Prediction of Peak Particle Velocity of Blast-induced Ground Vibrations using Boosted Regression Trees Authored
Keywords:Blast-induced Ground Vibrations, Boosted Regression Tree, Linear Regression, PPV Prediction Model, Stepwise Regression
AbstractLoosening of rockmass during its excavation in an infrastructure project is carried by rock blasting. The blast-induced ground vibrations pose a major challenge to the blasting engineers, whose main objective is to control their potential to cause any damage to the buildings in the vicinity. The research reported in this paper explains how the error in the prediction of the Peak Particle Velocity (PPV) by the United States Bureau of Mines (USBM)-based approach can be minimised using machine learning techniques. The complex correlation between the blast parameter and the PPV value has been modelled using the least square boosted decision tree approach after the selection of the best suitable feature has been selected based on the correlation matrix. The proposed model automatically maps the input blast feature (SD) with the target PPV values by aggregating the decision of various weak learners. The generalization of the proposed model has been validated through a 5-fold cross-validation approach using a dataset comprising of two hundred blast records generated by monitoring the blasts at International airport site, Navi Mumbai, India. The assessment of the prognostic ability of the proposed model demonstrates that it has outperformed the USBM-based approach for PPV prediction. The results establish that the predictions by the proposed model are closer to the measured values than the other regression models.
Amiri, M., Amnieh, H.B., 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. Engineering with Computers, 32(4): 631–644. https://doi.org/10.1007/s00366-016-0442-5
Ambraseys, N.R., & Hendron, A.J. (1968). Dynamic behavior of rock masses, rock mechanics in engineering practice (KG Stagg and OC Zienkiewicz, eds.)
Davies, B., Farmer, I.W., & Attewell, P.B. (1964). Ground vibration from shallow sub-surface blasts. Engineer, 217pp.
Duvall, W.I., & Petkof, B. (1959). Spherical propagation of explosion-generated strain pulses in rock. US Department of the Interior, Bureau of Mines
Ghosh, A., & Daemen, J.J.K. (1983). A simple new blast vibration predictor (based on wave propagation laws). In: The 24th US symposium on rock mechanics (USRMS)
Gupta, R.N., Roy, P.P., & Singh, 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.
Rai, R., & Singh, T. (2004). A new predictor for ground vibration prediction and its comparison with other predictors. Indian Journal of Engineering and Materials Sciences, 11: 178–184.
Peng, K., Zeng, J., Armaghani, D.J., et al. (2021). A novel combination of gradient boosted tree and optimized ANN models for forecasting ground vibration due to quarry blasting. Natural Resources Research, 30: 4657–4671. https://doi. org/10.1007/s11053-021-09899-1
Zhang, X., Nguyen, H., Bui, X.N., et al. (2020). Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Natural Resources Research. 29: 711–721. https://doi.org/10.1007/s11053-019-09492-7
Ding, Z., Nguyen, H., Bui, X.N., et al. (2020). Computational intelligence model for estimating intensity of blast-induced ground vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms. Natural Resources Research, 29: 751–769. https://doi.org/10.1007/s11053-019-09548-8
Qiu, Y., Zhou, J., Khandelwal, M., et al. (2021). Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration. Engineering with Computers. https://doi.org/10.1007/s00366-021-01393-9
Yang H, Hasanipanah M, Tahir MM, & Bui DT. (2020). Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Natural Resources Research. 29: 739–750. https://doi.org/10.1007/s11053-019-09515-3
Fattahi, H., & Hasanipanah, M. (2021). Prediction of blast induced ground vibration in a mine using relevance vector regression optimized by metaheuristic algorithms. Natural Resources Research, 30: 1849–1863. https://doi.org/10.1007/s11053-020-09764-7
Dehghani, H., Shokri, B.J., Mohammadzadeh, H., et al. (2021). Predicting and controlling the ground vibration using Gene Expression Programming (GEP) and Teaching– Learning-Based Optimization (TLBO) algorithms. Environmental Earth Sciences, 80. https://doi.org/10.1007/s12665-021-10052-7
Chen, W., Hasanipanah, M., Rad, H.N., et al. (2021). A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration. Engineering with Computers, 37: 1455–1471. https://doi.org/10.1007/s00366-019-00895-x
Shang, Y., Nguyen, H., Bui, X.N., et al. (2020). A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Natural Resources Research, 29: 723–737. https://doi.org/10.1007/s11053-019-09503-7
Ding, X., Hasanipanah, M., Rad, H.N., Zhou, W. (2021). Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm. Engineering with Computers, 37: 2273–2284. https://doi.org/10.1007/s00366-020-00937-9
Zhou, J., Qiu, Y., Khandelwal, M., et al (2021). Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. International Journal of Rock Mechanics and Mining Sciences. 145pp. https://doi.org/10.1016/j.ijrmms.2021.104856
Zhang, H., Zhou, J., Armaghani, D.J., Tahir, M.M., Pham, B.T., & Huynh, V.V. (2020). A combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration. Applied Sciences, 10(3): 869. https://doi.org/10.3390/app10030869
Zeng, J., Roussis, P.C., Mohammed, A.S., et al. (2021). Prediction of peak particle velocity caused by blasting through the combinations of boosted-chaid and SVM models with various kernels. Applied Sciences. 11(8): 3705. https://doi.org/10.3390/app11083705
Nguyen, H., Choi, Y., Bui, X. N., & Nguyen-Thoi, T. (2020). Predicting blast-induced ground vibration in open-pit mines using vibration sensors and support vector regression- based optimization algorithms. Sensors, 20(1): 132. https://doi.org/10.3390/s20010132. PMid:31878226. PMCid:PMC6983179
Sonkar, R., Dhekne, P.Y., Londhe, N.D. (2021). Improvement in the prediction of peak particle velocity of blast-induced ground vibrations using K-means clustering. Arabian Journal of Geosciences, 14pp. https://doi.org/10.1007/s12517-021-08620-z
Freund, R.M., Grigas, P., Mazumder, R. (2017). A new perspective on boosting in linear regression via subgradient optimization and relatives. Annals of Statistics.
Mathworks. (2016). MATLAB - Mathworks - MATLAB & Simulink. Available from: www.mathworks.com