A novel social network search and LightGBM framework for accurate prediction of blast-induced peak particle velocity

Abstract

The accurate prediction of peak particle velocity (PPV) is essential for effectively managing blast-induced vibrations in mining operations. This study presents a novel PPV prediction method based on the social network search and LightGBM (SNS-LightGBM) deep gradient cooperative learning framework. The SNS algorithm enhances LightGBM’s learning process by optimizing hyperparameters through global search capabilities and balancing model complexity to improve generalization. To assess its performance, five baseline machine learning models and a hybrid model combining SNS-LightGBM were developed for comparison. The predictive performance of these models was evaluated using metrics such as coefficient of determination (R²), mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE). The results indicate that the SNS-LightGBM model substantially improves both the accuracy and stability of PPV predictions. The SNS-LightGBM model outperformed all other models, achieving an R² of 0.975, MAE of 0.086, MAPE of 0.071, MSE of 0.019, and RMSE of 0.138. Additionally, a feature importance analysis revealed that distance and charge weight are the most significant factors influencing PPV, far surpassing other parameters. These findings offer valuable insights for improving the precision of blast vibration prediction and optimizing blasting designs.

Graphical abstract

Keywords

peak particle velocity / social network search / LightGBM / feature importance analysis / predicting performance

References

[1] Garai D, Agrawal H, Mishra A K. Impact of orientation of blast initiation on ground vibrations. Journal of Rock Mechanics and Geotechnical Engineering, 2023, 15(1): 255–261

[2] Hajihassani M, Armaghani D J, Marto A, Mohamad E T. Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bulletin of Engineering Geology and the Environment, 2015, 74(3): 873–886

[3] Vishwakarma A K, Himanshu V K, Dey K. Prediction of ground vibration at surface for ring blasting in sublevel stoping through empirical approach, K-nearest neighbor, and random forest model. Mining, Metallurgy & Exploration, 2024, 41(3): 1567–1584

[4] JimenoC LJimeno E LCarcedoF J A. Drilling and Blasting of Rocks. Boca Raton, FA: USA CRS Press, 1995

[5] LangeforsUKihlstrom B. The Modern Technique of Rock Blasting. Hoboken, NJ: Wiley or Almqvist & Wiksell, 1967

[6] Xue K, Yue Z, Ren M, Ma H, Jin Q, Ma W, Zhou X, Long S. Analysis of blasting vibration responses in the ramp section of the Beishan underground research laboratory. Tunnelling and Underground Space Technology, 2024, 153: 105999

[7] Gui Y L, Zhao Z Y, Jayasinghe L B, Zhou H Y, Goh A T C, Tao M. Blast wave induced spatial variation of ground vibration considering field geological conditions. International Journal of Rock Mechanics and Mining Sciences, 2018, 101: 63–68

[8] Yin Z, Hu Z, Wei Z, Zhao G, Ma H, Zhuo Z. Assessment of blasting-induced ground vibration in an open-pit mine under different rock properties. Advances in Civil Engineering, 2018, 2018(1): 4603687

[9] Khandelwal M, Singh T N. Prediction of blast-induced ground vibration using artificial neural network. International Journal of Rock Mechanics and Mining Sciences, 2009, 46(7): 1214–1222

[10] XuYLinZ MaTSheC XingSQi LFarkoushSPanJ. Optimization of a biomass-driven rankine cycle integrated with multi-effect desalination, and solid oxide electrolyzer for power, hydrogen, and freshwater production. Desalination, 2022, 525: 115486

[11] Xu B, Tan Y, Sun W, Ma T, Liu H, Wang D. Study on the prediction of the uniaxial compressive strength of rock based on the SSA-XGBoost model. Sustainability, 2023, 15(6): 5201

[12] Liu H, Ma T, Lin Y, Peng K, Hu X, Xie S, Luo K. Deep learning in rockburst intensity level prediction: performance evaluation and comparison of the NGO-CNN-BiGRU-attention model. Applied Sciences, 2024, 14(13): 5719

[13] Xie S, Jiang Z, Lin H, Ma T, Peng K, Liu H, Liu B. A new integrated intelligent computing paradigm for predicting joints shear strength. Geoscience Frontiers, 2024, 15(6): 101884

