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Prediction of rock mass rating using neural network with an improved rider optimization algorithm

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Abstract

Evaluation of TBM performance is critical for the choice of TBM specifications and tunnel design. In the past decades, the hypothetical schemes depending on the rock fragmentation process and the experimental models up to field surveillance as well as machine performance are the two main methods. Traditional and conventional approaches for rock mass rate (RMR) prediction usually consider excessive parameters and the accuracies are far from actual values. A new RMR prediction model based on the optimized neural network (NN) is designed. To improve the prediction accuracy, this paper proposed a new self-adaptive rider optimization algorithm (SA-ROA), which applied optimization logic to train the NN by updating the weight as wave velocity (Vp), transverse wave velocity (Vs), Vp/Vs, statistics (Stat), orientation, magnitude, polarity, wave type, and metre. Finally, the RMR prediction analysis of the adopted NN-SA-ROA model is compared to the conventional and traditional classifiers with varied learning percentages: 50%, 60%, 70%, and 80% for three data sets, respectively. Subsequently, the performance of the proposed work is verified using other approaches based on error analysis. The predicted mean absolute errors (MAEs) and the mean absolute percentage errors (MAPEs) of SA-ROA are smaller than conventional and traditional schemes. The results show that the proposed method can successfully predict the actual RMR.

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Abbreviations

TBM:

Tunnel boring machines

RMR:

Rock mass rate

DPW:

Distance among planes of weakness

SA-ROA:

Self adaptive rider optimization algorithm

UCS:

Uniaxial compressive strength

BI:

Brittleness index

BTS:

Brazilian tensile strength

ROP:

Rate of penetration

NN:

Neural network

BPNN:

Back propagation neural network

SA:

Simulated annealing

AI:

Artificial intelligence

XOR:

Exclusive OR

SVR:

Support vector regression

SST:

Stacked single-target

DF-HFIS:

Defuzzification-free hierarchical fuzzy inference system

MRA:

Multivariate regression analysis

MAE:

Mean absolute error

MSE:

Mean squared error

MAPE:

Mean absolute percentage error

MSRE:

Mean squared root error

RMSRE:

Relative MSRE

MSPE:

Mean squared percentage error

RMSPE:

Root MSPE

MARE:

Mean absolute relative error

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Acknowledgements

The authors are grateful to the editor and the anonymous reviewers for their very helpful and inspiring comments. This research was supported by the National Natural Science Foundation of China (No. 51774132, 51774131, 51974118) and the Natural Science Foundation of Hunan Province (Grants Nos. 2020JJ5188).

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Authors and Affiliations

  1. School of Resource, Environment and Safety Engineering, Hunan University of Science and Technology, 411201, Xiangtan, Hunan, People’s Republic of China

    Wei Chen, Wen Wan & Wenqing Peng

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  1. Wei Chen
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Correspondence to Wei Chen.

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Chen, W., Wan, W. & Peng, W. Prediction of rock mass rating using neural network with an improved rider optimization algorithm. Evol. Intel. 15, 2567–2579 (2022). https://doi.org/10.1007/s12065-021-00606-w

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