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Knowledge mapping of research progress in blast-induced ground vibration from 1990 to 2022 using CiteSpace-based scientometric analysis

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Abstract

Blasting constitutes an essential component of the mining and construction industries. However, the associated nuisances, particularly blast vibration, have emerged as significant concerns that pose threats to operational stability and the safety of the surrounding areas. Given the increasing emphasis on sustainability, ecological responsibility, safety, and geo-environmental practices, the impact of blast vibration has garnered heightened attention and scrutiny. Nevertheless, the field still lacks comprehensive phase analysis studies. Therefore, it is imperative to elucidate the research progress on blast vibration and discern its current frontiers of investigation. To address this need, this study employs bibliometric methods and the CiteSpace 6.1.R2 software to analyze 3093 papers from the Web of Science database. Through this comprehensive analysis, the study aims to chronicle the developmental trajectory, assess the present research status, and identify future trends in the field of blast vibration. The findings of this study reveal that research on “blasting vibration” is advancing rapidly, with the number of citations exhibiting a J-shaped growth curve over time. China emerges as the leading contributor to this research, followed by India, and the foremost institution in this field is Central South University in China. Cluster analysis identifies the effects of ground vibration, numerical simulation, blast load, blasting vibration and rockburst hazard as the most prominent research areas presently. The primary research directions in this domain revolve around the rock fragmentation, compressive strength, particle swarm optimization, and ann. The emergence of these keywords underscores a dynamic shift towards a more holistic and multidisciplinary approach in the field of blasting-induced ground vibration. Furthermore, this study provides a concise overview of blast vibration, discusses prediction techniques, and proposes measures for its control. Additionally, the discussion delves into the social significance of intelligent blasting systems within the context of artificial intelligence, aiming to address the hazards associated with blast-induced ground vibrations.

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References

  • Adhikari GR, Theresraj AI, Venkatesh HS, Balachander R, Gupta RN (2004) Ground vibration due to blasting in limestone quarries. Fragblast 8(2):85–94

    Google Scholar 

  • Ainalis D, Kaufmann O, Tshibangu JP, Verlinden O, Kouroussis G (2017) Modelling the source of blasting for the numerical simulation of blast-induced ground vibrations: a review. Rock Mech Rock Eng 50:171–193

    Google Scholar 

  • Álvarez-Vigil AE, González-Nicieza C, Gayarre FL, Álvarez-Fernández MI (2012) Predicting blasting propagation velocity and vibration frequency using artificial neural networks. Int J Rock Mech Min Sci 55:108–116

    Google Scholar 

  • Amiri M, Bakhshandeh Amnieh H, Hasanipanah M, Mohammad Khanli L (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput 32(4):631–644

    Google Scholar 

  • Armaghani DJ, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7:5383–5396

    Google Scholar 

  • Armaghani DJ, Hasanipanah M, Amnieh HB, Mohamad ET (2018) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Applic 29:457–465

    Google Scholar 

  • Blair DP, Armstrong LW (1999) The spectral control of ground vibration using electronic delay detonators. Fragblast 3(4):303–334

    Google Scholar 

  • Chang YW, Huang MH, Lin CW (2015) Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses. Scientometrics 105:2071–2087

    Google Scholar 

  • Chen C (2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Tech 57(3):359–377

    Google Scholar 

  • Chen C (2017) Science mapping: a systematic review of the literature. J Data Inf Sci 2(2):1–40

    CAS  Google Scholar 

  • Chen C (2018) Visualizing and exploring scientific literature with Citespace: an introduction. In: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, pp 369–370

    Google Scholar 

  • Chen C, Ibekwe-SanJuan F, Hou J (2010) The structure and dynamics of cocitation clusters: a multiple-perspective cocitation analysis. J Am Soc Inf Sci Tech 61(7):1386–1409

    Google Scholar 

  • Comina C, Foti S (2007) Surface wave tests for vibration mitigation studies. J Geotech Geoenviron Eng 133(10):1320–1324

    Google Scholar 

  • Ding Z, Nguyen H, Bui XN, Zhou J, Moayedi H (2020) Computational intelligence model for estimating intensity of blast-induced ground vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms. Nat Resour Res 29(2):751–769

