融合可掘性指标与支持向量回归的地铁盾构机姿态预测方法
张振1梁杰1张玉龙2陈铁1刘刚1
Metro Shield Machine Attitude Prediction Method Integrating Excavation Index and Support Vector Regression
ZHANG Zhen1LIANG Jie1ZHANG Yulong2CHEN Tie1LIU Gang1
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作者信息:1.中国水利水电第七工程局有限公司， 610213， 成都
2.重庆市铁路(集团)有限公司， 401120， 重庆
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Affiliation:1.Sinohydro Bureau 7 Co., Ltd., 610213, Chengdu, China
2.Chongqing Railway Group Co., Ltd., 401120, Chongqing, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.20253059
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中图分类号/CLCN:U455.43
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栏目/Col:土建工程
摘要:
［目的］地铁盾构机姿态偏差控制不当会对成型隧道的服役状态造成不利影响，预知施工过程中盾构机的姿态是及时调整其姿态的前提，而现有预测模型多存在可解释性差、数据量要求较高等问题。需研究新的盾构机姿态预测方法。［方法］为增加模型的可解释性，引入了表征盾构机在所处地层掘进状态的可掘性指标SE(掘进比能)，作为模型的特征参数，并利用在小样本学习方面具有优势的支持向量回归方法建立盾构机姿态预测模型。利用K折交叉验证进行超参数调优，评估预测模型的性能和泛化能力。［结果及结论］将融合模型应用于重庆轨道交通27号线工程实例中，表征盾构机姿态的4项参数的预测结果的拟合优度R2分别为0.94、0.94、0.90、0.87。融合可掘性指标后，支持向量回归模型的平均预测精度提高了11.96%；相较于反向传播神经网络模型，融合模型预测精度提升了6.41%。支持向量回归模型通过引入具有物理意义的特征参数，能够更准确地预测盾构机姿态，可为施工过程中实时调整盾构机姿态提供有效支撑。
Abstracts:
［Objective］ Improper control of metro shield machine attitude deviation will adversely affect the service status of the formed tunnel. Predicting the attitude of shield machine in the construction process is the premise for timely adjusting the attitude, but most of the existing prediction models have problems such as poor interpretability and high data requirements. ［Method］ To increase the interpretability for models, the excavation index SE (specific energy), representing the excavation state of the shield machine in surrounding stratum, is introduced as a characteristic parameter of the model. The shield machine attitude prediction model is established using support vector regression method, which has advantages in small sample learning. K-fold cross validation is used to tune hyperparameters and evaluate the performance and the generalization ability of the prediction model. ［Result & Conclusion］ The integrated model is applied to Chongqing Rail Transit Line 27 engineering case, the goodness-of-fit R2 of the prediction results of the four parameters characterizing shield machine attitude are 0.94, 0.94, 0.90, and 0.87, respectively. The integration of excavation index improves the average prediction accuracy of support vector regression model by 11.96%. Compared to the back propagation neural network model, this integrated model improves prediction accuracy by 6.41%. By introducing characteristic parameters with physical significance, the support vector regression model can more accurately predict the shield machine attitude and provide effective support for real-time shield machine attitude adjustments during the construction process.
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