Development of overbreak prediction models in drill and blast tunneling using soft computing methods

Mottahedi, Adel; Sereshki, Farhang; Ataei, Mohammad

doi:10.1007/s00366-017-0520-3

Cited by 52 publications

(15 citation statements)

References 38 publications

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“…ANNs, LMRA, NMRA, SVM, adaptive neuro-fuzzy inference system, and FLM, to predict the relationship between the causing factors and overbreak data [67]. The results indicate that specific drilling, specific charge, and rock mass rating are the most effective factors on the overbreak.…”

Section: E Overbreak Prediction Mottahedi Et Al Applied Various Metmentioning

confidence: 97%

“…Nevertheless, the abovementioned conclusions can provide a reference in the tunnel engineering field. [21] WNN>ANN [22] GP>SVM>ANN [78] SVMFF>ANNFF [79] MLP>MRA [76] PSO-ANN>ANN [93] ANFIS>CFT>ANN [5] FLM>BPNN>MRA [20] ICA-ANN>ANN>LMRA [94] TL>RNN>SVM [49] PSO-SVR>PSO-BPNN>PSO-ELM [53] Stability BPNN>LRM [56] MLP>RBFNN [58] TBM performance BPNN>NMRA [59] ANN>SPSS [31] ANN>MRA [60] ANN>LMRA [30] ICA-ANN>PSO-ANN>ANN [61] SVM>ANN [39] PSO-ANN>ICA-ANN>ANN [24] Geological conditions ANN>XGBoost, CatBoost, RF, DT, SVR, KNN, BLR [18] Overbreak ANN>NMRA>LMRA [67] ANFIS>FLM>ANN>SVM>NMRA>L MRA [38] GA-ANN>ANN [68] ABC-ANN>ANN [40] ABC-ANN>ANN [11] Tunnel convergence MLP>RBFNN>MRA [36] SVM>ANN [35] MLP>MARS [69] MLP>SPSS [70] Rockburst and flying rocks PSO-ANN>ICA-ANN>GA-ANN Note: The '>' means the performance of the left model outperforms the right one.…”

Section: A Characteristics Of Ann-based Modelsmentioning

confidence: 99%

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Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Wang

Wei

et al. 2020

IEEE Access

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Due to the lack of living space and the increase in population, there has been a construction boom in the underground space to improve the quality of human life. Tunnel engineering plays a vital role in the development of underground space. In addition to traditional methods, some intelligent methods such as artificial neural networks (ANNs) have been applied to various problems in the tunnel domain in recent years. This paper systematically reviews the application of ANNs from different aspects of tunnel engineering. It reveals that the backpropagation algorithm (BPA) and Levenberg-Marquardt algorithm (LMA) are the most widely used. Due to the limitations of some original models, some scholars use optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the original ANNs to obtain better prediction results. A comparison between the ANN-based methods and methods like statistical methods is conducted. Finally, the following conclusions can be drawn: (1) The recommended ratio of the training set and test set is 3:1; (2) The advantage of optimized ANNs is not apparent when the optimization algorithm varies. Additionally, the performance of ANNs is always better than that of statistical methods.

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Section: E Overbreak Prediction Mottahedi Et Al Applied Various Metmentioning

confidence: 97%

Section: A Characteristics Of Ann-based Modelsmentioning

confidence: 99%

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Wang

Wei

et al. 2020

IEEE Access

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“…Bell shape and trapezoidal MFs are widely used in the engineering literature (e.g. Mottahedi et al 2018;Suthar 2020). B/D and X B graphs indicate two subgroups (Fig.…”

Section: Fundamentals Of Anfis Modelmentioning

confidence: 99%

Prediction of flyrock throw distance in quarries by variable selection procedures and ANFIS modelling technique

Hudaverdi

2021

Preprint

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This study suggests application of variable reduction procedures for flyrock prediction. It was aimed to create robust and non-complex predictive models. Eleven operational blast parameters and rock mass properties were measured in an aggregate quarry. Dominant parameters for flyrock occurrence were determined by multivariate statistical methods. Two parallel ANFIS models were developed for flyrock prediction. The first ANFIS model was constructed based on the results of stepwise regression. Burden-hole diameter ratio, in-situ block size and specific charge are the input parameters of ANFIS 1. The second ANFIS model was created based on the results obtained by factor analysis. Burden-hole diameter ratio, bench height- burden ratio, number of holes and charge weight are used as input parameters for ANFIS 2. The calculated mean absolute percentage errors are lower than eight percent for the ANFIS predictions. The median absolute errors are lower than 5 meters. The study also investigates alternative accuracy measures to evaluate forecasting performance. Standardized errors, normalized errors and Nash–Sutcliffe Efficiency (NSE) were found to be useful for model validation. It is concluded that more than a single model can be created for a specific site. Pre-statistical analysis for variable reduction increases performance of the predictive models. Burden appeared to be a significant parameter for flyrock throw.

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“…AI models have been widely applied by many researchers in several tunneling projects [27][28][29][30][31][32]. Typical AI models include artificial neural network (ANN) [33][34][35], fuzzy logic (FL) model [36], Genetic algorithm (GA) [37,38], and adaptive neuro-fuzzy inference system (ANFIS) [39,40]. Minh et al [41] developed the fuzzy logic model as an alternative method that was more accurate in comparison with four statistical regression models to predict the TBM performance.…”

Section: Introductionmentioning

confidence: 99%

Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm

et al. 2019

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The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.

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Development of overbreak prediction models in drill and blast tunneling using soft computing methods

Cited by 52 publications

References 38 publications

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Prediction of flyrock throw distance in quarries by variable selection procedures and ANFIS modelling technique

Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm

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