Creating models, capable of making accurate predictions of the Peak Particle Velocity (PPV) after a blast, is always one of the researchers’ most important goals. The use of such prediction can assist in assessing the intensity of the blast vibrations and more importantly in designing the whole blasting phase so as to mitigate potential problems to nearby structures. Main aim of this paper is to demonstrate the capabilities of artificial intelligence applications in geotechnology and more specifically to assess PPV and the characteristics of the blast wave attenuation in an underground construction case study in Greece, using multilayer feed forward artificial neural networks (ANNs). The results showed that the forecasting ability of the developed ANNs was, in almost every case, more accurate than the ones given by the use of traditional empirical formulas, as benchmarked using Root Mean Squared Error (RMSE) and coefficient correlation (R). In this manner, the ANNs proved to be a reliable and accurate method to assess PPV from underground blasting and once trained they become an efficient off-the-shelf tool to assist engineers both in the blast design and in the mitigation of blast wave induced problems.

创建能够准确预测爆炸后的峰值粒子速度 (PPV) 的模型始终是研究人员最重要的目标之一。使用这种预测可以帮助评估爆破振动的强度，更重要的是有助于设计整个爆破阶段，以减轻附近结构的潜在问题。本文的主要目的是展示人工智能在地质技术中的应用能力，更具体地说，使用多层前馈人工神经网络 (ANN) 在希腊的地下建筑案例研究中评估 PPV 和冲击波衰减的特性。结果表明，开发的人工神经网络的预测能力几乎在每种情况下都是 比使用传统经验公式给出的公式更准确，以均方根误差 (RMSE) 和系数相关性 (R) 为基准。通过这种方式，人工神经网络被证明是一种可靠且准确的方法来评估地下爆破产生的 PPV，并且一旦经过培训，它们就成为一种有效的现成工具，可以帮助工程师进行爆破设计和缓解冲击波引起的问题.