ABSTRACT

This study constructs and verifies a new statistical meta based-model to predict tunnel-boring machine (TBM) performance, namely, polynomial chaos expansion (PCE). To test the validity of the proposed PCE, two well-known mathematical models, namely, response surface method (RSM) and multivariate adaptive regression spline (MARS) were developed. According to the results, it can be found that the PCE model, with a coefficient of determination (R²) of 0.843, was superior in comparison with the RSM and MARS models as well as those formerly presented in the literature for the same database and rock conditions.

Abbreviations: ANFIS: Adaptive Neuro-Fuzzy Inference System; ANN: Artificial Neural Networks; AR: Advance Rate; BI: Rock Brittleness; BTS: Brazilian Tensile Strength; CP: Cutterhead Power; CT: Cutterhead Torque; d: Modified Agreement Index; DNN: Deep Neural Networks; DPW: Distance between Planes of Weakness; ICA: Imperialist Competitive Algorithm; MAE: Mean Absolute Error; MARS: Multivariate Adaptive Regression Spline; NSE: Modified Nash and Sutcliffe Efficiency; NTNU: Norwegian Institute of Technology; PCE: Polynomial Chaos Expansion; PR: Penetration Rate; PSI: Point Strength Index; PSO: Particle Swarm Optimisation; R2: Coefficient of Determination; RF: Random Forests; RMR: Rock Mass Rating; RMSE: Root Mean Square Error; RQD: Rock Quality Designation; RSM: Response Surface Method; RSR: Rock Structure Rating; SE: Specific Energy; SVR: Support Vector Regression; TBM: Tunnel-Boring Machine; TF: Thrust Force; UCS: Uniaxial Compressive Strength; WZ: Weathering Zone; α: Planes Of weakness.

摘要

本研究构建并验证了一种新的基于统计元的模型来预测隧道掘进机 (TBM) 的性能，即多项式混沌扩展 (PCE)。为了测试所提出的 PCE 的有效性，开发了两个众所周知的数学模型，即响应面法 (RSM) 和多元自适应回归样条 (MARS)。根据结果​​可以发现，PCE 模型的决定系数 ( R ² ) 为 0.843，与 RSM 和 MARS 模型以及以前在同一数据库的文献中提出的模型相比，具有优越性。岩石条件。

缩写： ANFIS：自适应神经模糊推理系统；ANN：人工神经网络；AR：预付款；BI：岩石脆性；BTS：巴西抗拉强度；CP：刀头功率；CT：刀头扭矩；d：修改后的协议索引；DNN：深度神经网络；DPW：弱点之间的距离；ICA：帝国主义竞争算法；MAE：平均绝对误差；MARS：多元自适应回归样条；NSE：修正的纳什和萨特克利夫效率；NTNU：挪威理工学院；PCE：多项式混沌展开；PR：渗透率；PSI：点强度指数；PSO：粒子群优化；R2：决定系数；RF：随机森林；RMR：岩石质量等级；RMSE：均方根误差；RQD：岩石质量标志；RSM：响应面法；RSR：岩石结构等级；SE：比能；SVR：支持向量回归；TBM：隧道掘进机；TF：推力；UCS：单轴抗压强度；WZ：风化区；α：弱点位面。