Fatigue cracking is one of the most notable distresses in steel bridge deck pavement (SBDP), necessitating the development of high performance pavement materials to extend service life. In this paper, the fatigue resistance characteristics of natural rock asphalt (NRA)/SBS systematically evaluated. Twelve types of composite-modified asphalt binders were prepared by incorporating Iranian rock asphalt (IRA), Buton rock asphalt (BRA), and SBS as modifiers. IRA and BRA were added at 5 wt%, 10 wt%, and 15 wt%, while SBS was introduced at 2 wt% and 3 wt%. The modified asphalt binders subjected to time sweep (TS) test and linear amplitude sweep (LAS) test to comparatively analyze fatigue damage and fatigue life. DSR-C model was applied to calculate the fatigue crack length. The research also comparative evaluate differences and correlations between fatigue crack length indexes and conventional fatigue life indexes. The results showed that NRA enhanced the fatigue performance of modified asphalt binders. With the increase of NRA content, the fatigue properties of modified asphalt also become better. S₃I₁₅ is better than other composite modified asphalt binders in both fatigue life and fatigue cracking indexes, demonstrating that it offers the best resistance to fatigue damage. The fatigue cracking index calculated by the DSR-based cracking (DSR-C) model could effectively evaluate the fatigue performance of NRA/SBS composite modified asphalt binders. NRA/SBS composite modified asphalt could be widely used in steel bridge deck pavement.

The maximum packing fraction (φ_fm) of flexible fibers is an essential parameter for understanding the rheological behavior of flexible fiber-reinforced cement paste (FFRCP). However, direct measurement of φ_fm of flexible fibers is still lacking. In this study, a shear rheology-based method for direct measurement of φ_fm was proposed and the assumption of fiber conformation under shear was verified by micro-CT. Based on this, a yield stress model for FFRCP was constructed to explain the entanglement and friction effects in the fiber network. Finally, static yield stress tests and small amplitude oscillatory shear (SAOS) tests were carried out to explore the structural build-up of FFRCP. It was found that the proposed method enables direct determination of φ_fm through only a few viscosity-fiber content data for a given FFRCP. Furthermore, the proposed model can describe the static yield stress of FFRCP well. Finally, the relative structural build-up rate of FFRCP follows a similar trend as the relative yield stress, with a critical relative fiber volume fraction (0.299) as the boundary. Subsequently, the relative structural build-up gradually deviates from the relative yield stress due to the limiting effect of the fibers.

Nature-inspired self-cleaning surfaces have attracted considerable attention from both fundamental research and practical applications. This review adopts a chemical-engineering point of view and focuses on mechanisms, modelling, and manufacturing (M3) of nature-inspired self-cleaning surfaces. We will introduce six nature-inspired self-cleaning mechanisms: The Lotus-effect, superhydrophobic-induced droplet jumping, superhydrophobic-induced unidirectional movement of water droplet, underwater-superoleophobic-based self-cleaning, slippery-based self-cleaning, and dry self-cleaning. These mechanisms of nature self-cleaning examples are popular and well-known as well as have been widely applied or exhibited potential applications in our daily life and industrial productions. The mathematical and numerical modelling of the identified self-cleaning mechanisms will be carefully introduced, which will contribute to the rational design and reproducible construction of these functional self-cleaning surfaces. Finally, we will discuss how these materials can be produced, with a focus on scalable manufacturing. We hope this review will strengthen the understanding on nature-inspired self-cleaning surfaces and stimulate interdisciplinary collaboration of material science, biology and engineering.

The goal of this paper is to evaluate from a multi-objective perspective the performance on the detection of catenary support components when using state-of-the-art deep convolutional neural networks (DCNNs). The detection of components is the first step towards a complete automatized monitoring system that will provide actual information about defects in the catenary support devices. A series of experiments in an unified test environment for detection of components are performed using Faster-CNN, R-FCN, SSD, and YOLOv2. Through the comparison of different assessment indicators, such as precision, recall, average precision and mean average precision, the detection performance of the different DCNNs methods for the components of the catenary support devices is analyzed, discussed and evaluated. The experiment results show that among all considered methods, R-FCN is the more suitable for the detection of catenary support components.

Black phosphorus (BP) has received increasing research attention as an anode material in sodium-ion batteries (SIBs), owing to its high capacity, electronic conductivity, and chemical stability. However, it is still challenging for BP-based SIB anodes to achieve a high electrochemical performance utilizing cost-effective materials and synthetic methods. This work presents a sodium-ion anode based on a BP-carbon nanocomposite synthesized from commercial red phosphorus and low-cost super P carbon black. Intimate interactions between BP and carbon are present, which helps to maintain the electrical conduction during cycling and, therefore, a high cycling stability is achieved. It exhibits a high capacity retention of 1381mAhg^-1 for sodium-ion storage after 100 cycles, maintaining 90.5% of the initial reversible capacity. Such high performance/materials cost ratio may provide direction for future phosphorus-based anodes in high energy density SIBs.