Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations.

爆炸引起的地面振动被认为是爆破雷声中最危险的现象之一，它甚至可能造成人员伤亡和对相邻建筑物的严重破坏。测量峰值粒子速度（PPV）有助于了解实际的振动水平，但是由于涉及爆破设计，炸药和岩石参数，因此在爆破前预测爆破振动是一项繁琐的工作。如今，智能系统的有效应用已在科学和技术的不同领域得到认可。在本文中，开发了一种基因表达编程（GEP）模型，以各种爆破模式作为模型输入来预测PPV，这表明所实现模型的准确性很高。另外，为了优化爆破模式，使爆破操作期间的地面震动最小，所开发的功能性GEP模型被作为目标函数用于蚱optimization优化算法（GOA）。使用试错机制执行GOA模型的构建，以找出可能的最佳相关GOA参数。最后，观察到，利用GOA技术，通过优化的爆炸参数，包括3.21 m的负荷，3.75 m的间距和225 kg的每次延迟装药，可以将PPV降低67％。还进行了敏感性分析，以了解每个输入参数对爆炸振动的影响。通过优化的爆破参数（包括负载3.21 m，间距3.75 m和每次延迟225 kg的装料），PPV可以降低67％。还进行了敏感性分析，以了解每个输入参数对爆炸振动的影响。通过优化的爆破参数（包括负载3.21 m，间距3.75 m和每次延迟225 kg的装料），PPV可以降低67％。还进行了敏感性分析，以了解每个输入参数对爆炸振动的影响。