The potential application of particle swarm optimization algorithm for forecasting the air-overpressure induced by mine blasting

AminShokravi, Amir; Eskandar, Hajar; Derakhsh, Ali Mahmodi; Rad, Hima Nikafshan; Ghanadi, Ali

doi:10.1007/s00366-017-0539-5

Cited by 36 publications

(6 citation statements)

References 25 publications

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“…The model used nine input parameters. The PSO-linear model was shown to have a stronger predictive ability than the PSO-power, PSO-quadratic, ANN, and USBM models by AminShokravi et al, who examined the acceptability and reliability of three PSO-based air blast models (the PSO-linear, PSO power, and PSO-quadratic models) [26].Table 2 present some of the literature review of past application of machine learning approach to airblast prediction. The review shows that several Machine learning techniques have been use lately for addressing this blast related challenge but not much work had been done towards the use to long short term memory and Chao Game Optimizer (CGO) algorithm.…”

Section: Review Of Empirical Models and Machine Learning Application ...mentioning

confidence: 99%

Mine Induced Airblast prediction: An Application of Chaos Game Optimization based soft computing approaches

Hosseini,

Taiwo,

Fissha

et al. 2023

Preprint

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Air overpressure, often known as AOp, is one of the unfavourable effects of galena blasting. It has high damage potential to structural buildings, ecosystem, and occasionally endanger mine workers due to the ore characteristics. The efficiency of most available techniques to manage this mine challenge is site specific and sometimes limited by poor prediction accuracy. In the current study, several deep and machine learning approaches have used to develop blast-induced AOP prediction models as a way forward to the recent gap. These techniques include, Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), Random Forest (RF), and Multivariate Adaptive Regression Splines (MARS). To achieve the research aim, 90 measured field data sets were monitored and collected from the Anguran open-pit lead-and-zinc mine (Iran). To improve the proposed model performance, chaos game optimisation (CGO) as a nature-inspired algorithm was adopted. The optimized models were compared with each other and as well with the ordinary models to determine the model with the best prediction accuracy. The results show that, in terms of accuracy levels, the performance of hybrid algorithm approach is superior to that of single based models. The LSTM-CGO model, out of the 10 proposed models, has the highest prediction accuracy statistically. This study demonstrated how well deep learning techniques provide solution to safe and environmental friendly mining operation. The proposed soft computing models are applicable as a tool to forecast AOP in surface mine blasting operation as a pre-blast design decision making reference.

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Section: Review Of Empirical Models and Machine Learning Application ...mentioning

confidence: 99%

Mine Induced Airblast prediction: An Application of Chaos Game Optimization based soft computing approaches

Hosseini,

Taiwo,

Fissha

et al. 2023

Preprint

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“…Using nine input parameters, the proposed model had a correlation coefficient of 0.94, suggesting a superior predictive strength compared to empirical models. AminShokravi et al [134] evaluated the acceptability and reliability of three PSO-based airblast models (the PSO-linear, PSOpower, and PSO-quadratic models) and found that the PSO-linear model showed a higher predictive ability than the PSO-power, PSO-quadratic, ANN, and USBM models.…”

Section: Airblastmentioning

confidence: 99%

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

2021

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Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

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“…The PSO algorithm which is a meta heuristic algorithm based on the social behaviour of birds striving to achieve a goal was developed by Kennedy and Eberhart [11,35]. PSO algorithm has been used in different fields such as industry engineering [36], civil engineering [37], energy systems engineering [38], electrical engineering [39] and geology engineering [40] because of its successful performance. Qu and Lou [41] used the PSO algorithm for the optimal allocation of regional water resources.…”

Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Gulcu¹,

Kahramanlı²

2019

ijisae

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Nowadays, the systems that are inspired by biological structures have gained importance and attracted the attention of researchers. The Multiple Travelling Salesman Problem (MTSP) is an extended version of the TSP. The aim in the MTSP is to find the tours for m salesmen, who all start and end at the depot, such that each intermediate node is visited exactly once and the total cost of visiting nodes is minimized. The Particle Swarm Optimization (PSO) algorithm which is a meta-heuristic algorithm based on the social behaviour of birds. In this article, 2 algorithms based on PSO, called APSO and HAPSO, were proposed to solve the MTSP. The APSO algorithm is based on the PSO and 2-opt algorithms, the path-relink and swap operators. While the HAPSO algorithm is based on the GRASP, PSO and 2-opt algorithms, the path-relink and swap operators. In the experiments, 5 TSP instances are used and the algorithms are compared with the GA and ACO algorithms. According to the results, the HAPSO algorithm has the better performance than the other algorithms on the most instances. Moreover the HAPSO algorithm produces more stable results than the APSO algorithm and the performance of the HAPSO algorithm is better in all the MTSP instances. Therefore, the HAPSO algorithm is more robust than the APSO algorithm.

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The potential application of particle swarm optimization algorithm for forecasting the air-overpressure induced by mine blasting

Cited by 36 publications

References 25 publications

Mine Induced Airblast prediction: An Application of Chaos Game Optimization based soft computing approaches

Mine Induced Airblast prediction: An Application of Chaos Game Optimization based soft computing approaches

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

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