煤炭工程 ›› 2026, Vol. 58 ›› Issue (7): 203-209.doi: 10.11799/ce202607025

• 研究探讨 • 上一篇    下一篇

基于机器学习的煤样塑性流动本构模型研究

王中山,孙经平   

  1. 临沂矿业集团菏泽煤电有限公司 郭屯煤矿,山东 菏泽 274000
  • 收稿日期:2026-01-24 修回日期:2026-06-14 出版日期:2026-07-15 发布日期:2026-08-03
  • 通讯作者: 王中山 E-mail:wangzhongshan2026@163.com

Research on constitutive model for plastic flow of coal samples based on machine learning#br#

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  • Received:2026-01-24 Revised:2026-06-14 Online:2026-07-15 Published:2026-08-03
  • Contact: shan zhongwang E-mail:wangzhongshan2026@163.com

摘要:

针对郭屯煤矿煤柱在开挖屈服后易发生塑性流动的问题,揭示峰后循环加载条件下煤样的塑性变形规律及力学响应特征。开展不同围压及加载路径下煤样的循环加卸载试验,系统分析剪切屈服后煤样的轴向应力-应变响应特征;基于试验数据,利用机器学习方法的优秀泛化能力,构建描述煤样复杂塑性行为的本构模型,并设置动态弹性模量和损伤演化参数,以表征试验中煤样的前期压实与破坏过程。结果表明:随着围压的增大,煤样峰值强度从37.69MPa提升至68.36MPa,此外峰后的残余强度、塑性变形能力及整体抗变形能力均呈现增强趋势。循环加卸载阶段,应力-应变呈现明显滞回效应,第1至第3个滞回环面积逐次减小,累计衰减率达10.56%~68.45%,表明塑性耗散能逐渐降低;随围压由2MPa增至8MPa,总滞回环面积从0.051增大至0.153,塑性变形能力显著增强。基于试验数据,构建了机器学习本构模型,模型预测与试验结果的最大相对误差为5.97%,验证了模型的合理性。

关键词: 循环加卸载, 神经网络, 塑性流动下的本构模型, 围压效应

Abstract:

To address the issue that coal pillars in the Guotun Coal Mine are prone to plastic flow after excavation-induced yielding, this study aims to reveal the plastic deformation laws and mechanical response characteristics of coal samples under post-peak cyclic loading conditions. Cyclic loading–unloading tests were conducted on coal samples under different confining pressures and loading paths to systematically analyze the axial stress–strain response characteristics after shear yielding. Based on the experimental data, a constitutive model describing the complex plastic behavior of coal samples was constructed by leveraging the excellent generalization capability of machine learning methods. Dynamic elastic modulus and damage evolution parameters were incorporated into the model to characterize the early-stage compaction and failure processes of coal samples observed in the tests. The results show that as the confining pressure increases, the peak strength of coal samples increases from 37.69 MPa to 68.36 MPa. Furthermore, the post-peak residual strength, plastic deformation capacity, and overall deformation resistance all exhibit an increasing trend. During the cyclic loading–unloading stages, the stress–strain curves display a pronounced hysteresis effect. The areas of the first to third hysteresis loops decrease successively, with cumulative attenuation rates ranging from 10.56% to 68.45%, indicating a gradual reduction in plastic dissipation energy. As the confining pressure increases from 2 MPa to 8 MPa, the total hysteresis loop area increases from 0.051 to 0.153, demonstrating a significant enhancement in plastic deformation capacity. Based on the experimental data, a machine learning constitutive model was constructed. The maximum relative error between model predictions and experimental results is 5.97%, validating the rationality of the model. The proposed model can efficiently solve the plastic behavior of coal samples and effectively describe their complex plastic mechanical characteristics, thereby providing a theoretical basis for the stability analysis of coal pillars under similar conditions.

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