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Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1413-1427页

作者： bhatawdekar ramesh murlidhar Hoang Nguyen Jamal Rostami XuanNam Bui Danial Jahed Armaghani Prashanth Ragam Edy Tonnizam Mohamad Centre of Tropical Geoengineering(Geotropik) School of Civil EngineeringFaculty of EngineeringUniversiti Teknologi MalaysiaSkudaiJohor81310Malaysia Department of Mining Engineering Indian Institute of TechnologyKharagpur721302India Department of Surface Mining Mining FacultyHanoi University of Mining and GeologyHanoi100000Viet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyHanoi100000Viet Nam Department of Mining Engineering Earth Mechanics InstituteColorado School of MinesGoldenCO80401USA Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinsk454080Russia Department of ECE Kakatiya Institute of Technology and ScienceWarangal506015India

In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive ***,use of explosives may lead to the flyrock *** can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working ***,prediction of flyrock is of high *** this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried *** hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field *** collected data include blasting parameters and rock mass ***-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass ***-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were *** performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.

关键词： Flyrock Harris hawks optimization(HHO) Multi-layer perceptron(MLP) Random forest(RF) Support vector machine(SVM) Whale optimization algorithm(WOA)

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Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2023-2027页

作者： Danial Jahed Armaghani Ahmed SalihMohammed ramesh murlidhar bhatawdekar Pouyan Fakharian Ashutosh Kainthola Wael Imad Mahmood School of Civil and Environmental Engineering University of Technology SydneyUltimoNSW 2007Australia College of Engineering University of SulaimaniKurdistan46001Iraq Engineering Department American University of IraqSulaimaniKurdistan46001Iraq Centre of Tropical Geoengineering(GEOTROPIK) Institute of Smart Infrastructure and Innovative Engineering(ISIIC)Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Civil Engineering Semnan UniversitySemnanIran Geo-Engineering and Computing Laboratory Banaras Hindu UniversityVaranasi221005India Scientific Research Centre Soran UniversitySoranErbilKurdistan-Region44008Iraq

Computational Intelligent(CI)systems represent a pivotal intersection of cutting-edge technologies and complex engineering challenges aimed at solving real-world *** comprehensive body of work delves into the realm of CI,which is designed to tackle intricate and multifaceted engineering problems through advanced computational *** history of CI systems is a fascinating journey that spans several decades and has its roots in the development of artificial intelligence and machine learning *** a wide array of practical examples and case studies,this special issue bridges the gap between theoretical concepts and practical implementation,shedding light on how CI systems can optimize processes,design solutions,and inform decisions in complex engineering *** compilation stands as an essential resource for both novice learners and seasoned practitioners,offering a holistic perspective on the potential of CI in reshaping the future of engineering problem-solving.

关键词： offering bridges landscape

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