[14] Ma T, Hu X, Liu H, Peng K, Lin Y, Chen Y, Luo K, Xie S, Han C, Chen M. Elastic modulus prediction for high-temperature treated rock using multi-step hybrid ensemble model combined with coronavirus herd immunity optimizer. Measurement, 2025, 240: 115596

[15] Cheng Y, He D, Ma T, Lin H, Hu X, Liu H. Hybrid data-driven model and shapley additive explanations for peak dilation angle of rock discontinuities. Materials Today. Communications, 2024, 40: 110194

[16] Wang T, Luo R, Ma T, Chen H, Zhang K, Wang X, Chu Z, Sun H. Study and verification on an improved comprehensive prediction model of landslide displacement. Bulletin of Engineering Geology and the Environment, 2024, 83(3): 90

[17] Wang T, Zhang K, Liu Z, Ma T, Luo R, Chen H, Wang X, Ge W, Sun H. Prediction and explanation of debris flow velocity based on multi-strategy fusion Stacking ensemble learning model. Journal of Hydrology, 2024, 638: 131347

[18] XieSLingH MaTPengK SunZ. Prediction of joint roughness coefficient via hybrid machine learning model combined with principal components analysis. Journal of Rock Mechanics and Geotechnical Engineering, 2024, 059

[19] Lin Y, Guo Z, Meng Q, Li C, Ma T. Prediction of peak strength under triaxial compression for sandstone based on ABC-SVM algorithm. Expert Systems with Applications, 2025, 125923: 125923

[20] Choi Y H, Lee S S. Predictive modelling for blasting-induced vibrations from open-pit excavations. Applied Sciences, 2021, 11(16): 7487

[21] Gorai A K, Himanshu V K, Santi C. Development of ANN-based universal predictor for prediction of blast-induced vibration indicators and its performance comparison with existing empirical models. Mining, Metallurgy & Exploration, 2021, 38(5): 2021–2036

[22] Kan J, Dou L, Li X, Cao J, Bai J, Chai Y. Study on influencing factors and prediction of peak particle velocity induced by roof pre-split blasting in underground. Underground Space, 2022, 7(6): 1068–1085

[23] Mohamadnejad M, Gholami R, Ataei M. Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations. Tunnelling and Underground Space Technology, 2012, 28: 238–244

[24] Huang Y, Zhou Z, Li M, Luo X. Prediction of ground vibration induced by rock blasting based on optimized support vector regression models. Computer Modeling in Engineering & Sciences, 2024, 139(3): 3147–3165

[25] He B, Lai S H, Mohammed A S, Sabri M M S, Ulrikh D V. Estimation of blast-induced peak particle velocity through the improved weighted random forest technique. Applied Sciences, 2022, 12(10): 5019

[26] Dzimunya N, Besa B, Nyirenda R. Prediction of ground vibrations induced by bench blasting using the random forest algorithm. Journal of the Southern African Institute of Mining and Metallurgy, 2023, 123(3): 123–132

[27] Iramina W S, Sansone E C, Wichers M, Wahyudi S, de Eston S M, Shimada H, Sasaoka T. Comparing blast-induced ground vibration models using ANN and empirical geomechanical relationships. REM: International Engineering Journal, 2018, 71(1): 89–95

[28] Khandelwal M. Evaluation and prediction of blast-induced ground vibration using support vector machine. International Journal of Rock Mechanics and Mining Sciences, 2010, 47(3): 509–516

[29] Yu Z, Shi X, Zhou J, Chen X, Qiu X. Effective assessment of blast-induced ground vibration using an optimized random forest model based on a harris hawks optimization algorithm. Applied Sciences, 2020, 10(4): 1403

[30] Shishegaran A, Saeedi M, Kumar A, Ghiasinejad H. Prediction of air quality in Tehran by developing the nonlinear ensemble model. Journal of Cleaner Production, 2020, 259: 120825

[31] Shishegaran A, Varaee H, Rabczuk T, Shishegaran G. High correlated variables creator machine: Prediction of the compressive strength of concrete. Computers & Structures, 2021, 247: 106479

[32] Shishegaran A, Boushehri A N, Ismail A F. Gene expression programming for process parameter optimization during ultrafiltration of surfactant wastewater using hydrophilic polyethersulfone membrane. Journal of Environmental Management, 2020, 264: 110444

[33] Shishegaran A, Saeedi M, Mirvalad S, Korayem A H. Computational predictions for estimating the performance of flexural and compressive strength of epoxy resin-based artificial stones. Engineering with Computers, 2023, 39(1): 347–372

