Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

Zhou, Jian; Qiu, Yingui; Armaghani, Danial Jahed; Zhang, Wengang; Li, Chuanqi; Zhu, Shuangli; Tarinejad, Reza

doi:10.1016/j.gsf.2020.09.020

Cited by 196 publications

(44 citation statements)

References 77 publications

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“…M features are randomly selected from all feature sets during tree generation, and then an optimal eigenvalue mtry is selected as the split variable value according to the criterion of the maximum information gain ratio. Through the establishment of the RF model, the trend of ntree and the mean-square error is observed, and the decision tree corresponding to the minimum root-mean-square error (RMSE) is chosen as the best ntree value -that is, the number of regression trees (Zhou et al, 2021b). ( 3) Training model establishment.…”

Section: Optimal Parameter Determination and Training Model Establishmentmentioning

confidence: 99%

Risk Prediction and Diagnosis of Water Seepage in Operational Shield Tunnels Based on Random Forest

Liu

Chen

Zhang

et al. 2021

Journal of Civil Engineering and Management

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Water seepage (WS) is a paramount defect during tunnel operation and directly affects the operational safety of tunnels. Effectively predicting and diagnosing WS are problems that urgently need to be solved. This paper presents a standard and an evaluation index system for WS grades and constructs a sample dataset from monitoring recoreds for demonstration purposes. First, we use bootstrap resampling to build a random forest (RF) seepage risk prediction model. Second, the optimal branch and parameters are selected by the 5-fold cross-validation method to establish the RF prediction training model. Additionally, to illustrate the effectiveness of the method, the operational stage of Wuhan Metro Line 3 in China is taken as a case study. The results conclude that the segment spalling area, crack width, and loss rate of the rebar cross-section have a strong influence on WS. Finally, the test data are predicted, and the prediction result error index is calculated. Compared with the predictions of some traditional machine learning methods, such as support vector machines and artificial neural networks, RF prediction has the highest accuracy and is the closest to the true value, which demonstrates the accuracy of the model and its application potential.

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Section: Optimal Parameter Determination and Training Model Establishmentmentioning

confidence: 99%

Risk Prediction and Diagnosis of Water Seepage in Operational Shield Tunnels Based on Random Forest

Liu

Chen

Zhang

et al. 2021

Journal of Civil Engineering and Management

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“…For the comparison of the model performance, three performance metrics including R 2 , RMSE, and VAF were applied, and a prediction model can be considered as the best model when R 2 � 1, RMSE � 0, and VAF � 100. Meanwhile, the value of these performance metrics can be calculated using the following formula [32,[82][83][84][85][86][87]: where N, y, y, and y pre are the number of datasets, the average PPV values, the actual PPV values, and the predicted PPV values, respectively.…”

Section: Performance Of Various Modelsmentioning

confidence: 99%

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Jiang¹,

Xu²,

Chen

et al. 2021

Advances in Civil Engineering

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The accurate determination of blast-induced ground vibration has an important significance in protecting human activities and the surrounding environment. For evaluating the peak particle velocity resulting from the quarry blast, a robust artificial intelligence system combined with the salp swarm algorithm (SSA) and Gaussian process (GP) was proposed, and the SSA was used to find the optimal hyperparameters of the GP here. In this regard, 88 datasets with 9 variables including the ratio of bench height to burden (H/B) and the ratio of spacing to burden (S/B) were selected as the input variables, while peak particle velocity (PPV) was measured. Then, an ANN model, an SVR model, a GP model, an SSA-GP model, and three empirical models were established, and the predictive performance was evaluated by using the root-mean-square error (RMSE), determination coefficient (R2), value account for (VAF), Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and the run time. After comparing, it is found that the proposed SSA-GP yielded a superior performance and the ratio of bench height to burden (H/B) was the most sensitive variable.

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“…For the structural design and vibration reduction optimization of the cutterhead system. Liu, 14 Hasanpour et al, 15,16 and Afrasiabi et al 17–23 were devoted to studying various performance parameters that affect the work of TBM, such as rock parameters and machine parameters to establish prediction theoretical models of TBM performance. Methods such as “penalty factor” and “pso-ann hybrid model” were proposed to estimate the performance of TBM, which enriched the theoretical model of intelligent prediction.…”

Section: Introductionmentioning

confidence: 99%

Research on rock breaking mechanism and load characteristics of TBM cutter based on discrete element method

Ling

Cheng

Tong

et al. 2021

Advances in Mechanical Engineering

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Tunnel boring machine (TBM) is a large-scale tunnel engineering equipment, which has unparalleled advantages in safety and work efficiency. The cutter is subjected to complex and variable random impact loads, resulting in damage to bearings, cutter rings, and cutter shafts. Therefore, based on the discrete element simulation platform and experiment, this paper established the cutter rock simulation model based on the experimental data, and analyzed the rock breaking process, cutter load magnitude, and variation law. The research results show that when the cutter pole diameter decreases from 200 mm, 100 mm, and 0 mm in sequence, the rolling force increases accordingly, and the maximum increase of the rolling force is 56%. Meanwhile the lateral force decreases and the maximum average decrease value is 26%. Moreover, the rolling force showed a downward trend with the increase of the cutter spacing. When the cutter spacing is increased from 80 mm to 120 mm, the average rolling force decreases by 51%, and the maximum value rises slightly, increasing by 2%. And, the lateral force tends to increase, with an increase of 17% and the maximum lateral force value increase was 29%. The results can provide load input reference for TBM cutter layout, cutterhead structure design, and performance evaluation.

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Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

Cited by 196 publications

References 77 publications

Risk Prediction and Diagnosis of Water Seepage in Operational Shield Tunnels Based on Random Forest

Risk Prediction and Diagnosis of Water Seepage in Operational Shield Tunnels Based on Random Forest

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Research on rock breaking mechanism and load characteristics of TBM cutter based on discrete element method

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