comparing it to those of CNN and LSTM. The results demonstrated that the CNN-LSTM hybrid model exhibited much higher accuracy, reaching 94.8%, as compared to CNN and LSTM. Interestingly, the results also revealed that predetermined features, including the time, frequency, and time-frequency domains, could significantly improve the robustness of deep learning approaches, even though deep learning approaches are often believed to include automated feature extraction, without hand-crafted steps as in shallow learning. Furthermore, the CNN-LSTM model displayed higher performance when the noise level was relatively low (e.g., SNR = 9 or higher), as compared to the other two models, but its prediction dropped gradually with the increase of the noise.

Comparative Study of Three Carbon Additives: Carbon Nanotubes, Graphene, and Fullerene-C60, for Synthesizing Enhanced Polymer Nanocomposites.

While nanoparticles from the carbon family have been incorporated effectively for polymer matrixes, there is no clear information available for understanding the impacts of the morphology of different carbon nanoparticles on the performance of carbon-based nanocomposites. Therefore, this study aimed to provide a comprehensive, comparative investigation to systematically assess the impacts of nanoparticles on the tribological, mechanical, and electrochemical properties of the epoxy coatings using three representative 0D, 1D, and 2D nanoparticles: Fullerene-C60 (C60), graphene nanoplatelets (GNPs), and carbon nanotubes (CNTs). The anti-corrosion performance of the nanocomposites in both the short and long term was characterized. The mechanical properties were examined by abrasion, adhesion, and tensile tests. Fourier-transform infrared spectroscopy (FTIR) was conducted to determine their chemical structures, while scanning electron microscopy (SEM) was used to determine their surface texture. The electrochemical impedance spectroscopy (EIS) results revealed that the coatings reinforced by C60 and GNP had better anti-corrosion performance than that of the CNT/epoxy samples. The incorporation of C60 and CNT led to a considerable improvement in tensile properties, while improved abrasion resistance was observed in all types of nanofiller/epoxy groups. C60-loaded composites exhibited a significant enhancement in tensile properties as compared to CNT or GNP composites.

Enhanced Protective Coatings Based on Nanoparticle fullerene C60 for Oil & Gas Pipeline Corrosion Mitigation.

Corrosion accounts for huge maintenance cost in the pipeline community. Promotion of protective coatings used for oil/gas pipeline corrosion control, in terms of high corrosion resistance as well as high damage tolerance, are still in high demand. This study was to explore the inclusion of nanoparticle fullerene-C60 in protective coatings for oil/gas pipeline corrosion control and mitigation. Fullerene-C60/epoxy nanocomposite coatings were fabricated using a solvent-free dispersion method through high-speed disk (HSD) and ultrasonication. The morphology of fullerene-C60 particles was characterized by transmission electron microscopy (TEM), and dynamic light scattering (DLS). The data analysis indicated that the nanoparticles were effectively dispersed in the matrix. The performance of the nanocomposites was investigated through their mechanical and electrochemical properties, including corrosion potential, tensile strength, strain at failure, adhesion to substrate, and durability performance. Dogbone shaped samples were fabricated to study the tensile properties of the nanocomposites, and improvement of strength, ultimate strain, and Young's modulus were observed in the C60/epoxy specimens. The results demonstrated that the C60/epoxy composite coatings also had improvements in adhesion strength, suggesting that they could provide high damage tolerance of coatings for engineering applications. Moreover, the electrochemical impedance spectroscopy (EIS) results generated from the accelerated durability test revealed that the developed fullerene-C60 loaded composite coatings exhibited significantly improved corrosion resistance. The nanocomposite with 0.5 and 1.0 wt.% of C60 particles behaved as an intact layer for corrosion protection, even after 200-h salt spray exposure, as compared to the control coating without nanofiller in which severe damage by over 50% reduction was observed.

Field Validation of a Magnetic Sensor to Monitor Borehole Deviation during Tunnel Excavation.

In this article, a magnetic sensor is proposed to monitor borehole deviation during tunnel excavation. It is made by piling four super-strong N42 NdFeB cylinder magnets and then encasing them in an aluminum alloy hollow cylinder. The distribution of the magnetic field produced by the magnetic sensor and its summation with the geomagnetic field (GMF) in a global coordinate system are derived based on the theory of magnetic fields. An algorithm is developed to localize the position of the magnetic sensor. The effect of the GMF variation on the effective monitoring range of the magnetic sensor is also studied numerically. Field validation tests are conducted at Jinzhai Pumped-Storage hydroelectric power station, during the excavation of an inclined tunnel in Anhui Province of China. Test results show that the algorithm and the magnetic sensor are used successfully to detect the deviation of the borehole with an estimated error of approximately 0.5 m. The errors are mainly from the measurement errors of the coordinates, of both the test and the measurement points. The effective monitoring range of the magnetic sensor is dependent on the direction of the magnetic sensor as well as the variation of the GMF.

A Fe-C coated long-period fiber grating sensor for corrosion-induced mass loss measurement.

This Letter reports a Fe-C coated long period fiber gratings sensor with a grating period of 387±0.1 µm for corrosion monitoring of low carbon steel in a 3.5 wt. % NaCl solution. An LPFG sensor was first deposited with a 0.8 µm thick layer of silver (Ag) and then electroplated with a 20 µm thick Fe-C coating. The chemical composition of the Fe-C coating was designed to include the main elements of low carbon steel. The resonant wavelength of the coated sensor was correlated with the mass loss of steel over time. Test results indicated a corrosion sensitivity of 0.0423 nm per 1% mass loss up to 80% Fe-C mass loss and 0.576 nm per 1% mass loss between 80% and 95% Fe-C mass loss. The corrosion sensitivity of such a Fe-C coated LPFG sensor was a trade-off for the service life of the sensor, both depending on thicknesses of the inner silver layer and the outer Fe-C coating.

Corrosion Resistance of a Sand Particle-Modified Enamel Coating Applied to Smooth Steel Bars.

The protective performance of a sand particle-modified enamel coating on reinforcing steel bars was evaluated in 3.5 wt% NaCl solution by electrochemical impedance spectroscopy (EIS). Seven percentages of sand particles by weight were investigated: 0%, 5%, 10%, 20%, 30%, 50% and 70%. The phase composition of the enamel coating and sand particles were determined with the X-ray diffraction (XRD) technique. The surface and cross-sectional morphologies of the sand particle-modified enamel coating were characterized using scanning electron microscopy (SEM). XRD tests revealed three phases of sand particles: SiO2, CaCO3 and MgCO3. SEM images demonstrated that the enamel coating wetted well with the sand particles. However, a weak enamel coating zone was formed around the sand particles due to concentrated air bubbles, leading to micro-cracks as hydrogen gas pressure builds up and exceeds the tensile strength of the weak zone. As a result, the addition of sand particles into the enamel coating reduced both the coating and corrosion resistances.