In the loose and fractured coal seam with particularly low uniaxial compressive strength (UCS), driving a roadway is extremely difficult as roof falling and wall spalling occur frequently. To address this issue, the jet grouting (JG) technique (high-pressure grout mixed with coal particles) was first introduced in this study to improve the self-supporting ability of coal mass. To evaluate the strength of the jet-grouted coal-grout composite (JG composite), the UCS evolution patterns were analyzed by preparing 405 specimens combining the influential variables of grout types, curing time, and coal to grout (C/G) ratio. Furthermore, the relationships between UCS and these influencing variables were modeled using ensemble learning methods i.e. gradient boosted regression tree (GBRT) and random forest (RF) with their hyperparameters tuned by the particle swarm optimization (PSO). The results showed that the chemical grout composite has higher short-term strength, while the cement grout composite can achieve more stable strength in the long term. The PSO-GBRT and PSO-RF models can both achieve high prediction accuracy. Also, the variable importance analysis demonstrated that the grout type and curing time should be considered carefully. This study provides a robust intelligent model for predicting UCS of JG composites, which boosts JG design in the field.

在具有特别低的单轴抗压强度（UCS）的松散破裂的煤层中，由于经常发生顶板倒塌和墙体剥落，驾驶巷道极为困难。为了解决这个问题，本研究首先引入了喷射注浆（JG）技术（高压注浆与煤颗粒混合），以提高煤团的自支撑能力。为了评估喷射灌浆煤浆复合材料（JG复合材料）的强度，结合了灌浆类型，固化时间和煤浆比（C / G）的影响变量，准备了405个标本，分析了UCS的演化模式。此外，UCS与这些影响变量之间的关系使用集成学习方法建模，即 梯度增强回归树（GBRT）和随机森林（RF）的超参数通过粒子群优化（PSO）进行了调整。结果表明，化学灌浆复合材料具有较高的短期强度，而水泥灌浆复合材料可以长期获得更稳定的强度。PSO-GBRT和PSO-RF模型都可以实现较高的预测精度。此外，变量重要性分析表明，应仔细考虑灌浆类型和固化时间。这项研究提供了一个强大的智能模型来预测JG复合材料的UCS，这将促进该领域的JG设计。PSO-GBRT和PSO-RF模型都可以实现较高的预测精度。此外，变量重要性分析表明，应仔细考虑灌浆类型和固化时间。这项研究提供了一个强大的智能模型来预测JG复合材料的UCS，这将促进该领域的JG设计。PSO-GBRT和PSO-RF模型都可以实现较高的预测精度。此外，变量重要性分析表明，应仔细考虑灌浆类型和固化时间。这项研究提供了一个强大的智能模型来预测JG复合材料的UCS，这将促进该领域的JG设计。