The selection of printing parameters for 3D printing can dramatically affect the dynamic performance of components such as polymer spur gears. In this paper, the performance of 3D printed gears has been optimised using a machine learning process. A genetic algorithm (GA)–based artificial neural network (ANN) multi-parameter regression model was created. There were four print parameters considered in 3D printing process, i.e. printing temperature, printing speed, printing bed temperature and infill percentage. The parameter setting was generated by the Sobol sequence. Moreover, sensitivity analysis was carried out in this paper, and leave-one cross validation was applied to the genetic algorithm-based ANN which showed a relatively accurate performance in predictions and performance optimisation of 3D printed gears. Wear performance of 3D printed gears increased by 3 times after optimised parameter setting was applied during their manufacture.

In landslide analysis, reliable prediction of landslide’s influence zone is significantly difficult due to the inherent heterogeneity of soils and their spatially varying geological properties. In this paper, a probabilistic framework is proposed to evaluate the landslide hazard zoning through the prediction of their influence zone considering the effects of heterogeneous soil properties. The material point method is used to simulate the large deformations during landslide failure. The spatial variation in the shear strength parameters of soils at undrained conditions is modeled by random fields, which are discretized by the Cholesky matrix decomposition method to incorporate the effects of the soil spatial heterogeneity on the postfailure deformations. Furthermore, a practical landslide hazard zoning is conducted to quantitively evaluate the level of disaster for facilities or structures located in the vicinity of the slope by using the exceedance probability of influence distance and runout distance. Five categories based on different thresholds of exceedance probability are used to visualize the area potentially affected by the landslide. To demonstrate the capability of the stochastic assessment framework, a benchmark example of the heterogeneous clayey slope is simulated, and the associated hazard zone is predicted and discussed; it presents a practical procedure for improving the landslide influence zone prediction and provides new insights for hazard zoning.