In this paper, a parametric constitutive model based on Abel dashpot is established in a simple form and with clear physical meaning to deduce the expression of dynamic mechanical modulus of MREs. Meanwhile, in consideration for the pressure stress on MREs in the experiment of shear mechanical properties or the application to vibration damper, some improvements are made on the particle chain model based on the coupled field. In addition, in order to verify the accuracy of the overall model, five groups of MREs samples based on silicone rubber with different volume fractions are prepared and the MCR51 rheometer is used to conduct the experiment of dynamic mechanical properties based on frequency and magnetic field scanning. Finally, experimental results indicate that the established model fits well with laboratory data; namely, the relationship between the dynamic modulus of MREs and changes in frequency and magnetic field is well described by the model.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.