Cross Project Conversion Relationship of Key Parameters of TBM Rock Breaking
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摘要: TBM信息化施工中采集了海量数据，通过数据挖掘建立机器学习模型，是实现TBM智能化的前提.然而在TBM新建工程初期，由于数据量稀少导致机器学习模型预测效果不佳；同时由于TBM设备结构和刀盘直径存在差异，基于历史工程训练得到的机器学习模型也并不适用于新建工程.为了解决这一瓶颈问题，基于单刀受力分析、经验方法和扭剪实验模型等多种换算关系推导得到了仅与刀具数量和刀盘直径有关的物理不变量，利用由不变量组成的转换方案，可以对新建工程数据进行转换；之后针对围岩分类和机器学习模型上的应用效果，比选出最佳的破岩关键参数转换方案；进而采用遗传算法，以比选得到的转换方案不变量作为初值，迭代搜索出适合当前工程的最优转换方案不变量.研究结果表明，引绰工程（新建工程）数据经过不变量的转换后输入到引松工程（历史工程）机器学习模型，其刀盘扭矩T和刀盘推力F预测结果的拟合优度R2分别达到了0.84和0.70.本研究采用该转换方案不变量，可将不同工程的TBM施工数据归一化，将其统一到同一个框架下进行分析，实现了基于历史工程数据训练得到的机器学习模型指导新建工程施工.研究结果可为TBM机器学习模型跨工程应用提供参考.Abstract: A large amount of data have been collected in TBM information construction, and the establishment of machine learning model through data mining is the premise of realizing TBM intelligence. However, at the initial stage of TBM construction, the prediction performance of machine learning model is poor due to the lack of data; At the same time, due to the differences in TBM equipment structure and cutterhead diameter, the machine learning model based on historical projects training is not suitable for new projects. In order to solve this bottleneck problem, the physical invariants only related to the number of cutters and the diameter of the cutter head are derived from the force analysis of a single cutter, the empirical method and the torsional shear experimental model. The new projects data can be converted by using the conversion scheme composed of invariants; Then, the conversion scheme of key parameters of rock breaking with the best application performance in surrounding rock classification and machine learning model is selected; Then, the genetic algorithm is used to iteratively search the optimal conversion scheme invariant which is suitable for the current project. The research results show that the data of Yinchao project (new project) are input into the machine learning model of Yinsong project (historical project) after "invariant" conversion, and the prediction performance R2 of cutterhead torque T and cutterhead thrust F reach 0.84 and 0.70 respectively. By using this conversion scheme invariant, the TBM construction data of different projects can be normalized and analyzed under the same framework, and the machine learning model trained based on historical project data is realized to guide the construction of new projects. The research results can provide reference for the cross project application of TBM machine learning model.
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图 1 引松工程永吉号TBM (a)和引绰工程草原平安号TBM (b)
Fig. 1. Yongji TBM of Yinsong project (a) and Grassland Peace TBM of Yinchao project (b)
图 3 引松工程和引绰工程中关键破岩参数统计特征
a.刀盘推力F分布统计；b.刀盘扭矩T分布统计；c.贯入度p分布统计；d.刀盘转速n分布统计；e.推进速度v分布统计
Fig. 3. Statistical characteristics of key parameters of rock breaking in Yinsong project and Yinchao project
图 4 引松工程刀盘扭矩T的机器学习模型预测效果
Fig. 4. Prediction performance of machine learning model of cutterhead torque T in Yinsong project
图 5 引松工程刀盘扭矩T预测机器学习模型在新建引绰工程中的应用效果
Fig. 5. Application performance of machine learning model for cutterhead torque T prediction in new Yinchao project
图 7 不同转换方案条件下的围岩分类效果对比
Fig. 7. Comparison of surrounding rock classifications performances under different conversion schemes
图 8 不同转换方案条件下的机器学习模型预测效果对比
Fig. 8. Comparison of prediction performances of machine learning models under different conversion schemes
图 11 转换关系在引绰工程刀盘扭矩T预测模型中的应用效果
Fig. 11. Application performance of conversion relation in the prediction model of cutterhead torque T of Yinchao project
图 12 转换关系在引绰工程刀盘推力F预测模型中的应用效果
Fig. 12. Application performance of conversion relation in the prediction model of cutterhead thrust F of Yinchao project
表 1 引松工程和引绰工程TBM设备设计参数
Table 1. Design parameters of TBM equipment of Yinsong project and Yinchao project
项目名称 引松工程 引绰工程 设备名称 永吉号 草原平安号 TBM型式 开敞式 开敞式 刀盘直径D (mm) 7 930 5 200 额定刀盘推力F (kN) 23 260 11 340 额定刀盘扭矩T (kN·m) 8 410 3 340 最大刀盘转速nmax (r/min) 7.6 11.45 滚刀数量N (把) 56 34 摩擦阻力Ff (kN) 5 000 4 000 摩擦扭矩Tf (kN·m) 200 50 
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表 2 TBM破岩关键参数
Table 2. Key parameters of rock breaking
参数 类型 备注 刀盘转速n (r/min) 控制参数 刀盘在掘进状态下的旋转速度，受TBM操作手控制 推进速度v (mm/min) 控制参数 主推油缸的伸出速度，受TBM操作手控制 贯入度p (mm/r) 衍生参数 刀盘每掘进一周的掘进进尺 刀盘扭矩T (kN·m) 响应参数 刀盘在掘进状态下产生的总扭矩 刀盘推力F (kN) 响应参数 由主机油缸施加给主机大梁的向前推进力 注：贯入度p为衍生参数，其计算公式为：p = v/n.
