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Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance

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Abstract

A proper planning schedule for tunnel boring machine (TBM) construction is considered as a necessary and difficult task in tunneling projects. Therefore, prediction of TBM performance with high degree of accuracy is needed to prepare a suitable planning schedule. This study aims to predict the advance rate of TBMs using optimized extreme learning machine (ELM) model with six particles swam optimization (PSO) techniques. Hence, six deterministically adaptive models, including time-varying acceleration (TAC)–PSO–ELM, improved PSO–ELM, Modified PSO–ELM, TAC–MeanPSO–ELM, improved MeanPSO–ELM, and Modified MeanPSO–ELM were developed. A number of performance criteria along with ranking system were used to identify the best model. The results showed that modified MeanPSO–ELM achieved the highest cumulative ranking (56), while the modified PSO–ELM achieved the lowest cumulative ranking (51). For training phase, improved PSO–ELM and TAC–PSO–ELM achieved the highest ranking (30) for each. The TAC–MeanPSO–ELM obtained the lowest ranking in the testing phase (29). Concerning the coefficient of determination (R2), modified PSO–ELM, improved PSO–ELM, TAC–PSO–ELM, and modified MeanPSO–ELM showed a similar behavior and achieved 0.97 for training and 0.96 for testing phases. Two models, including improved MeanPSO–ELM and TAC–MeanPSO–ELM achieved the same R2 of 0.96 for both training and testing phases. The findings of this study suggest that the hybridization of ELM and PSO may result in more accurate results than single ELM model to predict the TBM advance rate.

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Abbreviations

ANFIS:

Adoptive neuro-fuzzy inference system

ELM:

Extreme learning machine

TBM:

Tunnel boring machine

PR:

Penetration rate

FCM:

Fuzzy c–means

GMDH:

Group modeling of data handling

SLFN:

Single-hidden layer feedforward neural network

AR:

Advance rate

TAC:

Time-varying acceleration

ANN:

Artificial Neural Network

MPSO:

Modified PSO

P:

Population size

w :

Inertia weight

C :

Exploitation operator

PSO:

Particle swarm optimization

UCS:

Uniaxial compressive strength

XGB:

Extreme gradient boosting

AI:

Artificial intelligence

GEP:

Gene expression programming

GP:

Genetic programming

ML:

Machine learning

RQD:

Rock quality designation

TFC:

Trust force per cutter

RPM:

Revolution per minute

WZ:

Weathering zone

RMR:

Rock mass rating

R 2 :

Coefficient of determination

DA:

Deterministically adaptive

FPI:

Field penetration index

RMSE:

Root mean square error

α :

Planes of weakness

SVR:

Support vector regression

ICA:

Imperialism competitive algorithm

PSRWT:

Pahang Selangor raw water transfer

WOA:

Whale optimization algorithm

gbest:

Global solution

pbest:

Local solution

UA:

Uncertainty analysis

RMSE:

Root mean square error

MAE:

Mean absolute error

SVM:

Support vector machine

DNN:

Deep neural network

MFO:

Moth flame optimization

BTS:

Brazilian tensile strength

DPW:

Distance between planes of weakness

BI:

Rock brittleness

α:

Angle between plane of weakness and TBM-driven direction

Q:

Quartz content

PSI:

Peak slope index

Qu:

Quartz percentage

Rs:

Rotational speed of TBM

Js:

Joint spacing

Jc:

Joint condition

SE:

Specific energy

CP:

Cutterhead power

CT:

Cutterhead torque

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Acknowledgements

The first author would like to acknowledge the Science and Technology Planning Project of Chongqing Education Commission (KJQN201804305) (JG-KJ-2019-006). In addition, the corresponding author would like to acknowledge Geotropik Centre, Universiti Teknologi Malaysia, for supporting this study during data collection phase.

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Authors and Affiliations

  1. Department of Transportation and Municipal Engineering, Chongqing Jianzhu College, Chongqing, 400072, China

    Jie Zeng

  2. School of Computing Science and Engineering, Vellore Institute of Technology Bhopal University, Kothri-Kalan, Near Ashta, Bhopal-Indore Highway, Bhopal, Madhya Pradesh, 466114, India

    Bishwajit Roy

  3. Department of Civil Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna, 800005, India

    Deepak Kumar

  4. Civil Engineering Department, College of Engineering, University of Sulaimani, Kurdistan Region, Iraq

    Ahmed Salih Mohammed

  5. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Danial Jahed Armaghani

  6. Geotropik-Centre of Tropical Geoengineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Danial Jahed Armaghani & Edy Tonnizam Mohamad

  7. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Jian Zhou

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  1. Jie Zeng
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Zeng, J., Roy, B., Kumar, D. et al. Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance. Engineering with Computers 38 (Suppl 5), 3811–3827 (2022). https://doi.org/10.1007/s00366-020-01225-2

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