Rockburst is a major instability issue faced by underground excavation projects, which is induced by the instantaneous release of a large amount of strain energy stored in rock mass. Because of its disastrous damage to infrastructures and facilities, more and more studies have been focused on rockburst prediction. However, due to highly nonlinear relationships between the occurrence of rockburst and potential triggering factors, traditional mechanism-based prediction methods have great difficulties in providing the reliable results. In this study, a multivariate adaptive regression splines (MARS) model and a novel deep forest algorithm were applied to predict and classify rockburst intensity of a database including 344 rockburst cases collected worldwide. The t-distributed stochastic neighbor embedding method (t-SNE) was utilized for nonlinear dimensionality reduction and visualization of the original input features. After that, the Gaussian mixture model was adopted to relabel original data to determine relative intensity of these rockburst cases. Then, the MARS model and deep forest model were constructed with these newly labeled data. Their performances were compared with some widely used machine learning methods, such as random forest, extreme gradient boost, and ANN model. The results clearly proved the capability of the proposed models to assess and forecast rockburst risk. It also proved that these approaches should be used as cross-validation against each other. The Shapley additive explanations method was adopted to investigate the relative importance of input features of the developed MARS model. The result shows that σ_θ and σ_c are the most important features for rockburst intensity prediction, where σ_θ is the tangential stress around underground opening and σ_c refers to uniaxial compressive strength of the rock.

岩爆是地下开挖工程面临的主要失稳问题，它是由储存在岩体中的大量应变能瞬时释放引起的。由于其对基础设施和设施的灾难性破坏，越来越多的研究集中在岩爆预测上。然而，由于岩爆发生与潜在触发因素之间的高度非线性关系，传统的基于机理的预测方法难以提供可靠的结果。在本研究中，应用多元自适应回归样条 (MARS) 模型和新型深森林算法对包括全球 344 个岩爆案例的数据库的岩爆强度进行预测和分类。该Ť-分布式随机邻居嵌入方法（t-SNE) 用于原始输入特征的非线性降维和可视化。之后，采用高斯混合模型对原始数据进行重新标记，以确定这些岩爆情况的相对强度。然后，利用这些新标记的数据构建 MARS 模型和深森林模型。将它们的性能与一些广泛使用的机器学习方法进行了比较，例如随机森林、极端梯度提升和 ANN 模型。结果清楚地证明了所提出的模型评估和预测岩爆风险的能力。它还证明，这些方法应该用作相互之间的交叉验证。采用 Shapley 加性解释方法来研究开发的 MARS 模型的输入特征的相对重要性。结果表明，σ_θ和σ _c是岩爆强度预测的最重要特征，其中σ _θ是地下洞口周围的切向应力，σ _c是指岩石的单轴抗压强度。