Establishing an accurate model to predict burst pressure is desired, which has been developed for decades. Although various models have been developed, errors unavoidably appear in the prediction of burst pressures because of the uncertainty in both input variables and nonlinear relationship of such variables to the burst pressure. Consequently, machine learning models, which is a data-driven approach, are potential alternatives. In this paper, various machine learning models such as Random Forest, Support Vector Machine, and Artificial Neural Network are examined to predict the burst pressure, gathering databases available in the literature. The applications of these models are investigated to identify the advantages and limitations of the models. The machine learning models showed a significant improvement in the prediction of the burst pressures compared to the available reference models. However, some drawbacks of the models should be carefully considered, including an increase of error with the unfamiliar data and the fluctuations within the overall trend in the parametric study.

期望建立精确的模型以预测爆破压力，该模型已经开发了数十年。尽管已经开发了各种模型，但是由于输入变量的不确定性以及这些变量与爆破压力的非线性关系，在爆破压力的预测中不可避免地会出现误差。因此，作为数据驱动方法的机器学习模型是潜在的替代方法。在本文中，研究了各种机器学习模型，例如随机森林，支持向量机和人工神经网络，以预测爆破压力，并收集文献中可用的数据库。对这些模型的应用进行了研究，以确定模型的优点和局限性。与可用的参考模型相比，机器学习模型在爆破压力的预测上显示出显着的改进。但是，应仔细考虑模型的一些缺点，包括因不熟悉的数据而导致的误差增加以及参数研究中总体趋势内的波动。