Multi-layered cement-hydrogel composite with high toughness, low thermal conductivity, and self-healing capability.

The inherent quasi-brittleness of cement-based materials, due to the disorder of their hydration products and pore structures, present significant challenges for directional matrix toughening. In this work, a rigid layered skeleton of cement slurry was prepared using a simplified ice-template method, and subsequently flexible polyvinyl alcohol hydrogel was introduced into the unidirectional pores between neighboring cement platelets, resulting in the formation of a multi-layered cement-based composite. A toughness improvement of over 175 times is achieved by the implantation of such hard-soft alternatively layered microstructure. The toughening mechanism is the stretching of hydrogels at the nano-scale and deflections of micro-cracks at the interfaces, which avoid stress concentration and dissipate huge energy. Furthermore, this cement-hydrogel composite also exhibits a low thermal conductivity (around 1/10 of normal cement) and density, high specific strength and self-healing properties, which can be used in thermal insulation, seismic high-rise buildings and long-span bridges.

Research on the Combination of Firefly Intelligent Algorithm and Asphalt Material Modulus Back Calculation.

The modulus of asphalt pavement material is a necessary parameter for the design, strength measuring and stability evaluation of asphalt pavement. To get more precise test data for asphalt pavement material modulus, a new modulus back calculation method is proposed in this article, named as the Firefly Asphalt Back Calculation Method (FABCM). This novel method uses the firefly optimization algorithm, which is a kind of particle swarm intelligence algorithm imitating the information transfer process among fireflies. To demonstrate the reliability and stability of FABCM, and to study the feasibility of multi-parameter modulus back calculation methods, this article used theoretical deflection curves calculated by BISAR3.0 and the actual measurement data of deflection curves and vertical pressures on the subgrade top surfaces on the full-scale test circular track in the Research Institute of Highway, Ministry of Transport (RIOHTrack) to conduct a modulus back calculation. The results show that FABCM only takes 0.5-1 s for each calculation, and the back calculation errors in the verification of FABCM are mostly smaller than 1%, which means that the firefly optimization algorithm was modified effectively in this article. Moreover, this article also indicates some key factors influencing the accuracy of modulus back calculation, and several reasonable suggestions to the application of modulus back calculation.

Correlation Analysis between Mechanical Properties and Fractions Composition of Oil-Rejuvenated Asphalt.

To clarify the intrinsic relationship between the mechanical properties of asphalt and its fraction composition, the SARA fraction composition and six macroscopic mechanical properties (critical cracking temperature (TCR), fatigue life (Nf), non-recoverable creep (Jnr3.2), penetration, ductility, and softening point) were investigated for 16 asphalt samples. Fraction contents of asphaltene and aromatic are strongly correlated with TCR and ductility (R2 > 0.92) that characterize the ability of asphalt to adapt to deformation at low and medium temperatures. Heavy fraction (asphaltene and resins) content is also strongly correlated with (R2 > 0.90) penetration and Jnr3.2 that characterize the resistance of the asphalt to overall deformation at medium and high temperatures. To express the changes in the four fractions simultaneously with one indicator, a statistic, average deviation of the fractions between the given asphalt and its original (marked σ), is introduced in this study to characterize the degree of asphalt aging based on the fraction changes. It normalizes the four simultaneous change indicators (percentage of SARA fractions) during asphalt aging into one indicator. This new indicator has a strong correlation with several mechanical performance indicators of asphalt, where it is strongly correlated with TCR (R2 > 0.90), ductility, and penetration, which are also well correlated with Jnr3.2 (R2 > 0.85), Nf (R2 > 0.75), and softening point (R2 > 0.75).