Prediction of ground vibration induced by blasting operations is a crucial challenge to engineers working in surface mines. This study aims to assess the efficiency of two advanced machine learning models in predicting ground vibrations in a granite quarry located in Malaysia. To this end, two intelligent models were proposed by hybridizing the relevance vector regression (RVR) with the grey wolf optimization (GWO) (which formed the RVR-GWO model) and with the bat-inspired algorithm (BA) (which formed the RVR-BA model). To the best of our knowledge, this is the first attempt to predict ground vibration using the RVR-GWO and RVR-BA models. The afore-mentioned models were developed and tested using 95 datasets. Then, the performance of the developed models was statistically checked through four comparative experiments using, among others, mean square error (MSE) and correlation coefficient (R). The results indicated the superiority of the RVR-GWO model over the RVR-BA model in terms of prediction precision. The RVR-GWO model with R of 0.915 and MSE = 7.920 predicted the ground vibration better than the RVR-BA model with R of 0.867 and MSE = 8.551. Accordingly, it was concluded that applying the GWO algorithm to RVR can result in high accuracy in the prediction of blast-induced ground vibration.

爆破作业引起的地面振动的预测对地表矿山的工程师来说是至关重要的挑战。这项研究旨在评估两种先进的机器学习模型在预测马来西亚花岗岩采石场的地面振动方面的效率。为此，通过将相关向量回归（RVR）与灰太狼优化（GWO）（形成RVR-GWO模型）和蝙蝠启发算法（BA）（形成RVR）混合，提出了两个智能模型-BA模型）。据我们所知，这是使用RVR-GWO和RVR-BA模型预测地面振动的首次尝试。使用95个数据集开发并测试了上述模型。然后，通过以下四个比较实验对开发模型的性能进行了统计检验，其中包括：R）。结果表明，就预测精度而言，RVR-GWO模型优于RVR-BA模型。R为0.915且MSE = 7.920的RVR-GWO模型预测的地面振动要好于R为0.867且MSE = 8.551的RVR-BA模型。因此，可以得出结论，将GWO算法应用于RVR可以在爆破引起的地面振动的预测中获得较高的精度。