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Superlubricity of graphite and graphene has aroused increasing interest in recent years. Yet how to obtain a long-lasting superlubricity between graphene layers, under high applied normal load in ambient atmosphere still remains a challenge but is highly desirable. Here, we report a direct measurement of sliding friction between graphene and graphene, and graphene and hexagonal boron nitride (h-BN) under high contact pressures by employing graphene-coated microsphere (GMS) probe prepared by metal-catalyst-free chemical vapour deposition. The exceptionally low and robust friction coefficient of 0.003 is accomplished under local asperity contact pressure up to 1 GPa, at arbitrary relative surface rotation angles, which is insensitive to relative humidity up to 51% RH. This ultralow friction is attributed to the sustainable overall incommensurability due to the multi-asperity contact covered with randomly oriented graphene nanograins. This realization of microscale superlubricity can be extended to the sliding between a variety of two-dimensional (2D) layers. Superlubricity can be unstable in graphene systems, especially under high applied loads. Here the authors use microspheres uniformly coated by graphene to measure friction between 2D materials and show that superlow friction is preserved for long periods of time under high loads and various atmospheres.

Railway infrastructures have played a critical role to ensure the continuity of goods and passenger transportation in China. Under extreme loading and environmental conditions, railway structures are vulnerable to deterioration and failure, leading to the interruption of the whole transportation system. Several techniques have been used for the health monitoring of railway structures. Optical fiber sensors are the widely recognized technique due to their inherent advantages such as high sensitivity, anti-electromagnetic interference, light weight, tiny size, corrosion resistance, and easy integration and network configuration. This paper provides a state-of-the-art of optical fiber sensing technologies and their practical application in railway infrastructures. In addition, the strain transfer analysis of optical fiber sensors is described for parameter reflection. A smart concept for artificial intelligence contribution is also declared. Finally, existing and future prospects on smart concept-based optical fiber sensors for railway infrastructure are discussed. The study can provide useful guidance to understand the problems in artificial intelligence which contributed to the Structural Health Monitoring system of railway structures.

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure. Therefore, it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring (SHM) system, so as to provide a scientific basis for structural damage identification and dynamic model modification. In view of this, this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters. The paper primarily introduces data-driven modal parameter recognition methods (e.g., time-domain, frequency-domain, and time-frequency-domain methods, etc.), briefly describes damage identification methods based on the variations of modal parameters (e.g., natural frequency, modal shapes, and curvature modal shapes, etc.) and modal validation methods (e.g., Stability Diagram and Modal Assurance Criterion, etc.). The current status of the application of artificial intelligence (AI) methods in the direction of modal parameter recognition and damage identification is further discussed. Based on the previous analysis, the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

The in-situ health condition of carbon fiber reinforced polymer (CFRP) reinforced structures has become an important topic, which can reflect the structural performance of the retrofitted structures and judge the design theory. An optical fiber-based structural health monitoring technique is thus suggested. To check the effectiveness of the proposed method, experimental testing on smart CFRP reinforced steel beams under impact action has been performed, and the dynamic response of the structure has been measured by the packaged FBG sensors attached to the surface of the beam and the FBG sensors inserted in the CFRP plates. Time and frequency domain analysis has been conducted to check the structural feature of the structures and the performance of the installed sensors. Results indicate that the packaged Fiber Bragg Grating (FBG) sensors show better sensing performance than the bare FBG sensors in perceiving the impact response of the beam. The sensors embedded in the CFRP plate show good measurement accuracy in sensing the external excitation and can replace the surface-attached FBG sensors. The dynamic performance of the reinforced structures subjected to the impact action can be straightforwardly read from the signals of FBG sensors. The larger impact energies bring about stronger impact signals.

Transportation structures such as composite pavements and railway foundations typically consist of multi-layered media designed to withstand high bearing capacity. A theoretical understanding of load transfer mechanisms in these multi-layer composites is essential, as it offers intuitive insights into parametric influences and facilitates enhanced structural performance. This paper employs an improved transfer matrix method to address the limitations of existing theoretical approaches for analyzing multi-layer composite structures. By establishing a two-dimensional composite pavement model, it investigates load transfer characteristics and validates the accuracy through finite element simulation. The proposed method offers a straightforward analytical approach for examining internal interactions between structural layers. Case studies indicate that the concrete surface layer is the main load-bearing layer for most vertical normal and shear stresses. The soil base layer reduces the overall mechanical response of the substructure, while horizontal actions increase the risk of interfacial slip and cracking. Structural optimization analysis demonstrates that increasing the thickness of the concrete surface layer, enhancing the thickness and stiffness of the soil base layer, or incorporating gradient layers can significantly mitigate these risks of interfacial slip and cracking. The findings of this study can guide the optimization design, parameter analysis, and damage prevention of multi-layer composite structures.

