A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility

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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“…Application of ICA algorithm in automated clustering of remote sensing images was presented by Karami and Shokouhi [53]. In addition, this algorithm was applied to determine the optimum weights and biases used in ANNs [22,23,54]. To predict soil compaction indices, Taghavifar et al [22] employed conventional ANN and ICA-ANN techniques.…”

Section: Imperialist Competitive Algorithmmentioning

confidence: 99%

“…In addition, this algorithm was applied to determine the optimum weights and biases used in ANNs [22,23,54]. To predict soil compaction indices, Taghavifar et al [22] employed conventional ANN and ICA-ANN techniques. They successfully indicated that the network optimized by ICA can perform better compared to conventional ANN technique.…”

Section: Imperialist Competitive Algorithmmentioning

confidence: 99%

“…These algorithms can minimize the error values of the system as stated by many researchers [22,[77][78][79][80]. In this objective, ICA was applied for determining the optimum weights and biases of ANN model to minimize error values of the system.…”

Section: Ica-ann Modelingmentioning

confidence: 99%

“…However, ANNs have a number of disadvantages: slow learning rate and getting trapped in local minima [20,21]. The ANN performance can be considerably improved by the use of optimization algorithms such as imperialist competitive algorithm (ICA) as reported by many scholars [5,22,23]. This study is aimed to investigate the efficiency of the empirical and intelligent systems, namely ANN and ICA-ANN in predicting AOp resulting from the blasting operation.…”

Section: Introductionmentioning

confidence: 99%

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A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

Armaghani

Hasanipanah

Mohamad

2015

Engineering with Computers

126

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including coefficient of determination (R 2 ), root mean square error and value account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models were selected among all constructed models. It was found that the ICA-ANN approach can provide higher performance capacity in predicting AOp compared to other predictive methods.

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Hybrid smart model to determine concentration of acidic gases in absorption tower of sweetening process

Nejad

Amin

Mohsenipour³

et al. 2022

Can J Chem Eng

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The natural gases produced from underground reserves may contain sour gases such as H 2 S and CO 2 . If the amount of these gases is greater than the standard level, it will be harmful to humans and the environment. Hence, it appears important to accurately determine the concentration of H 2 S and CO 2 in various equipment/units of a natural gas sweetening plant. In this study, a new connectionist approach is introduced to obtain the concentration of outlet acid gases of the sweetening tower of the South Pars gas refinery (in Iran), by utilizing a conventional connectionist tool optimized by an imperialist competitive algorithm (ICA). This ICA strategy is applied to optimize the weights, biases, and number of neurons of the artificial neural network (ANN) model.The input parameters in this modelling strategy include time, the amount of H 2 S in the output amine, the flow rate of input sour gas, the temperature of sea water for cooling, the mass flow rate of low-pressure steam to the amineamine heat exchanger, and the flow rate of input amine. The performance of the hybrid deterministic tool, ANN-ICA, is compared with the ANN-back propagation (BP) method. The coefficient of determination and mean squared error to forecast the concentration of output H 2 S are 0.8931 and 0.0125 for the ANN-BP algorithm and 0.9057 and 0.0104 for the ANN-ICA algorithm, respectively. These statistical values are 0.8428 and 0.0001 for the ANN-BP model and 0.9307 and 0.000 05 for the ANN-ICA model, respectively, while predicting output CO 2 concentration. The results confirm that the ANN-ICA is a more reliable approach, compared to ANN-BP, to forecast the concentration of acidic gases leaving the absorption tower.

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A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility

Cited by 89 publications

References 13 publications

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

Hybrid smart model to determine concentration of acidic gases in absorption tower of sweetening process

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