[34] Liu B, Vu-Bac N, Zhuang X, Lu W, Fu X, Rabczuk T. Al-DeMat: A web-based expert system platform for computationally expensive models in materials design. Advances in Engineering Software, 2023, 176: 103398

[35] Liu B, Lu W. Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design. International Journal of Hydromechatronics, 2022, 5(4): 336–365

[36] Liu B, Vu-Bac N, Zhuang X, Fu X, Rabczuk T. Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites. Composites Science and Technology, 2022, 224: 109425

[37] Xia Y, Zhang C, Wang C, Liu H, Sang X, Liu R, Zhao P, An G, Fang H, Shi M. . Prediction of bending strength of glass fiber reinforced methacrylate-based pipeline UV-CIPP rehabilitation materials based on machine learning. Tunnelling and Underground Space Technology, 2023, 140: 105319

[38] Liu B, Lu W, Olofsson T, Zhuang X, Rabczuk T. Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites. Composite Structures, 2024, 327: 117601

[39] BigdeliAShishegaran ANaghshM AKaramiBShishegaran AAlizadehG. Surrogate models for the prediction of damage in reinforced concrete tunnels under internal water pressure. Journal of Zhejiang University. Science A, 2021, 22(8): 632–656 (in Chinese)

[40] Fathipour-Azar H. Hybrid data-driven polyaxial rock strength meta model. Rock Mechanics and Rock Engineering, 2023, 56(8): 5993–6007

[41] Liu L, Song Z, Zhou P, He X, Zhao L. AI-based rock strength assessment from tunnel face images using hybrid neural networks. Scientific Reports, 2024, 14(1): 17512

[42] Zhou Z, Chen C, Cai X, Wang P. Blastability evaluation for rock mass: Review and new tendency. Bulletin of Engineering Geology and the Environment, 2024, 83(12): 517

[43] Shishegaran A, Khalili M R, Karami B, Rabczuk T, Shishegaran A. Computational predictions for estimating the maximum deflection of reinforced concrete panels subjected to the blast load. International Journal of Impact Engineering, 2020, 139: 103527

[44] ShishegaranAMoradiMNaghshM A KaramiBShishegaran A. Prediction of the load-carrying capacity of reinforced concrete connections under post-earthquake fire. Journal of Zhejiang University. Science A, 2021, 22(6): 441–466 (in Chinese)

[45] Naghsh M A, Shishegaran A, Karami B, Rabczuk T, Shishegaran A, Taghavizadeh H, Moradi M. An innovative model for predicting the displacement and rotation of column-tree moment connection under fire. Frontiers of Structural and Civil Engineering, 2021, 15(1): 194–212

[46] Shishegaran A, Karami B, Danalou E S, Varaee H, Rabczuk T. Computational predictions for predicting the performance of steel 1 panel shear wall under explosive loads. Engineering Computations, 2021, 38(9): 3564–3589

[47] Dong M, Yao L, Wang X, Benatallah B, Zhang S, Sheng Q Z. Gradient boosted neural decision forest. IEEE Transactions on Services Computing, 2023, 16: 330–342

[48] Sun J, Wu S, Wang H, Wang T, Geng X, Zhang Y. Inversion of surrounding rock mechanical parameters in a soft rock tunnel based on a hybrid model EO-LightGBM. Rock Mechanics and Rock Engineering, 2023, 56(9): 6691–6707

[49] Yan Z, Chen H, Dong X, Zhou K, Xu Z. Research on prediction of multi-class theft crimes by an optimized decomposition and fusion method based on XGBoost. Expert Systems with Applications, 2022, 207: 117943

[50] Zhang H, Wang M. Search for the smallest random forest. Statistics and Its Interface, 2009, 2(3): 381–388

[51] Ye J, Yang Z, Li Z. Quadratic hyper-surface kernel-free least squares support vector regression. Intelligent Data Analysis, 2021, 25(2): 265–281

[52] ChoeI HKim G JKimN CKoM CRyomJ S HanR MHan T GHanI. Can quantum genetic algorithm really improve quantum backpropagation neural network? Quantum Information Processing, 2023, 22(3): 154

[53] SaraeeMJafari AYazdaniDShishehgarkhanehM BNouhi BTalatahariS. Hybrid Social Network Search and Material Generation Algorithm for Shape and Size Optimization of Truss Structures. Cham: Springer Nature Switzerland, 2023

