Corrigendum to “State-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming” [Measurement 126 (2018) 46–57]

Naghadehi, Masoud Zare; Samaei, Masoud; Ranjbarnia, Masoud; Nourani, Vahid

doi:10.1016/j.measurement.2018.09.034

Cited by 2 publications

(1 citation statement)

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“…In another publication in which Bayesian employed by Adoko et al to predict the rate of penetration by applying the features of UCS, BI, DPW, and Alpha, an R2 of 0.93 has been obtained (Adoko et al, 2017), again at least two models, of XGBoost and CatBoost, developed here showed higher accuracy in the prediction from R2 point of view. And the last paper in which almost the same input parameters of UCS, BI, DPW, and Alpha were used for the estimation of the ROP presented by Zare Naghadehi et al (Naghadehi et al, 2018) through GEM, an R2 of 0.7230 calculated which is lower than the R2 of the developed models in this study except for AdaBoost.…”

Section: Comparison With Literaturementioning

confidence: 64%

Estimation of TBM Penetration rate using Gradient Boosting-based Algorithms

Ghorbani

Adoko

Yağız

2023

Preprint

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TBM performance prediction from the rate of penetration (ROP) point of view has yet to draw a lot of attention since it is one of the main challenges for mechanized excavation with tunnel boring machines (TBMs). In this study, five algorithms, Gradient Boosting (GB), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), AdaBoost (AB), and CatBoost (CB) have been conducted to predict the ROP based on the Gradient Boosting theory. Six tunnel cases from different projects were examined to obtain the aim of the research. Dataset developed using those tunnel datasets includes Uniaxial compressive strength (UCS), Rock Type, Distance between Plane of Weakness (DPW), and TBM-related parameter of thrust force (TF). Mentioned Gradient Boosting algorithms were performed to obtain the most accurate results for the study. The developed models showed that XGBoost outperformed the other models, followed by the CatBoost model according to seven different evaluation metrics used to rank the models. After parameter tuning, the GB model outperformed others while those were not improved very much. By using the overall ranking according to the metrics and considering the parameter tuning time, XGBoost and CatBoost presented the first two best performances. Through SHAP values and dependency plots, the features and importance of the inputs showed the TF has the highest impact on the ROP, followed by UCS, Rock Type, and DPW. It is concluded that the XGBoost and CatBoost algorithms could be used for modeling to obtain the TBM penetration for similar rock types.

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Section: Comparison With Literaturementioning

confidence: 64%