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A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass

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Abstract

The efficiency of tunnel boring machines (TBMs) in underground projects has great significance for the mining and tunneling industries, demanding a reliable estimation of the TBM’s performance in different geotechnical conditions. The current research work attempted to suggest an optimal predictor model of TBM performance as a reliable alternative to experimental and numerical techniques. To achieve this target, three data-mining techniques, namely neural network (NN), gene expression programming (GEP), and multivariate adaptive regression splines (MARS), were employed for modelling the TBM performance, and then the most robust predictive model was optimized via a metaheuristic search method known as whale optimization algorithm (WOA). For the modelling purpose, an experimental database was compiled by performing a field assessment program in a tunneling project in Malaysia and then conducting laboratory testing on the derived rock specimens. Based on the measured experimental data, the six most influential parameters were identified and served as model inputs to predict penetration rate (PR). In order to indicate the capability of the developed GEP and NN models, a stepwise linear regression model, i.e., MARS, was designed for PR prediction as well. The predictive capacity of the constructed models was quantified using a series of statistical indices, i.e., root mean squared error (RMSE), determination coefficient (R2) and variance account for (VAF). Based on the computed indices for testing records, both the proposed GEP and NN models (with RMSE values of 0.1882 and 0.2120 and R2 values of 0.9058 and 0.8735, respectively) yielded more accurate predictive results than the MARS model with RMSE of 0.2553 and R2 of 0.8346. Hence, by achieving the most robust performance compared to the rest, GEP-based model can provide a new practical equation with a high level of accuracy. In other part of this study, the six input parameters of the GEP model and its equation were, respectively, defined as decision variables and objective function for the WOA technique to find the optimum values of PR. As a consequence of optimizing the GEP equation, the maximum value of PR rose from 3.75 m/h to 4.022 m/h, equivalent to an increase of 7.25% in PR value. The findings of this study verified the applicability of the proposed hybrid GEP and WOA approach in the site investigation phase of tunneling projects constructed by TBMs.

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Abbreviations

ANFIS:

Adoptive neuro-fuzzy inference system

TBM:

Tunnel boring machine

PR :

Penetration rate

FCM:

Fuzzy c-means

AR :

Advance rate

NN:

Neural network

GEP:

Gene expression programming

ANN:

Artificial neural network

PSO:

Particle swarm optimization

UCS :

Uniaxial compressive strength

AI:

Artificial intelligence

MLP:

Multilayer perceptron

RQD:

Rock quality designation

F:

Thrust force

RPM:

Revolution per minute

MARS:

Multivariate adaptive regression splines

WZ:

Weathering zone

RMR :

Rock mass rating

R 2 :

Coefficient of determination

RMSE:

Root mean square error

SVR:

Support vector regression

ICA:

Imperialism competitive algorithm

ET:

Expression tree

PSRWT:

Pahang–Selangor Raw Water Transfer

BFs:

Basis functions

BP:

Backpropagation

WOA:

Whale optimization algorithm

GP:

Genetic programming

ICA:

Imperialist competitive algorithm

EA:

Evolutionary algorithm

BTS :

Brazilian tensile strength

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Authors and Affiliations

  1. School of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Zimu Li

  2. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Behnam Yazdani Bejarbaneh

  3. Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121, Heraklion, Athens, Greece

    Panagiotis G. Asteris

  4. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, 15914, Tehran, Iran

    Mohammadreza Koopialipoor

  5. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Danial Jahed Armaghani

  6. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), School of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    M. M. Tahir

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  1. Zimu Li
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  2. Behnam Yazdani Bejarbaneh
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  3. Panagiotis G. Asteris
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Correspondence to Danial Jahed Armaghani.

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Li, Z., Yazdani Bejarbaneh, B., Asteris, P.G. et al. A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass. Soft Comput 25, 11877–11895 (2021). https://doi.org/10.1007/s00500-021-06005-8

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