Based on the Guangzhou Business Center project, a typical super high-rise building with an asymmetric plan, taking the construction speed, closure time of mega braces and belt trusses as influencing factors, a parametric analysis on its lateral and vertical deformations, as well as the maximum stress of key structural members was conducted. The analysis results indicated that the construction speed had a relatively small impact on the deformation and the maximum stress of key members. However, synchronous closure of belt truss compared with the delayed closure would result in smaller horizontal and vertical deformation differences, as well as the stress of belt truss. Meanwhile, the closure timing of the mega braces had little influence on the vertical deformation difference and the stress of belt truss. And the earlier the closure, the smaller the horizontal drift ratio, the greater the maximum stress of the mega braces. Further, deformation control measurements were brought forward. On the one hand, FEM simulation was carried out according to the above construction suggestions. On the other hand, real-time monitoring was also used. Finally, by comparing both results, proposed construction deformation control measures and simulation methods were verified.

Carbon-fiber-reinforced polymer (CFRP) composites have many advantages, and have been widely used in aerospace structures, buildings, bridges, etc. The analysis of dynamic response characteristics of CFRP composite structures is of great significance for promoting the development of smart composite structures. For this reason, vibration experiments of CFRP laminates with surface-attached fiber Bragg grating (FBG) sensors under various dynamic loading conditions were carried out. Time- and frequency-domain analyses were conducted on the FBG testing signals to check the dynamic characteristics of the CFRP structure and the sensing performance of the installed sensors. The results show that the FBG sensors attached to the surface of the CFRP laminates can accurately measure the dynamic response and determine the excited position of the CFRP laminates, as well as invert the strain distribution of the CFRP laminates through the FBG sensors at different positions. By performing Fourier transform, short-time Fourier transform, and frequency domain decomposition (FDD) on the FBG sensing signals, the time–frequency information and the first eight modal frequencies of the excited CFRP structure can be obtained. The modal frequencies obtained by different excitation types are similar, which can be used for structural damage identification. The research in this paper clarifies the effectiveness and accuracy of FBG sensors in sensing the dynamic characteristics of CFRP structures, which can be used for performance evaluation of CFRP structures and will effectively promote the design and development of intelligent composite material structures.

Timely detection of leaks is essential for the safe and reliable operation of pressure vessels used in superconducting systems, aerospace, and medical equipment. To address the lack of efficient online leak detection methods for such vessels, this paper proposes a quasi-distributed fiber Bragg grating (FBG) sensing network combined with theoretical stress analysis to diagnose vessel conditions. We analyze the stress–strain distributions of vacuum vessels under varying pressures and examine stress concentration effects induced by small holes; these analyses guided the design and placement of quasi-distributed FBG sensors around the vacuum valve for online leakage monitoring. To improve measurement accuracy, we introduce a vibration correction algorithm that mitigates pump-induced vibration interference. Comparative tests under three leakage scenarios demonstrate that when leakage occurs during vacuum extraction, the proposed system can reliably detect the approximate leak location. The results indicate that combining an FBG sensing network with stress concentration analysis enables initial localization and assessment of leak severity, providing valuable support for the safe operation and rapid maintenance of vacuum pressure vessels.

Pipes are the main structures serving as the lifeline for oil and gas transportation. However, they are prone to cracks, holes and other damages due to harsh working environments, which can lead to leakage incidents and result in significant economic losses. Therefore, the development of structural health monitoring systems with advanced online diagnostic methods is of great importance for identifying local damages and assessing the safety state of pipe structures. These efforts can guide rapid repairs and ensure the continuous, efficient and cost-effective transportation of oil and gas resources. To address this problem, this paper proposes the development of a pipe monitoring system based on quasi-distributed fiber Bragg grating (FBG) sensing technology. The SSI-COV method is employed to process the sensor responses and extract the modal parameters of the structure. Based on this foundation, an enhanced damage identification index is proposed, which mitigates the effects of support and excitation positions on damage identification. The pipe structure can be regarded as a continuous super-statical beam, and based on its structural symmetry, a unit structure, specifically a stainless-steel pipe with fixed ends, is regarded as the experimental subject. Impact experiments have been conducted to analyze its behavior in both undamaged and damaged states. The research indicates that by using the proposed modal parameter identification method and the ASMDI damage index, ASMDI exhibits peak values at damage locations of the pipe structure. This allows for the identification of structural damage with high accuracy, fast processing efficiency and strong robustness. The study provides an effective and reliable damage diagnosis method, which can contribute to the refinement and visualization of pipe structural health monitoring systems.

The wide-spread application of carbon fiber-reinforced polymer (CFRP) composites in industrial fields has led to high demand for developing a rapid detection method for assessing the structural performance of CFRP composites in operation based on optical fiber sensing technology. Therefore, the effectiveness and reliability of evaluating the fatigue resistance of CFRP plates based on fiber Bragg grating (FBG) monitoring information were explored. The strain response of CFRP plates at key positions under constant amplitude fatigue load was monitored by bare FBGs in series and packaged quasi-distributed FBGs in series. The structural performance and fatigue resistance characteristics of CFRP plates were evaluated by statistical analysis and fatigue life prediction theory. The validity and accuracy of the test and analysis results were demonstrated by finite element modeling analysis. Compared with the traditional methods that evaluate the structural fatigue performance based on mass destructive experiments, this method significantly improves the detection efficiency and realizes the non-destructive and rapid online evaluation of structural service performance. Research shows that the designed FBG sensors can effectively monitor the strain response of CFRP plate under fatigue load, and the correlated fatigue algorithm can provide feasible and reliable technical approaches for online detection and evaluation on the structural performance of CFRP components.