Optimal ELM–Harris Hawks Optimization and ELM–Grasshopper Optimization Models to Forecast Peak Particle Velocity Resulting from Mine Blasting View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-02-05

AUTHORS

Canxin Yu, Mohammadreza Koopialipoor, Bhatawdekar Ramesh Murlidhar, Ahmed Salih Mohammed, Danial Jahed Armaghani, Edy Tonnizam Mohamad, Zengli Wang

ABSTRACT

Most mining and tunneling projects usually require blasting operations to remove rock mass. Previous studies have mentioned that if the blasting operation is not properly designed, it may lead to several environmental issues, such as ground vibration. This study presents various machine learning (ML) techniques, i.e., hybrid extreme learning machines (ELMs) with the grasshopper optimization algorithm (GOA) and Harris hawks optimization (HHO) for controlling and predicting ground vibrations resulting from mine blasting. Actually, the GOA–ELM and HHO–ELM models are improved versions of a previously developed ELM model, and they are able to provide higher performance capacity. For the proposed ML modeling, a database was established consisting of 166 datasets collected from Malaysian quarries. The efficacy of the proposed ML techniques was observed in the training stage as well as in the testing stage, and the results were evaluated against five parameters constituting the fitness criteria. The results showed that the GOA–ELM model delivered more accurate ground vibration values compared to the HHO–ELM model. The system error values of the GOA–ELM model for the training and testing datasets were 2.0239 and 2.8551, respectively. The coefficients of determination of the GOA-ELM model for the training and testing datasets were 0.9410 and 0.9105, respectively. It was concluded that the new hybrid model is able to forecast ground vibration resulting from mine blasting with high level of accuracy. The capabilities of this hybrid model can be extended further to mitigate other environmental issues caused by mine blasting. More... »

PAGES

2647-2662

References to SciGraph publications

  • 2009-11-13. Application of soft computing to predict blast-induced ground vibration in ENGINEERING WITH COMPUTERS
  • 2019-07-03. Deep neural network and whale optimization algorithm to assess flyrock induced by blasting in ENGINEERING WITH COMPUTERS
  • 2013-11-16. Extreme Learning Machine Based Modeling of Resilient Modulus of Subgrade Soils in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2019-10-04. An analytical solution for evaluating the safety of an exposed face in a paste backfill stope incorporating the arching phenomenon in INTERNATIONAL JOURNAL OF MINERALS, METALLURGY AND MATERIALS
  • 2018-07-28. Predicting tunnel boring machine performance through a new model based on the group method of data handling in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2019-02-27. Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN in ENVIRONMENTAL EARTH SCIENCES
  • 2019-08-24. Effect of Water Content on Argillization of Mudstone During the Tunnelling process in ROCK MECHANICS AND ROCK ENGINEERING
  • 2019-06-08. A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network in NATURAL RESOURCES RESEARCH
  • 2020-09-05. Mechanics and safety issues in tailing-based backfill: A review in INTERNATIONAL JOURNAL OF MINERALS, METALLURGY AND MATERIALS
  • 2019-03-01. Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network in NATURAL RESOURCES RESEARCH
  • 2018-01-17. Training an extreme learning machine by localized generalization error model in SOFT COMPUTING
  • 2013-11-27. Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization in ARABIAN JOURNAL OF GEOSCIENCES
  • 2019-01-08. Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance in ENGINEERING WITH COMPUTERS
  • 2012-07-18. Self-Adaptive Evolutionary Extreme Learning Machine in NEURAL PROCESSING LETTERS
  • 2019-01-09. The use of new intelligent techniques in designing retaining walls in ENGINEERING WITH COMPUTERS
  • 2004-05-04. Human response to blast-induced vibration and air-overpressure: an Indian scenario in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2020-05-16. Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2016-02-29. A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure in ENGINEERING WITH COMPUTERS
  • 2017-11-16. Forecasting ground vibration due to rock blasting: a hybrid intelligent approach using support vector regression and fuzzy C-means clustering in ENGINEERING WITH COMPUTERS
  • 2010-07-07. Intelligent systems for ground vibration measurement: a comparative study in ENGINEERING WITH COMPUTERS
  • 2020-02-06. Big data management in the mining industry in INTERNATIONAL JOURNAL OF MINERALS, METALLURGY AND MATERIALS
  • 2015-03-25. Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting in ENVIRONMENTAL EARTH SCIENCES
  • 2019-10-30. Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples in ENGINEERING WITH COMPUTERS
  • 2017-08-03. A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model in ENVIRONMENTAL EARTH SCIENCES
  • 2020-08-10. A Novel Combination of Tree-Based Modeling and Monte Carlo Simulation for Assessing Risk Levels of Flyrock Induced by Mine Blasting in NATURAL RESOURCES RESEARCH
  • 2020-05-14. A SVR-GWO technique to minimize flyrock distance resulting from blasting in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2018-03-03. Three hybrid intelligent models in estimating flyrock distance resulting from blasting in ENGINEERING WITH COMPUTERS
  • 2019-07-15. Use of Intelligent Methods to Design Effective Pattern Parameters of Mine Blasting to Minimize Flyrock Distance in NATURAL RESOURCES RESEARCH
  • 2018-10-24. Overbreak prediction and optimization in tunnel using neural network and bee colony techniques in ENGINEERING WITH COMPUTERS
  • 2018-05-28. Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions in SOFT COMPUTING
  • 2020-01-10. Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm in ENGINEERING WITH COMPUTERS
  • 2020-08-15. Safety of barricades in cemented paste-backfilled stopes in INTERNATIONAL JOURNAL OF MINERALS, METALLURGY AND MATERIALS
  • 2012-02-18. Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network in NEURAL COMPUTING AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11053-021-09826-4

    DOI

    http://dx.doi.org/10.1007/s11053-021-09826-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1135163130


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