Automated Recognition Model of Geomechanical Information Based on Operational Data of Tunneling Boring Machines

Yang, Haiqing; Song, Kanglei; Zhou, Jiayuan

doi:10.1007/s00603-021-02723-5

Cited by 129 publications

(12 citation statements)

References 46 publications

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“…As described in the JSA methodology, the number of populations is considered a controllable parameter of JSA. To specify the best number of jellyfish, several JSA-ANN-ANN models with different populations, i.e., 25,50,75,100,125,150,175,200,225, and 250, were trained. The revealed results in Figure 12, the parametric investigation indicated that the number of jellyfish of 200 could achieve the best accuracy and higher system capacity.…”

Section: Jsa-annmentioning

confidence: 99%

“…Nonetheless, using statistically based methods to solve a highly non-linear issue such as flyrock and rock fragmentation can be a challenging and difficult endeavor. Many attempts are conducted to solve engineering problems by using artificial intelligence and soft computing techniques [16][17][18][19][20][21][22][23][24][25]. Therefore, the application of intelligent machine learning, such as artificial intelligence (AI) and soft computing (SC), could have relevance and benefit when attempting to solve issues related to this type.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

et al. 2023

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One of the most undesirable consequences induced by blasting in open-pit mines and civil activities is flyrock. Furthermore, the production of oversize boulders creates many problems for the continuation of the work and usually imposes additional costs on the project. In this way, the breakage of oversize boulders is associated with throwing small fragments particles at high speed, which can lead to serious risks to human resources and infrastructures. Hence, the accurate prediction of flyrock induced by boulder blasting is crucial to avoid possible consequences and its’ environmental side effects. This study attempts to develop an optimized artificial neural network (ANN) by particle swarm optimization (PSO) and jellyfish search algorithm (JSA) to construct the hybrid models for anticipating flyrock distance resulting in boulder blasting in a quarry mine. The PSO and JSA algorithms were used to determine the optimum values of neurons’ weight and biases connected to neurons. In this regard, a database involving 65 monitored boulders blasting for recording flyrock distance was collected that comprises six influential parameters on flyrock distance, i.e., hole depth, burden, hole angle, charge weight, stemming, and powder factor and one target parameter, i.e., flyrock distance. The ten various models of ANN, PSO–ANN, and JSA–ANN were established for estimating flyrock distance, and their results were investigated by applying three evaluation indices of coefficient of determination (R2), root mean square error (RMSE) and value accounted for (VAF). The results of the calculation of evaluation indicators revealed that R2, values of (0.957, 0.972 and 0.995) and (0.945, 0.954 and 0.989) were determined to train and test of proposed predictive models, respectively. The yielded results denoted that although ANN model is capable of anticipating flyrock distance, the hybrid PSO–ANN and JSA–ANN models can anticipate flyrock distance with more accuracy. Furthermore, the performance and accuracy level of the JSA–ANN predictive model can estimate better compared to ANN and PSO–ANN models. Therefore, the JSA–ANN model is identified as the superior predictive model in estimating flyrock distance induced from boulder blasting. In the final, a sensitivity analysis was conducted to determine the most influential parameters in flyrock distance, and the results showed that charge weight, powder factor, and hole angle have a high impact on flyrock changes.

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Section: Jsa-annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

et al. 2023

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“…From a monetary perspective, the application of ML techniques is also profitable, because it reduces the costs related to lab tests for ascertaining the UCS. It is important to note that the mentioned ML techniques have been used and applied to solve science and engineering problems [ 21 , 23 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ].…”

Section: Introductionmentioning

confidence: 99%

Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests

et al. 2023

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The accurate estimation of rock strength is an essential task in almost all rock-based projects, such as tunnelling and excavation. Numerous efforts to create indirect techniques for calculating unconfined compressive strength (UCS) have been attempted. This is often due to the complexity of collecting and completing the abovementioned lab tests. This study applied two advanced machine learning techniques, including the extreme gradient boosting trees and random forest, for predicting the UCS based on non-destructive tests and petrographic studies. Before applying these models, a feature selection was conducted using a Pearson’s Chi-Square test. This technique selected the following inputs for the development of the gradient boosting tree (XGBT) and random forest (RF) models: dry density and ultrasonic velocity as non-destructive tests, and mica, quartz, and plagioclase as petrographic results. In addition to XGBT and RF models, some empirical equations and two single decision trees (DTs) were developed to predict UCS values. The results of this study showed that the XGBT model outperforms the RF for UCS prediction in terms of both system accuracy and error. The linear correlation of XGBT was 0.994, and its mean absolute error was 0.113. In addition, the XGBT model outperformed single DTs and empirical equations. The XGBT and RF models also outperformed KNN (R = 0.708), ANN (R = 0.625), and SVM (R = 0.816) models. The findings of this study imply that the XGBT and RF can be employed efficiently for predicting the UCS values.

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“…In recent years, several studies have presented the successful usage of intelligent algorithms to simulate complex problems in civil and geotechnical engineering [16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Several scholars have highlighted the applicability of these techniques in predicting pilerelated issues, e.g., pile capacity, settlement, lateral deflection [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time

2022

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Prediction of pile bearing capacity has been considered an unsolved problem for years. This study presents a practical solution for the preparation and maximization of pile bearing capacity, considering the effects of time after the end of pile driving. The prediction phase proposes an intelligent equation using a genetic programming (GP) model. Thus, pile geometry, soil properties, initial pile capacity, and time after the end of driving were considered predictors to predict pile bearing capacity. The developed GP equation provided an acceptable level of accuracy in estimating pile bearing capacity. In the optimization phase, the developed GP equation was used as input in two powerful optimization algorithms, namely, the artificial bee colony (ABC) and the grey wolf optimization (GWO), in order to obtain the highest bearing capacity of the pile, which corresponds to the optimum values for input parameters. Among these two algorithms, GWO obtained a higher value for pile capacity compared to the ABC algorithm. The introduced models and their modeling procedure in this study can be used to predict the ultimate capacity of piles in such projects.

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Automated Recognition Model of Geomechanical Information Based on Operational Data of Tunneling Boring Machines

Cited by 129 publications

References 46 publications

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests

Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time

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