Solid backfilling is an environmentally friendly method of coal mining. Backfilling hydraulic supports are important devices in solid backfill mining. In order to speed up the design and manufacturing process of them, this paper proposes a method of hydraulic support reformation and evaluation. The mechanical models of top-coal caving hydraulic support and backfilling hydraulic support were established to analyze the stress characteristics of the main structure. ABAQUS was used to establish the 3D numerical model to analyze the load-bearing characteristics, as well as verify the strength requirement. In addition, the structural characteristics, compaction characteristics, geological adaptation characteristics, and interference characteristics of the structure were analyzed using motion simulation. After reforming, the ramming force is just above 2.0 MPa, and the utilization ratio of the old parts of the top-coal caving hydraulic support is more than 50%, which saved the costs of the new backfilling hydraulic support and reduced the waste of idle top-coal caving hydraulic support. The successful application of the backfilling hydraulic support in the Tangshan mine showed the rationality of the reform design.

Previously conducted studies have established that surface subsidence is typically avoided by filling coal mined-out areas with solid waste. Backfilling hydraulic supports are critically important devices in solid backfill mining, whose operating performance can directly affect backfill mining efficiency. To accurately evaluate the operating performance, this paper proposes hybrid machine learning models for the operating states. An analysis of the factors that influence operating performance provides eight indices for evaluating backfilling hydraulic supports. Based on the data obtained from the Creo simulation model and field measurement, six hybrid models were constructed by combining swarm intelligent algorithms and support vector machines (SVM). Models of the SVM optimized by the modified sparrow search algorithm have shown improved convergence performance. The results show that the modified model has a prediction accuracy of 95.52%. The related evaluation results fit well with the actual support intervals of the backfilling hydraulic support.