    Google Scholar 

  • Dumakor-Dupey NK, Arya S, Jha A (2021) Advances in blast-induced impact prediction—a review of machine learning applications. Minerals 11(6):601

    Google Scholar 

  • Duvall WI, Fogelson DE (1962) Review of criteria for estimating damage to residences from blasting vibrations, US Dept. of the Interior, Bureau of Mines. Report of investigations 5968

  • Fakhimi A, Lanari M (2014) DEM–SPH simulation of rock blasting. Comput Geotech 55:158–164

    Google Scholar 

  • Faradonbeh RS, Armaghani DJ, Amnieh HB, Mohamad ET (2018) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput Applic 29:269–281

    Google Scholar 

  • Freeman LC (2002) Centrality in social networks: conceptual clarification. Social network: critical concepts in sociology. Londres 1:238–263

    Google Scholar 

  • Gao J, Wu X, Luo X, Guan S (2021) Scientometric analysis of safety sign research: 1990–2019. Int J Environ Res Public Health 18(1):273

    Google Scholar 

  • Ghasemi E, Ataei M, Hashemolhosseini H (2013) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19(5):755–770

    Google Scholar 

  • Ghosh A, Daemen JK (1983) A simple new blast vibration predictor of ground vibrations induced predictor. In: The 24th U.S. Symposium on Rock Mechanics (USRMS), College Station, Texas, pp 83-0151

  • Gou Y, Shi X, Zhou J, Qiu X, Chen X, Huo X (2020) Attenuation assessment of blast-induced vibrations derived from an underground mine. Int J Rock Mech Min Sci 127:104220

    Google Scholar 

  • Gou Y, Shi X, Huo X, Zhou J, Yu Z, Qiu X (2019) Motion parameter estimation and measured data correction derived from blast-induced vibration: new insights. Measurement 135:213–230

    Google Scholar 

  • Guo H, Zhou J, Koopialipoor M, Jahed Armaghani D, Tahir MM (2021) Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Eng Comput 37:173–186

    Google Scholar 

  • Gupta RN, Roy PP, Sing B (1988) On a blast induced blast vibration predictor for efficient blasting. In: Proceedings of the 22nd international conference of safety in mines, Beijing China, pp 1015–1021

  • Hajihassani M, Armaghani DJ, Marto A, Mohamad ET (2015) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74(3):873–886

    Google Scholar 

  • Hajihassani M, Armaghani DJ, Monjezi M, Mohamad ET, Marto A (2015a) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74(4):2799–2817

    Google Scholar 

  • Hasanipanah M, Amnieh HB, Arab H, Zamzam MS (2018) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Applic 30:1015–1024

    Google Scholar 

  • Hasanipanah M, Faradonbeh RS, Amnieh HB, Armaghani DJ, Monjezi M (2017) Forecasting blast-induced ground vibration developing a CART model. Eng Comput 33:307–316

    Google Scholar 

  • Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32:705–715

    Google Scholar 

  • Hu Y, Lu W, Chen M, Yan P, Zhang Y (2015) Numerical simulation of the complete rock blasting response by SPH–DAM–FEM approach. Simul Model Pract Theory 56:55–68

    Google Scholar 

  • Huang D, Cui S, Li X (2019) Wavelet packet analysis of blasting vibration signal of mountain tunnel. Soil Dyn Earthq Eng 117:72–80

    Google Scholar 

  • Huo X, Shi X, Qiu X, Zhou J, Gou Y, Yu Z, Zhang S (2022) A study on raise blasting and blast-induced vibrations in highly stressed rock masses. Tunn Undergr Space Technol 123:104407

    Google Scholar 

  • Jahed Armaghani D, Hasanipanah M, Tonnizam Mohamad E (2016) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Engineering with Computers 32:155–171

    Google Scholar 

  • Jiang N, Gao T, Zhou C, Luo X (2018) Effect of excavation blasting vibration on adjacent buried gas pipeline in a metro tunnel. Tunn Undergr Space Technol 81:590–601

    Google Scholar 

  • Jiang Z, Xu H, Chen H, Gao B, Jia S, Yu Z, Zhou J (2021) Indirect determination approach of blast-induced ground vibration based on a hybrid SSA-optimized GP-based technique. Adv Civ Eng 2021:1–14

