The vertical tunneling method is an emerging technique to build sewage inlets or outlets in constructed horizontal tunnels. The jacking force used to drive the standpipes upward is an essential factor during the construction process. This study aims to predict the jacking forces during the vertical tunneling construction process through two intelligence systems, namely, artificial neural networks (ANNs) and hybrid genetic algorithm optimized ANNs (GA-ANNs). In this paper, the Beihai hydraulic tunnel constructed by the vertical tunneling method in China is introduced, and the direct shear tests have been conducted. A database composed of 546 datasets with ten inputs and one output was prepared. The effective parameters are classified into three categories, including tunnel geometry factors, the geological factor, and jacking operation factors. These factors are considered as input parameters. The tunnel geometry factors include the jacking distance, the thickness of overlaying soil, and the height of overlaying water; the geological factor refers to the geological conditions; and the jacking operation factors consist of the dead weight of standpipes, effective overburden soil pressure, effective lateral soil pressure, average jacking speed, construction hours, and soil weakening measure. The output parameter, on the other hand, refers to the jacking force. Performance indices, including the coefficient of determination (R²), root mean square error (RMSE), and the absolute value of relative error (RE), are computed to compare the performance of the ANN models and the GA-ANN models. Comparison results show that the GA-ANN models perform better than the ANN model, especially on the RMSE values. Finally, parametric sensitivity analysis between the input parameters and output parameter is conducted, reaching the result that the height of overlaying water, the average jacking speed, and the geological condition are the most effective input parameters on the jacking force in this study.

中文翻译：

---

基于神经遗传模型的竖向隧道工程顶进力预测

垂直隧道法是在水平隧道中建造污水入口或出口的新兴技术。在施工过程中，用于向上推动竖管的顶起力是至关重要的因素。这项研究旨在通过两个智能系统，即人工神经网络（ANN）和混合遗传算法优化的神经网络（GA-ANN），预测垂直隧道施工过程中的顶推力。本文介绍了我国采用垂直隧洞法修建的北海水工隧洞，并进行了直接剪切试验。准备了一个由546个数据集组成的数据库，其中包含10个输入和1个输出。有效参数分为三类，包括隧道几何因素，地质因素和顶进作业因素。这些因素被视为输入参数。隧道的几何因素包括顶升距离，覆土厚度和覆水高度。地质因素是指地质条件；顶进作业的因素包括立管的自重，有效上覆土压力，有效侧向土压力，平均顶进速度，施工时间和土壤软化措施。另一方面，输出参数指的是顶升力。性能指标，包括确定系数（R 顶进作业的因素包括立管的自重，有效上覆土压力，有效侧向土压力，平均顶进速度，施工时间和土壤软化措施。另一方面，输出参数指的是顶升力。性能指标，包括确定系数（R 顶进作业的因素包括立管的自重，有效上覆土压力，有效侧向土压力，平均顶进速度，施工时间和土壤软化措施。另一方面，输出参数指的是顶升力。性能指标，包括确定系数（R²），计算均方根误差（RMSE）和相对误差的绝对值（RE），以比较ANN模型和GA-ANN模型的性能。比较结果表明，GA-ANN模型的性能优于ANN模型，尤其是在RMSE值上。最后，对输入参数和输出参数之间的参数敏感性进行了分析，得出的结果是，在本研究中，覆水高度，平均顶升速度和地质条件是最有效的顶升力输入参数。