.png){kind=icon}
返回
Multi-output prediction for TBM operation parameters based on stacking ensemble algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.tust.2024.105960.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Thrust and torque are two key operation parameters for tunnel boring machine (TBM), a rapid and accurate simultaneous prediction or determination of them is essential for TBM construction. In this study, based on the real dataset collected from the Chaoerhe River to Liaohe River project, a stacking algorithm framework is proposed for establishing a multi-output stacked prediction model for TBM thrust and torque. Such model is constructed by integrating the basic models including Random Forest (RF), XGBoost and CatBoost, Support Vector Regression (SVR), and Multilayer Perceptron Neural Network (MLPNN). Both Knowledge Driven and Data Driven approaches were employed to select feature parameters, and the importance of these parameters was analyzed in combination with the RF, XGBoost, and CatBoost algorithms. Meanwhile, the OPTUNA optimizer and Nondominated Sorting Genetic Algorithms III (NSGA-III) were used for hyperparameters optimization of models. The results indicate that the multi-output stacked prediction model outperforms the any of single basic models, with corresponding aCC, aRMSE and aRRMSE are 0.9524, 376.30 and 1.3260, respectively. Moreover, the method of multi-output prediction has about 5.8 times efficiency higher than the single-output prediction. The proposed framework provides an efficient, accurate, and promising method for the simultaneous prediction of multiple operation parameters of TBM.
Keyword:
Hyperparameter optimization
Multi -output prediction
Operation parameters
Stacking ensemble
Tunnel boring machine
期刊
IF:
7.4
论文数:
7.1K
被引数:
3.5W
机构
引用论文
Advance prediction of collapse for TBM tunneling using deep learning method.png)
ENGINEERING GEOLOGY
ENGINEERING GEOLOGYIF8.4
PREAI
分享收藏Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning
PREAI
分享收藏Analysis and assessment of ship collision accidents using Fault Tree and Multiple Correspondence Analysis基于故障树和多重对应分析的船舶碰撞事故分析与评估
PREAI
分享收藏分享收藏Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniquesGEOSCIENCE FRONTIERS
OAAI
GEOSCIENCE FRONTIERSIF8.9
OAAI分享收藏
查看更多内容