The blast-induced ground vibration (BIGV) is a severe environmental impact of blasting as it can affect the integrity of the structures and cause civil unrest. In this study, the BIGV of Daejeon tunnel was predicted taking into consideration parameters such as hole length, the charge per delay, number of holes, total charge, distance from the measuring station to the blasting point and the rock mass rating as the input parameters, while the peak particle velocity (PPV) was the targeted output parameter. An artificial neural network (ANN) model was first simulated. The optimum ANN structure obtained was optimized using a novel moth-flame optimization algorithm (MFO). The gene expression program (GEP) was also used to develop another new model. The proposed models were compared with the multilinear regression (MLR) model and the selected empirical models for the PPV predictions. The performance of the proposed model was evaluated using statistical indices such as adjusted coefficient of determination (adj R²), mean square error (MSE), mean absolute error (MAE), and the variance accounted for (VAF). The proposed MFO-ANN outperformed other models with the adj R² of 0.9702 and 0.9577, VAF of 97.0472 and 95.9832, MSE of 0.0009 and 0.0008, and MAE of 0.0233 and 0.0216 for the respective training and testing phases. The sensitivity analysis was conducted using the weight partitioning method (WPM), and the charge per delay has the highest influence on the predicted PPV. This study indicates the suitability of the proposed models for the prediction of PPV.

爆炸引起的地面振动（BIGV）是爆破对环境的严重影响，因为它会影响结构的完整性并引起内乱。在这项研究中，大田隧道的BIGV预测是将孔长，每次延迟装药量，孔数，总装药量，从测量站到爆破点的距离以及岩体质量等参数作为输入参数进行预测的，而峰值粒子速度（PPV）是目标输出参数。首先模拟了人工神经网络（ANN）模型。获得的最佳人工神经网络结构进行了优化，使用一种新颖的蛾-火焰优化算法（MFO）。基因表达程序（GEP）也用于开发另一个新模型。将提出的模型与多线性回归（MLR）模型以及为PPV预测选择的经验模型进行了比较。使用统计指标（例如调整后的确定系数（adj）R ²），均方误差（MSE），平均绝对误差（MAE）和方差占（VAF）。拟议的MFO-ANN的其他R2^分别为0.9702和0.9577，VAF为97.0472和95.9832，MSE为0.0009和0.0008，MAE为0.0233和0.0216，优于其他模型。使用权重分配方法（WPM）进行了灵敏度分析，并且每个延迟的电荷对预测PPV的影响最大。这项研究表明所提出的模型对PPV预测的适用性。