Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners.

A thermal-structural-electromagnetic coupling model through the stochastic perturbation technique (SP-TSE) is proposed to deal with the stochastic analysis of the Active Phased Array Antenna (APAA) performance considering random material parameters. Firstly, stochastic perturbation technique is employed to analyze the temperature distribution and thermal deformation of APAA, in which the randomness of both thermal and structural material parameters is considered. Secondly, without any additional assumptions, the obtained thermal deformation results are directly introduced into the antenna radiation pattern function to analyze the electromagnetic performance (EMP) of APAA. Finally, the effectiveness of the proposed SP-TSE model is fully verified and the results show that this model can obtain very accurate stochastic multi-physics information of APAA and save a huge amount of computational cost compared with the Monte Carlo method.

Wear is one of the most common failure forms of gear transmission system. In this paper, a dynamic wear reliability evaluation method for gear system, which subjects to stochastic external load, is proposed on the basis of the Markov diffusive process. The stochastic load is considered as the combination of constant load and random noise. The failure is defined as the wear depth of the maximum wear point of gear under the constant load that exceeds a specific threshold. The maximum wear point and relative sliding velocity are obtained by deterministic wear analysis. The stochastic noise is assumed as Gaussian white noise; hence, the wear depth can be described as Markov diffusive process, and the transition probability is governed by Fokker–Planck–Kolmogorov (FPK) equation. With the transition probability function, the wear life and dynamic reliability of gear systems with different noise spectral densities are predicted. The results reveal that the wear depth obeys normal distribution and becomes more and more scattered with noise spectral density increases. The wear life does not obey normal distribution, and the effect of noise spectral density on mean life is neglectable while on a standard deviation of life is considerable.