# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## 1 Introduction  
The rapid construction of new infrastructure (including roads, railways, subways, etc.) has been, and will continue to be, a key process of urbanization in many countries. However, as the city grows, the available area for its expansion becomes restricted. In this context, the growth of underground space for infrastructure needs has achieved major developments in the last few decades. Despite significant technical and scientific developments, a crucial issue connected to tunneling in metropolitan regions persists. Ground movement induced by tunneling is unavoidable, which manifests as settling troughs at the ground surface, and many accidents, such as structural damage and ground collapse, may occur as a result of deformation. The Earth pressure balance (EPB) shield method has been widely used in the development of urban underground space, especially in the subway systems [1]. Over the last few decades, the EPB shield method has shown high adaptability to different types of formations because of the advancements in soil conditioning technology [2; 3]. However, predicting the magnitude and distribution of ground deformations induced by EPB shield driving, as well as assessing their effect on closely spaced structures, is a component of tunnel construction as significant as structural safety [4]. Various approaches, including on-site monitoring, analytical solutions, model tests, and numerical models, have been used to evaluate the ground displacement generated by shield driving. In terms of on-site monitoring, the surface deformation caused by the tunneling in the soft soil can be described in the form of a Gaussian curve, with the maximum settlement located above the tunnel centerline [5]. On the time scale of occurrence, this settlement trough can be distinguished into three types: short-term, medium-term, and long-term deformation [6], despite the fact that it might be difficult to tell them apart at times [7]. Based on the monitoring data, a series of empirical equations on the transversalsettlement profile were derived and became the most common assessment used in construction applications [8]. In addition to field monitoring, analytical methods mainly involve the elastic continuum approach and have been extensively used in the case of shield tunneling owing to their stringent framework [9]. Even for closed-form analytical solutions, some empirical parameters, such as ground loss, are required for accurate predictions, so they can also be called semiempirical methods. In addition, the model tests, as well as the numerical models, are often used to investigate the effect of a specific factor on settlement or for detailed analysis of a particularly complex case [10; 11; 12]. These methods may be time-consuming in assessing all conditions along a tunnel and lack the flexibility to provide quick feedback, leaving them impractical for engineers to use in less complex situations [13]. In recent years, artificial intelligence (AI) methods, such as artificial neural networks (ANNs), and some evolutionary algorithms, have been developed for tunneling-induced settlement prediction [14]. However, the accuracy of AI algorithms is strongly reliant on the considered datasets. Hence, the empirical or semiempirical method remains the most common method for predicting tunneling-induced deformations, with the main advantage of higher prediction reliability based on a small number of input parameters. In this study, a classical empirical method for predicting the long-term deformation was used in a typical water-rich soft stratum. Based on the monitoring data collected from 59 cross sections, the prediction parameters in the empirical method and their variability were analyzed and summarized. Additionally, an analytical method was established and used to predict the short-term ground deformation induced by shield driving. A good agreement was observed between the proposed analytical method and the field measurements.