A three-dimensional backfill pipeline transport model is developed using the computational fluid dynamics (CFD) technique, which is applied to study the pipeline transport properties of three-phase foam slurry backfill (TFSB). Based on rheological property tests and CFD simulations, the foam phase, pressure, and velocity in the pipeline system are investigated using the CFD mixture method for different bubble volume fractions and bubble diameters. The simulation results indicate that TFSB can maintain a steady state during pipeline transport, experience a markedly reduced pipeline transport resistance, and exhibit better liquidity than conventional cement slurry. Furthermore, as the bubble volume fraction increases, the resistance of the pipeline decreases and the fluidity improves. By contrast, the bubble diameter has little effect on the transport properties of TFSB. The combined results of CFD simulations, slump tests, and strength tests indicate that, when the bubble volume fraction is 15–20 vol %, TFSB can satisfy the necessary strength requirements and exhibit self-flowing transport. The CFD technique provides an intuitive and accurate basis for pipeline transport research and has the potential for wider application in studies of mine backfill.

A simple and accurate evaluation method of broken rock zone thickness (BRZT), which is usually used to describe the broken rock zone (BRZ), is meaningful, due to its ability to provide a reference for the roadway stability evaluation and support design. To create a relationship between various geological variables and the broken rock zone thickness (BRZT), the multiple linear regression (MLR), artificial neural network (ANN), Gaussian process (GP) and particle swarm optimization algorithm (PSO)-GP method were utilized, and the corresponding intelligence models were developed based on the database collected from various mines in China. Four variables including embedding depth (ED), drift span (DS), surrounding rock mass strength (RMS) and joint index (JI) were selected to train the intelligence model, while broken rock zone thickness (BRZT) is chosen as the output variable, and the k-fold cross-validation method was applied in the training process. After training, three validation metrics including variance account for (VAF), determination coefficient (R²) and root mean squared error (RMSE) were applied to describe the predictive performance of these developed models. After comparing performance based on a ranking method, the obtained results show that the PSO-GP model provides the best predictive performance in estimating broken rock zone thickness (BRZT). In addition, the sensitive effect of collected variables on broken rock zone thickness (BRZT) can be listed as JI, ED, DS and RMS, and JI was found to be the most sensitive factor.