    CAS  Google Scholar 

  • Khandelwal M, Armaghani DJ, Faradonbeh RS, Yellishetty M, Majid MZA, Monjezi M (2017) Classification and regression tree technique in estimating peak particle velocity caused by blasting. Eng Comput 33:45–53

    Google Scholar 

  • Koçaslan A, Yüksek AG, Görgülü K, Arpaz E (2017) Evaluation of blast-induced ground vibrations in open-pit mines by using adaptive neuro-fuzzy inference systems. Environ Earth Sci 76:1–11

    Google Scholar 

  • Langefors, U., & Kihlström, B. (1963). The modern technique of rock blasting.

    Google Scholar 

  • Lawal AI, Kwon S, Hammed OS, Idris MA (2021) Blast-induced ground vibration prediction in granite quarries: an application of gene expression programming, ANFIS, and sine cosine algorithm optimized ANN. Int J Min Sci Technol 31(2):265–277

    Google Scholar 

  • Li C, Zhou J, Khandelwal M, Zhang X, Monjezi M, Qiu Y (2022) Six novel hybrid extreme learning machine–swarm intelligence optimization (ELM–SIO) models for predicting backbreak in open-pit blasting. Nat Resour Res 31(5):3017–3039

    Google Scholar 

  • Ma C, Wu L, Sun M, Lei D (2021) Failure mechanism and stability analysis of bank slope deformation under the synergistic effect of heavy rainfall and blasting vibration. Geotech. Geol. Eng 39(8):5811–5824

    Google Scholar 

  • Minchinton A (2015) On the influence of fundamental detonics on blasting practice. In: Paper presented at the 11th international symposium on rock fragmentation by blasting, Sydney, pp 41–53

  • Nguyen H, Bui XN, Bui HB, Mai NL (2020) A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine. Vietnam Neural Comput Appl 32:3939–3955

    Google Scholar 

  • Nguyen H, Bui XN, Tran QH, Mai NL (2019) A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Appl Soft Comput 77:376–386

    Google Scholar 

  • Nguyen H, Drebenstedt C, Bui XN, Bui DT (2020a) Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Nat Resour Res 29:691–709

    Google Scholar 

  • Prakash AJ, Palroy P, Misra DD (2004) Analysis of blast vibration characteristics across a trench and a pre-split plane. Fragblast 8(1):51–60

    Google Scholar 

  • Qiu Y, Zhou J, Khandelwal M, Yang H, Yang P, Li C (2021) Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration. Eng Comput 38:4145–4162

    Google Scholar 

  • Saadat M, Khandelwal M, Monjezi M (2014) An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine. Iran J Rock Mech Geotech Eng 6(1):67–76

    Google Scholar 

  • Shirani Faradonbeh R, Jahed Armaghani D, Abd Majid MZ, Tahir MM, Ramesh Murlidhar B, Monjezi M, Wong HM (2016) Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction. Int J Environ Sci Technol 13:1453–1464

    Google Scholar 

  • Taheri K, Hasanipanah M, Golzar SB, Majid MZA (2017) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33:689–700

    Google Scholar 

  • Uttarwar M, Gurharikar M, Gujjula P (2020) Effect of blast induced ground vibrations on green concrete at Mumbai Metro Rail Project. Helix-The Scientific Explorer| Peer Reviewed Bimonthly. Int Dent J 10(01):51–55

    Google Scholar 

  • Valdivia C, Vega M, Scherpenisse CR, Adamson WR (2003) Vibration simulation method to control stability in the Northeast corner of Escondida Mine. Fragblast 7(2):63–78

    Google Scholar 

  • Verma AK, Singh TN (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27:225–233

    Google Scholar 

  • Wei H, Chen J, Zhu J, Yang X, Chu H (2022) A novel algorithm of Nested-ELM for predicting blasting vibration. Eng Comput 38:1241–1256

    Google Scholar 

  • Yan Y, Hou X, Fei H (2020) Review of predicting the blast-induced ground vibrations to reduce impacts on ambient urban communities. J Clean Prod 260:121135

