Abstract In this paper, a newly proposed approach on horizontal stress estimation from borehole breakout data is presented. In a previous study, a machine learning model was developed, capable of estimating maximum horizontal stress (σH) from breakout data accurately. However, due to the limitation in experimental data, it was difficult to obtain the minimum horizontal stress (σh) reliably. In this study, a series of breakout tests on Hydrostone-TB specimens was carried out to investigate the influence of σh and vertical stress (σv) on breakout geometries, as these two parameters were rarely studied previously. Results revealed that both breakout angular span and depth decrease with increasing σh or σv, although the influence of σh is more significant. Based on experimental results from this paper as well as the literature, nine failure criteria were examined on the prediction accuracy of σh providing the magnitude of σH. Except for one model, all the other eight failure criteria consider the influence of σv, as indicated in experimental findings. However, none of the failure criteria yielded reasonable σh estimations. To overcome this problem, an Artificial Neural Network (ANN) model was developed from the experimental dataset. Once the model was constructed, it was examined against twenty-three field data, and yielded an acceptable average error rate of 15.88% on σh considering the easily accessible breakout data. Then a comparative analysis on σH estimation was performed via a number of approaches, namely, Kriging, ANN, and constitutive modeling. Results revealed that the use of the Mogi-Coulomb failure criterion is the most reliable approach for σH estimation, with an average error rate of 6.82%. Overall, this newly presented ‘ANN’-‘Mogi-Coulomb’ approach to horizontal stress estimation shows reasonable prediction results, which is expected to be improved in future studies by including additional data.

中文翻译：

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一种通过人工神经网络和岩石破坏准则从钻孔破裂数据估计水平主应力大小的组合方法

摘要 在本文中，提出了一种新提出的从钻孔突破数据估计水平应力的方法。在之前的一项研究中，开发了一种机器学习模型，能够准确地从突破数据中估计最大水平应力 (σH)。然而，由于实验数据的限制，很难可靠地获得最小水平应力（σh）。在本研究中，对 Hydrostone-TB 试样进行了一系列爆破测试，以研究 σh 和垂直应力 (σv) 对爆破几何形状的影响，因为这两个参数以前很少研究。结果表明，尽管 σh 的影响更显着，但随着 σh 或 σv 的增加，突破角跨度和深度均减小。根据本文和文献的实验结果，在 σh 的预测精度上检查了九个失效标准，提供了 σH 的大小。除了一个模型，所有其他八个失效标准都考虑了 σv 的影响，如实验结果所示。然而，没有一个失效标准产生合理的 σh 估计。为了克服这个问题，从实验数据集开发了人工神经网络 (ANN) 模型。模型构建完成后，将根据 23 个现场数据对其进行检查，考虑到易于获取的突破数据，在 σh 上产生了 15.88% 的可接受平均错误率。然后通过多种方法对 σH 估计进行比较分析，即克里金法、人工神经网络和本构模型。结果表明，使用 Mogi-Coulomb 失效准则是最可靠的 σH 估计方法，平均错误率为 6.82%。总体而言，这种新提出的用于水平应力估计的“ANN”-“Mogi-Coulomb”方法显示出合理的预测结果，预计将在未来的研究中通过包含更多数据得到改进。