In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with Gray Wolf Optimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. Gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy.

The inflammability of asphalt road will promote fire spread in the tunnel and produce lots of toxic smoke. To improve the fire resistance of asphalt pavement, mineral powder flame retardants are generally replaced by flame retardants in equal amounts. In this study, the effects of the synergistic flame retardancy system of halloysite nanotubes (HNTs) and conventional flame retardants (CFR) on the flame retardancy performance and mechanism of asphalt were investigated. Firstly, the flame retardancy properties of the HNTs and CFR composite modified asphalt were investigated based on the Cleveland open cup method (COC), Limiting oxygen index meter (LOI), and Cone calorimeter tests (CCTs). Then, the flame retardancy mechanism of the modified asphalt was studied based on Thermogravimetric analyzer (TGA), Fourier-transform infrared (FTIR), and Scanning electron microscopy (SEM). The results show that adding HNTs could improve the flame retardancy of the CFR modified asphalt binder. When 1 wt % HNTs and 8 wt % CFR were used, the limiting oxygen index of asphalt increased by 40.1%, the ignition temperature increased by 40 °C, while the heat release rate, total heat release, the smoke production rate, total smoke release, and other parameters decreased with varying degrees. Based on TG, FTIR, and SEM, the targeted flame retardancy mechanism and synergistic effect of HNTs/CFR flame retardancy system were revealed and summarized as three stages: (1) Stage 1, aluminum hydroxide (ATH) absorbs heat through thermal decomposition and inhibits the decomposition of lightweight components in asphalt; (2) Stage 2, aluminum diethyl phosphate (ADP) decomposes and produces organic phosphoric acid, which catalyzes crosslinking and ring thickening of asphalt and the quenching effect of phosphorus free radicals to block the combustion; and (3) Stage 3, HNTs plays an important role in increasing the integrity and density of the barrier layer. In addition, the Al₂O₃ produced by the decomposition of ATH, the carbon layer formed by the ADP catalyzed pitch, and HNTs play a significant synergistic effect in the formation of the barrier layer. Thus, the combination of HNTs and CFR has been proved to be a prospective flame retardancy system for asphalt.

This study aimed to research the whole stress–strain curves of coral Fly Ash-Slag Alkali-Activated Concrete (CAAC) in different strength grades. Fly ash-slag alkali-activated concrete was used as cementing materials to produce coral aggregate concrete. A monotonic loading test was conducted on the prismatic specimens to obtain elastic modulus (E_c), Poisson’s ratio (μ) and the constitutive relationship of CAAC under uniaxial compression. When the strain of CAAC reached the maximum value, the specimen was split and damaged rapidly. As the strength grade increased, the ratio of residual stress (σ_ri) to peak stress (σ_0i) decreased in the range of 0.17–0.28. The E_c of CAAC increased gradually, and μ increased to the peak value and then decreased. According to the test results, the constitutive equation of CAAC can be expressed by piecewise expression, which can better reflect all the experimental characteristics. It was also found that CAAC has many similar characteristics with coral concrete and lightweight aggregate concrete. To improve the strength and toughness of CAAC, some fibers, such as organic fiber, can be added to expand the application of CAAC in engineering projects.