Prediction of hard rock TBM penetration rate using particle swarm optimization

Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R 2 ). The obtained results showed that although all predictive models are able to approximate flyrock, PSO-ANN predictive model can perform better compared to others. Based on R 2 , values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. These results show higher efficiency of the PSO-ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

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“…[51,[61][62][63][64][65]). Due to the weakness of BP in finding the accurate global minimum, the ANN model may achieve undesirable results [66].…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

155

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“…To achieve this, datasets given in previous papers are borrowed (Yagiz 2008;Yagiz et al 2009;Yagiz and Karahan 2011). The database is composed of actual values of TBM penetration rate and rock properties collected from a hard-rock TBM tunnel (the Queens Water Tunnel # 3, Stage 2) of 7.5 km long in New York City (USA).…”

Section: Data Source and Structurementioning

confidence: 99%

“…Torabi et al (2013) investigated two main elements of the TBM performance including the rate of penetration and utilization factor using ANN and regression models. Yagiz and Karahan (2011) predicted the hard-rock TBM penetration rate using the Particle Swarm Optimization (PSO) technique. Mahdevari et al (2014) predicted TBM penetration rates using the Support Vector Regression (SVR).…”

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confidence: 99%

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

Fattahi

Babanouri

2017

Geotech Geol Eng

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One of the main factors in the effective application of a tunnel boring machine (TBM) is the ability to accurately estimate the machine performance in order to determine the project costs and schedule. Predicting the TBM performance is a nonlinear and multivariable complex problem. The aim of this study is to predict the performance of TBM using the hybrid of support vector regression (SVR) and the differential evolution algorithm (DE), artificial bee colony algorithm (ABC), and gravitational search algorithm (GSA). The DE, ABC and GSA are combined with the SVR for determining the optimal value of its user defined parameters. The optimization implementation by the DE, ABC and GSA significantly improves the generalization ability of the SVR. The uniaxial compressive strength (UCS), average distance between planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (a), and intact rock brittleness (BI) were considered as the input parameters, while the rate of penetration was the output parameter. The prediction models were applied to the available data given in the literature, and their performance was assessed based on statistical criteria. The results clearly show the superiority of DE when integrated with SVR for optimizing values of its parameters. In addition, the suggested model was compared with the methods previously presented for predicting the TBM penetration rate. The comparative results revealed that the hybrid of DE and SVR yields a robust model which outperforms other models in terms of the higher correlation coefficient and lower mean squared error.

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“…Two-dimensional numerical analyses were applied to explore the effect of joint orientation and joint spacing on rock fragmentation by TBM cutters by Gong et al (2005Gong et al ( , 2006. Models including ANN and particle swarm optimization (PSO) were developed to estimate the TBM performance using rock mass properties by Yagiz et al (2009b) and also Yagiz and Karahan (2011). Further, Kim (2004) developed a model for prediction of TBM utilization using rock mass properties.…”

Section: Background On Tunnel Boring Machine and Rock Mass Interactionmentioning

confidence: 99%

Application of RMR for Estimating Rock-Mass–Related TBM Utilization and Performance Parameters: A Case Study

2014

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List of symbolsAlpha (a) The angle between tunnel axis and the planes of weakness AR Advanced rate BI Brittleness index BTS Brazilian tensile strength BWI Bit wear index CLI Cutter life index CAI Cherchar abrasivity index CCS Constant cross section CSM Colorado School of Mines Model DPW Distance between planes of weakness DRI Drilling rate index FPI Field penetration index GRRD Geology and rock related downtime MCSM Modified Colorado School of Mines Model NTNU Norwegian University of Sciences and University ORD Other related downtime Q Rock mass classification system Q TBM Modified Q system RME Rock mass excavation index RMi Rock mass index RMR Rock mass rating ROP Rate of penetration RQD Rock quality designation RSR Rock structure rating U (%) Utilization UCS Uniaxial compressive strength 1 Introduction

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Prediction of hard rock TBM penetration rate using particle swarm optimization

Cited by 176 publications

References 24 publications

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

Application of RMR for Estimating Rock-Mass–Related TBM Utilization and Performance Parameters: A Case Study

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