In past decades, impact hammers have played a key role in underground construction. Simply in Istanbul, impact hammers have been used to excavate more than 20 km of metro tunnels. Thus, determining the instantaneous breaking rate (IBR) of an impact hammer is attracting increasing attention. A number of IBR prediction models have been developed for impact hammers. However, there is still a demand for models that require a smaller number of easy-to-obtain rock properties as inputs and provide a reasonable level of accuracy, a wide range of applications, and high reliability. This study had the goal of developing such a prediction model based on an investigation of two subway tunnels built in Istanbul. In order to enhance the results generated by multiple linear regression analysis, a customized tool was developed for the non-linear analysis of a relatively large set of data collected for the present research. Gene expression programming (GEP) and particle swarm optimization (PSO) were merged to create a non-linear analysis tool. The GEP–PSO algorithm was trained using 80% of the available data, with the remaining 20% reserved to validate the results. The output of the algorithm was presented in the form of a mathematical equation that predicted the IBR using the uniaxial compressive strength, rock quality designation, Schmidt hammer rebound value, and machine power as input parameters. The predicted IBR values were in remarkable agreement with the recorded values. In order to verify the efficiency of the proposed prediction model, it was successfully tested against previously developed models for which input parameters were available. In addition, the proposed model was investigated under hypothetical circumstances to ensure that it legitimately described the performance changes due to changes in the input parameters. The model developed in this research is proposed as an accurate and reliable tool for predicting the performance of impact hammers over a wide application range.

在过去的几十年中，冲击锤在地下建筑中发挥了关键作用。仅在伊斯坦布尔，冲击锤就已经用于挖掘20多公里的地铁隧道。因此，确定冲击锤的瞬时断裂率（IBR）正引起越来越多的关注。一些IBR已经为冲击锤开发了预测模型。然而，仍然需要这样的模型，其需要较少数量的易于获得的岩石特性作为输入并且提供合理水平的精度，广泛的应用范围和高可靠性。这项研究的目的是基于对伊斯坦布尔建的两条地铁隧道的调查，开发出这样的预测模型。为了增强通过多元线性回归分析生成的结果，开发了一种定制工具，用于非线性分析本研究收集的相对较大的数据集。将基因表达编程（GEP）和粒子群优化（PSO）合并以创建一个非线性分析工具。在GEP - PSO使用80％的可用数据对算法进行了训练，其余20％保留用于验证结果。该算法的输出以数学方程式的形式呈现，该数学方程式使用单轴抗压强度，岩石质量指定，施密特锤回弹值和机器功率作为输入参数来预测IBR。预测的IBR值与记录的值明显一致。为了验证所提出的预测模型的效率，已针对输入参数可用的先前开发的模型成功进行了测试。此外，在假设的情况下对建议的模型进行了研究，以确保其合理地描述了由于输入参数的变化而导致的性能变化。这项研究中开发的模型被认为是在广泛的应用范围内预测冲击锤性能的准确而可靠的工具。