Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm

Asl, Parvin Faraji; Monjezi, Masoud; Hamidi, Jafar Khademi; Armaghani, Danial Jahed

doi:10.1007/s00366-017-0535-9

Cited by 74 publications

(19 citation statements)

References 43 publications

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“…As far as the project client can estimate the size of damage zones (i.e., crushed and cracked zones) as a function of input parameters such as rock properties and explosive characteristics, the optimal values of input parameters can be obtained using a blast design optimization. This optimization can be done through a try-and-error process to obtain the optimal values of target parameters or can be mathematically implemented in an optimization algorithm [29][30][31].…”

Section: Estimation Of Induced Damagementioning

confidence: 99%

Estimation of Damage Induced by Single-Hole Rock Blasting: A Review on Analytical, Numerical, and Experimental Solutions

et al. 2020

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This paper presents a review of the existing models for the estimation of explosion-induced crushed and cracked zones. The control of these zones is of utmost importance in the rock explosion design, since it aims at optimizing the fragmentation and, as a result, minimizing the fine grain production and recovery cycle. Moreover, this optimization can reduce the damage beyond the set border and align the excavation plan with the geometric design. The models are categorized into three groups based on the approach, i.e., analytical, numerical, and experimental approaches, and for each group, the relevant studies are classified and presented in a comprehensive manner. More specifically, in the analytical methods, the assumptions and results are described and discussed in order to provide a useful reference to judge the applicability of each model. Considering the numerical models, all commonly-used algorithms along with the simulation details and the influential parameters are reported and discussed. Finally, considering the experimental models, the emphasis is given here on presenting the most practical and widely employed laboratory models. The empirical equations derived from the models and their applications are examined in detail. In the Discussion section, the most common methods are selected and used to estimate the damage size of 13 case study problems. The results are then utilized to compare the accuracy and applicability of each selected method. Furthermore, the probabilistic analysis of the explosion-induced failure is reviewed using several structural reliability models. The selection, classification, and discussion of the models presented in this paper can be used as a reference in real engineering projects.

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Section: Estimation Of Induced Damagementioning

confidence: 99%

Estimation of Damage Induced by Single-Hole Rock Blasting: A Review on Analytical, Numerical, and Experimental Solutions

et al. 2020

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“…To select the best predictive neuro-swarm model, five performance indices including variance account for (VAF), R 2 , mean absolute error (MAE), RMSE and the a20-index have been used and applied. These performance indices have been widely utilized to assess model performance in the previous related works [23,[71][72][73][74][75][76][77][78][79][80][81]. The computation formulas of VAF, MAE and a20-index are presented as follows:…”

Section: Resultsmentioning

confidence: 99%

On the Use of Neuro-Swarm System to Forecast the Pile Settlement

et al. 2020

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In civil engineering applications, piles (deep foundations) are pushed into the ground in order to perform as steady support of structures. As these type of foundations are able to carry a huge amount of load, they should be carefully designed in terms of their settlement. Therefore, the control and estimation of settlement is a significant issue in pilling design and construction. The objective of the present study is to introduce a modeling process of a hybrid intelligence system namely neural network optimized by particle swarm optimization (neuro-swarm) for estimation of pile settlement. To do that, properties results of several piles socketed into rock mass together with their settlements were considered as established databased to propose neuro-swarm model. Then, several sensitivity analyses were carried out to determine the most influential particle swarm optimization parameters for pile settlement prediction. Eventually, five neuro-swarm models were constructed to understand the behavior of this hybrid model on them in pile settlement prediction. As a result, according to results of five performance indices, dataset number 4 showed the highest prediction capacity among all five datasets. The coefficient of determination (R2) and system error values of (0.851 and 0.079) and (0.892 and 0.099) were obtained respectively for train and test stages of the best neuro-swarm model which reveal the capability level of this hybrid model in predicting pile settlement. The modeling process introduced in this study can be useful for the researchers who are interested to work on the same hybrid technique.

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“…e ANN model is an important branch of the machine learning (ML) technique and is inspired by the human brain [45,46]. With the help of computer calculation, many problems including blast-induced rock movement [47][48][49], blast-induced overpressure [50], rockburst [51], flyrock [52], and rock fragmentation [53,54] can be solved by learning message from the input variables and using these messages to predict the output variables. After reviewing previous studies [55,56], multilayer perception (MLP) which is composed of input layers, hidden layers, and output layers is the best type of neural network among many artificial neural networks.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Jiang¹,

Xu²,

Chen

et al. 2021

Advances in Civil Engineering

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The accurate determination of blast-induced ground vibration has an important significance in protecting human activities and the surrounding environment. For evaluating the peak particle velocity resulting from the quarry blast, a robust artificial intelligence system combined with the salp swarm algorithm (SSA) and Gaussian process (GP) was proposed, and the SSA was used to find the optimal hyperparameters of the GP here. In this regard, 88 datasets with 9 variables including the ratio of bench height to burden (H/B) and the ratio of spacing to burden (S/B) were selected as the input variables, while peak particle velocity (PPV) was measured. Then, an ANN model, an SVR model, a GP model, an SSA-GP model, and three empirical models were established, and the predictive performance was evaluated by using the root-mean-square error (RMSE), determination coefficient (R2), value account for (VAF), Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and the run time. After comparing, it is found that the proposed SSA-GP yielded a superior performance and the ratio of bench height to burden (H/B) was the most sensitive variable.

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Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm

Cited by 74 publications

References 43 publications

Estimation of Damage Induced by Single-Hole Rock Blasting: A Review on Analytical, Numerical, and Experimental Solutions

Estimation of Damage Induced by Single-Hole Rock Blasting: A Review on Analytical, Numerical, and Experimental Solutions

On the Use of Neuro-Swarm System to Forecast the Pile Settlement

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

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