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The purpose of this paper is to establish a new two-phase Discrete Element Method (DEM) model to investigate the movement of fresh concrete which consists of mortar and aggregate. The established DEM model was adopted to simulate the mixing process of fresh concrete based on the commercial software package PFC3D. The trajectories of particles and particle clusters were recorded to analyze the mixing behavior from different scales. On one hand, the macro-scale movement was obtained to make the mixing process visualization. On the other hand, the relative micro movement of the single particle and particle clusters was also monitored to further study the mixing mechanism of the fresh concrete.

A new two-phase DEM model was designed to simulate the movement of fresh concrete which consists of mortar and aggregate. The linear-spring dashpot model was used to model all the contacts between particle and particle/wall to characterize the viscidity of fresh concrete. Moreover, two sets of parallel bond models were employed to characterize the contact between the mortar particles and mortar/coarse aggregate particles, namely the pbond1 and pbond2. The hybrid treatment enables the current DEM model to handle the yield behavior.

The mixing process of fresh concrete is mainly composed by the transportation in the x-direction and the overturn and fall off in the y- and z-directions. With these movements in different directions, the concrete particles can be fully mixed in the mixing drum.

A new two-phase DEM model was proposed and used to simulate the mixing process of fresh concrete. The outcomes of the simulation would be helpful for making the transporting truck visualization and the movement behavior of fresh concrete observable. The model can provide dynamic information of particles to reveal the interaction mechanism of fresh concrete in the truck mixer which is extremely difficult to obtain on-line in physical experiments or building site.

The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual network is used to improve the diagnostic accuracy of the bearing fault intelligent diagnosis model in the environment of high signal noise.

A bearing vibration data generation model based on conditional GAN (CGAN) framework is proposed. The method generates data based on the adversarial mechanism of GANs and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

The method proposed in this paper is verified by the western reserve data set and the truck bearing test bench data set, proving that the CGAN-based data generation method can form a high-quality augmented data set, while the CGAN-based and improved residual with attention mechanism. The diagnostic model of the network has better diagnostic accuracy under low signal-to-noise ratio samples.

A bearing vibration data generation model based on CGAN framework is proposed. The method generates data based on the adversarial mechanism of GAN and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.