Air overpressure (AOp) is one of the most important undesirable effects induced by blasting operations in the mining or tunneling projects. Hence, the present precise model for the prediction of AOp would be much beneficial to control the AOp. To this end, the present study proposes a new hybrid of group method of data handling (GMDH) and genetic algorithm (GA). In the other words, the GA is used to optimize the GMDH. The proposed GMDH–GA model was constructed, trained, and tested based on a collection of 84 actual datasets collected from the Shur river dam region. In the modeling, four input parameters were considered: maximum charge per delay, distance between the blasting point and monitoring station, powder factor and rock mass rating. The coefficient of determination ( R 2 ), root mean square error (RMSE) and variance account for (VAF), as the statistical performance indices, were used to evaluate the accuracy of the proposed GMDH–GA model. Consequently, the results indicate that the predicted values using the GMDH–GA model are in excellent agreement with the actual data (with the R 2 of 0.988), which demonstrate the reliability of the GMDH–GA model.

中文翻译：

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开发一种创新的软计算方案，用于使用 GA 优化的 GMDH 预测矿井爆破产生的空气超压

空气超压 (AOp) 是采矿或隧道工程中爆破作业引起的最重要的不良影响之一。因此，目前用于预测 AOp 的精确模型将有利于控制 AOp。为此，本研究提出了一种新的数据处理组方法 (GMDH) 和遗传算法 (GA) 的混合。换句话说，GA 用于优化 GMDH。提议的 GMDH-GA 模型是基于从舒尔河大坝地区收集的 84 个实际数据集的集合构建、训练和测试的。在建模中，考虑了四个输入参数：每次延迟的最大装药量、爆破点与监测站之间的距离、粉末系数和岩体等级。决定系数 (R 2 )、均方根误差 (RMSE) 和方差解释 (VAF)，作为统计性能指标，用于评估所提出的 GMDH-GA 模型的准确性。因此，结果表明使用 GMDH-GA 模型的预测值与实际数据非常吻合（R 2 为 0.988），这证明了 GMDH-GA 模型的可靠性。