在双轴压缩下使用深度学习方法预测岩石材料的应力-应变行为
Changsheng Li1,2,3, Xinsong Zhang3
1State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Efficient Development, Beijing, China.
PloS one
|April 29, 2025
概括
这项研究引入了长期短期记忆自编码器 (LSTM-AE),以提高模拟中预测岩石材料应力-张力曲线的准确性. 与其他模型相比,LSTM-AE方法表现出卓越的性能,为增强材料行为预测提供了有价值的参考.
科学领域:
- 地质技术工程 地质技术工程
- 计算力学 计算力学 计算力学
- 人工智能的人工智能
背景情况:
- 预测岩石材料的应力-应变曲线对于工程应用至关重要.
- 现有的深度学习模型与岩石复杂的机械特性作斗争,导致预测准确性不足.
- 离散元素数值模拟被广泛使用,但需要准确的材料行为模型.
研究的目的:
- 提出一种新的深度学习方法,用于准确预测岩石材料应力-应变曲线.
- 为了解决当前深度学习方法在捕获复杂岩石力学方面的局限性.
- 通过改进材料属性预测,提高离散元素数值模拟的可靠性.
主要方法:
- 开发了一种长短期内存自编码器 (LSTM-AE),利用LSTM网络进行编码器和解码器组件.
- 编码器从输入数据中提取关键特征,而解码器生成预测的应力-应变曲线序列.
- 使用平均平方误差 (MSE),根平均平方误差 (RMSE),平均绝对误差 (MAE) 和确定系数 (R2) 评估性能.
主要成果:
- 拟议的LSTM-AE网络在预测准确性方面明显优于标准的LSTM,循环神经网络 (RNN),逆向传播神经网络 (BPNN) 和XGBoost模型.
- 通过对10个专门数据集的成功预测,证实了LSTM-AE的稳定性.
- 定量指标证实了LSTM-AE在预测应力-应变曲线方面的卓越性能.
结论:
- 该LSTM-AE模型在预测岩石材料的应力-应变曲线方面取得了重大进展,其性能优于现有的深度学习和机器学习方法.
- 这项研究为提高数字模拟中材料行为预测的准确性提供了有价值的参考.
- 建议进行进一步的研究,以探索LSTM-AE对大型数据集的可扩展性及其对实验室实验数据的应用.
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