基于机器学习的高应力软岩巷道支护抗毁能力预测
Prediction of anti-destructive ability of high stress soft rock roadway support based on machine learning
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摘要: 为了避免传统预测方法在高应力软岩巷道支护抗毁能力预测时出现的问题，提出了基于机器学习的高应力软岩巷道支护抗毁能力预测方法；首先根据岩石物理力学性质，分析软岩强度特性，同时在支护构建模型支持下，分析软岩巷道围岩流变特性，由此研究高应力软岩巷道特性；然后依据软岩塑性变形机理，确定弹性区、塑性硬化区、塑性软化区和塑性流动区对应关系，并计算软岩周围岩层向临空区域运动的合力，由此确定最佳支护时间；最后使用机器学习方法将高应力软岩巷道支护结构简化为葫芦结构模型，并计算外界作用力大小，确定高应力软岩巷道支护结构质量参数矩阵。通过对比3种常规方法可知，该方法在3种不同破坏强度下预测精准度均较高。Abstract: In order to avoid the problems of the traditional prediction methods in the prediction of the support anti-destruction ability of the high stress soft rock roadway, a prediction method of the support anti-destruction ability of the high stress soft rock roadway based on machine learning is proposed. Firstly, according to the physical and mechanical properties of the rock, the strength characteristics of the soft rock are analyzed. At the same time, under the support of support construction model, the rheological characteristics of surrounding rock of soft rock roadway are analyzed, and the characteristics of high stress soft rock roadway are studied. Then, according to the plastic deformation mechanism of soft rock, the corresponding relationship among elastic zone, plastic hardening zone, plastic softening zone and plastic flow zone is determined, and the resultant force of surrounding rock movement to the free area is calculated to determine the optimal support time. The machine learning method simplifies the support structure of high stress soft rock roadway into a gourd structure model, calculates the external force, and determines the support structure quality parameter matrix of high stress soft rock roadway. By comparing the three conventional methods, it can be seen that the prediction accuracy of this method is higher under three kinds of different failure strength.
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