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Potential efficacy and application of a new statistical meta based-model to predict TBM performance
International Journal of Mining Reclamation and Environment ( IF 2.7 ) Pub Date : 2021-02-14 , DOI: 10.1080/17480930.2021.1878087
Behrooz Keshtegar 1 , Mahdi Hasanipanah 2 , Troung Nguyen-Thoi 3, 4 , Saffet Yagiz 5 , Hassan Bakhshandeh Amnieh 6
Affiliation  

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.



中文翻译:

基于新的统计元模型预测 TBM 性能的潜在功效和应用

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。

更新日期:2021-02-14
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