ArticleScientific reports2024
Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Pre-blast prediction of ground vibration in surface mining using rock acoustic impedance and wave attenuation calibrated with in-situ measurements.Scientific reports · 2026Article
- Particle peak velocity prediction based on risk-oriented hybrid ensemble learning.Scientific reports · 2026Article
- Simultaneous prediction and optimisation of rock fragmentation and ground vibration using an ANN-RF ensemble in open-pit blasting.Scientific reports · 2025Article
- A comparative machine and deep learning approach for predicting ultimate bearing capacity of shallow foundations in cohesionless soil.Scientific reports · 2025Article
- Prediction of damage evolution in carbonate building stones subjected to simulated acid rain using M5P model.Scientific reports · 2025Article
- Application of the metaheuristic algorithms to quantify the GSI based on the RMR classification.Scientific reports · 2025Article
- Prediction of loess collapsibility coefficient using bayesian optimized random forest model.Scientific reports · 2025Article
- Research on early warning model of coal spontaneous combustion based on interpretability.Scientific reports · 2025Article
- Evaluating the RMR correlation with the rock mass wave velocity using the meta-heuristics algorithms.Scientific reports · 2025Article
- Machine learning-based prediction of heating values in municipal solid waste.Scientific reports · 2025Article
- Predicting peak particle velocity in pre-splitting of gas-producing devices using improved particle swarm optimization algorithm.Scientific reports · 2025Article
- Analysis of the characteristics of blasting seismic wave induced by explosive blasting with different coupling medium.Scientific reports · 2025Article
- Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine.Scientific reports · 2025Article
- Assessment of resilient modulus of soil using hybrid extreme gradient boosting models.Scientific reports · 2024Article
- Prediction of time-dependent bearing capacity of concrete pile in cohesive soil using optimized relevance vector machine and long short-term memory models.Scientific reports · 2024Article
- Prediction of peak particle velocity using hybrid random forest approach.Scientific reports · 2024Article
- Calibration of contact parameters of sandy soil for planting tiger nut based on non-linear tools.Scientific reports · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ground vibration caused by rock blasting is an extremely hazardous outcome of the blasting operation. Blasting activity has detrimental effects on both the ecology and the human population living in proximity to the area. Evaluating the magnitude of blasting vibrations requires careful evaluation of the peak particle velocity (PPV) as a fundamental and essential parameter for quantifying vibration velocity. Therefore, this study employs models using the relevance vector machine (RVM) approach for predicting the PPV resulting from quarry blasting. This investigation utilized the conventional and optimized RVM models for the first time in ground vibration prediction. This work compares thirty-three RVM models to choose the most efficient performance model. The following conclusions have been mapped from the outcomes of the several analyses. The performance evaluation of each RVM model demonstrates each model achieved a performance of more than 0.85 during the testing phase, there was a strong correlation observed between the actual ground vibrations and the predicted ones. The analysis of performance metrics (RMSE = 21.2999 mm/s, 16.2272 mm/s, R = 0.9175, PI = 1.59, IOA = 0.8239, IOS = 0.2541), score analysis (= 93), REC curve (= 6.85E-03, close to the actual, i.e., 0), curve fitting (= 1.05 close to best fit, i.e., 1), AD test (= 11.607 close to the actual, i.e., 9.790), Wilcoxon test (= 95%), Uncertainty analysis (WCB = 0.0134), and computational cost (= 0.0180) demonstrate that PSO_DRVM model MD29 outperformed better than other RVM models in the testing phase. This study will help mining and civil engineers and blasting experts to select the best kernel function and its hyperparameters in estimating ground vibration during rock blasting project. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency.
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