    Google Scholar 

  • Yu Z, Shi X, Zhou J, Gou Y, Huo X, Zhang J, Armaghani DJ (2020) A new multikernel relevance vector machine based on the HPSOGWO algorithm for predicting and controlling blast-induced ground vibration. Eng Comput 38:1905–1920

    Google Scholar 

  • Yu Z, Shi X, Zhou J, Chen X, Qiu X (2020a) Effective assessment of blast-induced ground vibration using an optimized random forest model based on a Harris hawks optimization algorithm. Appl Sci 10(4):1403

    Google Scholar 

  • Zhang C, Gholipour G, Mousavi AA (2019) Nonlinear dynamic behavior of simply-supported RC beams subjected to combined impact-blast loading. Eng Struct 181:124–142

    Google Scholar 

  • Zhang X, Nguyen H, Choi Y, Bui XN, Zhou J (2021) Novel extreme learning machine-multi-verse optimization model for predicting peak particle velocity induced by mine blasting. Nat Resour Res 30:4735–4751

    Google Scholar 

  • Zhang X, Nguyen H, Bui XN, Tran QH, Nguyen DA, Bui DT, Moayedi H (2020) Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Nat Resour Res 29:711–721

    Google Scholar 

  • Zhang ZX, Lindqvist PA, Naarttijärvi T, Wikström K (2004) A feasibility study on controlling ground vibrations caused by blasts in Malmberget underground mine. Fragblast 8(1):3–21

    Google Scholar 

  • Zhang H, Zhou J, Jahed Armaghani D, Tahir MM, Pham BT, Huynh VV (2020a) A combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration. Appl Sci 10(3):869

    Google Scholar 

  • Zhou J, Asteris PG, Armaghani DJ, Pham BT (2020a) Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dyn Earthq Eng 139:106390

    Google Scholar 

  • Zhou J, Nekouie A, Arslan CA, Pham BT, Hasanipanah M (2020b) Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm. Eng Comput 36:703–712

    Google Scholar 

  • Zhou J, Dai Y, Khandelwal M, Monjezi M, Yu Z, Qiu Y (2021a) Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations. Nat Resour Res 30:4753–4771

    Google Scholar 

  • Zhou J, Li C, Koopialipoor M, Jahed Armaghani D, Thai Pham B (2021b) Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC). Int J Min Reclam Environ 35(1):48–68

    CAS  Google Scholar 

  • Zhou J, Qiu Y, Khandelwal M, Zhu S, Zhang X (2021c) Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. Int J Rock Mech Min Sci 145:104856

    Google Scholar 

  • Zhou J, Shi X, Li X (2016) Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. J Vib Control 22(19):3986–3997

    Google Scholar 

  • Zhou J, Zhang Y, Li C, He H, Li X (2023) Rockburst prediction and prevention in underground space excavation. Undergr Space. https://doi.org/10.1016/j.undsp.2023.05.009

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Funding

This research was funded by the National Science Foundation of China (42177164) and the Distinguished Youth Science Foundation of Hunan Province of China (2022JJ10073).

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Contributions

Yulin Zhang: methodology, software, formal analysis, visualization investigation, data curation, writing—original draft.

Haini He: visualization, validation, formal analysis.

Manoj Khandelwal: resources, writing—review and editing, validation.

Kun Du: software, investigation.

Jian Zhou: conceptualization, writing—review and editing, supervision, project administration, funding acquisition. All authors read and approved the final manuscript for publication.

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Correspondence to Jian Zhou.

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Highlights

• Blast-induced ground vibrations studies were systematically and quantitatively analyzed by bibliometric methods with 2080 related papers published from 1990 to 2022.

• The influential authors and their relationships in this area were analyzed, and current hot topics and potential development trends are presented and discussed.

• Understanding, prediction, and mitigation measures of blast vibration are reviewed and discussed.

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Zhang, Y., He, H., Khandelwal, M. et al. Knowledge mapping of research progress in blast-induced ground vibration from 1990 to 2022 using CiteSpace-based scientometric analysis. Environ Sci Pollut Res 30, 103534–103555 (2023). https://doi.org/10.1007/s11356-023-29712-1

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