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表 3 破岩关键参数候选转换方案汇总
Table 3. Summary of candidate conversion schemes for key parameters of rock breaking
组别 转换方案 刀盘转速n 刀盘扭矩T 刀盘推力F 不变量符号 转换关系 不变量符号 转换关系 不变量符号 转换关系 试验组 ① $ {n}_{V} $ $ \frac{{n}_{1}}{{n}_{2}}=\frac{{D}_{2}}{{D}_{1}} $ $ {f}_{r}^{k} $ $ \frac{{T}_{1}}{{T}_{2}}=\frac{{D}_{1}{N}_{1}}{{D}_{2}{N}_{2}} $ $ \overline{{f}_{n}} $ $ \frac{{F}_{1}}{{F}_{2}}=\frac{{N}_{1}}{{N}_{2}} $ ② $ {n}_{V} $ $ \frac{{n}_{1}}{{n}_{2}}=\frac{{D}_{2}}{{D}_{1}} $ $ {S}_{T} $ $ \frac{{T}_{1}}{{T}_{2}}=\frac{{D}_{1}^{2}}{{D}_{2}^{2}} $ $ {S}_{F} $ $ \frac{{F}_{1}}{{F}_{2}}=\frac{{N}_{1}}{{N}_{2}} $ ③ $ {n}_{V} $ $ \frac{{n}_{1}}{{n}_{2}}=\frac{{D}_{2}}{{D}_{1}} $ $ {C}_{u} $ $ \frac{{T}_{1}}{{T}_{2}}=\frac{{D}_{1}^{3}}{{D}_{2}^{3}} $ $ {p}_{p} $ $ \frac{{F}_{1}}{{F}_{2}}=\frac{{D}_{1}^{2}}{{D}_{2}^{2}} $ 对照组 ④ $ {n}_{0} $ $ {n}_{1}={n}_{2} $ $ {f}_{r}^{k} $ $ \frac{{T}_{1}}{{T}_{2}}=\frac{{D}_{1}{N}_{1}}{{D}_{2}{N}_{2}} $ $ \overline{{f}_{n}} $ $ \frac{{F}_{1}}{{F}_{2}}=\frac{{N}_{1}}{{N}_{2}} $ ⑤ $ {n}_{0} $ $ {n}_{1}={n}_{2} $ $ {S}_{T} $ $ \frac{{T}_{1}}{{T}_{2}}=\frac{{D}_{1}^{2}}{{D}_{2}^{2}} $ $ {S}_{F} $ $ \frac{{F}_{1}}{{F}_{2}}=\frac{{N}_{1}}{{N}_{2}} $ ⑥ $ {n}_{0} $ $ {n}_{1}={n}_{2} $ $ {C}_{u} $ $ \frac{{T}_{1}}{{T}_{2}}=\frac{{D}_{1}^{3}}{{D}_{2}^{3}} $ $ {p}_{p} $ $ \frac{{F}_{1}}{{F}_{2}}=\frac{{D}_{1}^{2}}{{D}_{2}^{2}} $
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表 4 不同围岩等级条件下的破岩关键参数均值统计
Table 4. Mean statistics of key parameters of rock breaking under different surrounding rock grades
名称 Ⅱ类围岩 Ⅲ类围岩 Ⅳ类围岩 Ⅴ类围岩 引松 引绰 引松 引绰 引松 引绰 引松 引绰 刀盘推力$ F $(kN) 15 181.72 8 641.82 13 987.69 8 504.79 10 236.44 7 746.12 10 160.86 7 594.79 刀盘扭矩$ T $(kN·m) 2 782.66 1 059.12 2 609.13 962.56 1 910.83 727.17 1 912.56 589.80 刀盘转速n (r/min) 6.92 8.72 6.7 8.53 5.66 7.99 5.01 8.02
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表 5 以比选值为初值的遗传算法优选结果
Table 5. Optimization results of genetic algorithm with comparison value as initial value
刀盘转速n 刀盘扭矩T 刀盘推力F $ {n}_{V}\mathrm{比}\mathrm{选} $值 $ {n}_{V}\mathrm{优}\mathrm{选} $值 $ {S}_{T}\mathrm{比}\mathrm{选} $值 $ {S}_{T}\mathrm{优}\mathrm{选} $值 $ {S}_{F}\mathrm{比}\mathrm{选} $值 $ {S}_{F}\mathrm{优}\mathrm{选} $值 0.66 0.81 2.34 2.27 1.65 1.83
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表 6 三种转换关系跨工程机器学习模型应用效果
Table 6. Application performance of three conversion relations across project machine learning model
应用方案 转换关系 评价指标 R2 MAPE 刀盘扭矩T 刀盘推力F 刀盘扭矩T 刀盘推力F 直接泛化应用 ‒ ‒0.37 ‒5.53 28.8% 46.4% 按照不变量比选值转换 $ {n}_{V}= $0.66, $ {S}_{T} $=2.34, $ {S}_{F}= $1.65 0.80 0.68 8.6% 8.4% 按照不变量优选值转换 $ {n}_{V}= $0.81, $ {S}_{T} $=2.27, $ {S}_{F}= $1.83 0.84 0.70 8.2% 7.8%
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