[54] SuY. Research on blasting vibration analysis and construction technology optimization of Leye tunnel. Thesis for the Master Degree. Nanning: Guangxi University, 2023

[55] Handhal A M, Al-Abadi A M, Chafeet H E, Ismail M J. Prediction of total organic carbon at Rumaila oil field, Southern Iraq using conventional well logs and machine learning algorithms. Marine and Petroleum Geology, 2020, 116: 104347

[56] Ma T, Lin Y, Zhou X, Zhang M. Grading evaluation of goaf stability based on entropy and normal cloud model. Advances in Civil Engineering, 2022, 2022(1): 9600909

[57] Tanious R, Manolov R. Violin plots as visual tools in the meta-analysis of single-case experimental designs. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 2022, 18(3): 221–238

[58] Lawal A I, Kwon S, Kim G Y. Prediction of the blast-induced ground vibration in tunnel blasting using ANN, moth-flame optimized ANN, and gene expression programming. Acta Geophysica, 2021, 69(1): 161–174

[59] Kisi O, Alizamir M, Zounemat-Kermani M. Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data. Natural Hazards, 2017, 87(1): 367–381

[60] ZhouSZhang Z XLuoXHuangYYuZ YangX. Predicting dynamic compressive strength of frozen-thawed rocks by characteristic impedance and data-driven methods. Journal of Rock Mechanics and Geotechnical Engineering, 2023, S1674775523003219

[61] Liu B, Vu-Bac N, Zhuang X, Rabczuk T. Stochastic multiscale modeling of heat conductivity of polymeric clay nanocomposites. Mechanics of Materials, 2020, 142: 103280

[62] Liu B, Penaka S R, Lu W, Feng K, Rebbling A, Olofsson T. Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in northern Sweden. Technology in Society, 2023, 75: 102347

[63] Rajbahadur G K, Wang S, Oliva G A, Kamei Y, Hassan A E. The impact of feature importance methods on the interpretation of defect classifiers. IEEE Transactions on Software Engineering, 2021, 48(7): 2245–2261

[64] Oh S. Predictive case-based feature importance and interaction. Information Sciences, 2022, 593: 155–176

[65] Dauji S. Quantifying and addressing uncertainties in empirical vibration attenuation relationship for underground blast by re-sampling. SN Applied Sciences, 2019, 1(11): 1350

[66] Hu B, Hu X, Lin C, Du G, Ma T, Li K. Evolution of physical and mechanical properties of granite after thermal treatment under cyclic uniaxial compression. Sustainability, 2023, 15(18): 13676

[67] Xie S, Lin H, Chen Y, Ma T. Modified Mohr–Coulomb criterion for nonlinear strength characteristics of rocks. Fatigue & Fracture of Engineering Materials & Structures, 2024, 47(6): 2228–2242

[68] Fan L, Chang Y, Peng K, Bai Y, Luo K, Wu T, Ma T. Research progress on the mechanisms and control methods of rockbursts under water–rock interactions. Applied Sciences, 2024, 14(19): 3168495

[69] XieSLinH DuanHYao RMaT. A novel triaxial strength criterion for rocks based on the ultimate strength and its application. Geoenergy Science and Engineering, 2024, 213590

[70] Jayasinghe B, Zhao Z, Chee A G T, Zhou H, Gui Y. Attenuation of rock blasting induced ground vibration in rock–soil interface. Journal of Rock Mechanics and Geotechnical Engineering, 2019, 11(4): 770–778

[71] Guan X, Xu H, Fu H, Zhang W, Li P, Ding H, Yu K, Zhang S. Vibration characteristics, attenuation law and prediction method in the near field of tunnel blasting. Case Studies in Construction Materials, 2023, 19: e02662

[72] Ercins S. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study. Open Geosciences, 2024, 16(1): 20220633

[73] Bokai L, Yizheng W, Timon R, Olofsson T, Lu W. Multi-scale modeling in thermal conductivity of polyurethane incorporated with phase change materials using physics-informed neural networks. Renewable Energy, 2024, 220: 119565

[74] Ma T, Chen H, Zhang K, Shen L, Sun H. The rheological intelligent constitutive model of debris flow: A new paradigm for integrating mechanics mechanisms with data-driven approaches by combining data mapping and deep learning. Expert Systems with Applications, 2025, 269: 126405

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