## 1 Introduction  
Rapid urbanization leads to severe traffic congestion in many cities, especially in metropolises, and constructing road tunnels is an effective solution to this problem [1]. The cut-and-cover method, which involves the excavation of foundation pits and the construction of tunnel structures within the foundation pits, is a commonly used construction method for road tunnels in cities. Foundation pit works can result in ground settlement surrounding a foundation pit; however, given that many foundation pits are located in the central areas of cities with mass roads, bridges, buildings, and pipelines nearby, excessive settlements may crack pavements and pipelines and even lead to the collapse of buildings and bridges [2, 3]. Moreover, excessive settlements surrounding a foundation pit may indicate the foundation pit is at a high risk of collapse. In practice, detailed calculations regarding the deformation of a foundation pit and its surrounding ground settlements would be performed during the design of the foundation pit to avoid excessive settlements. However, the precise prediction of ground settlements at the design stage remains unrealistic because prediction accuracies of these calculation methods largely depend on the properties of soils and execution process, while these factors are uncertain at the design stage [4]. Thus, field observation data on ground settlement are usually collected at the construction stage to aid in the understanding of the actual ground settlement, and based on these field data, it is possible to dynamically update the prediction of ground settlements. A full understanding of future ground settlements during the construction of foundation pits enables constructors to implement timely and appropriate control measures to mitigate ground settlement and thereby prevent accidents resulting from excessive settlements. Accordingly, it is of critical importance to accurately predict ground settlements during the construction of foundation pits, with the aim of mitigating the construction risk of tunneling projects at the construction stage. To estimate the maximum ground settlements induced by excavation works, previous studies have developed various methods, including empirical methods, numerical methods, and machine learning-based methods. Empirical methods analyze the historical ground settlement data and represent the shape of the daily settlement trough induced by tunneling works using Gaussian or normal distribution curves; however, such methods cannot provide highly accurate results mainly because of the simple empirical relationships established to predict ground settlements [5,6]. Numerical methods overcome some limitations in empirical methods by using the finite element method, in which tunnel-soil-ground interactions are explicitly analyzed by taking into consideration a number of relevant factors, such as the non-linear behavior of soils, ground heterogeneity, and construction methods. However, numerical methods are criticized with regard to their accuracy and validity because finite element models are time-consuming to create and verify and are sensitive to boundary conditions [7,8]. With the development of artificial intelligence technologies, machine learning-based methods have been successfully applied in the prediction of excavation-induced ground settlements, and among these methods, artificial neural networks (ANNs) have been proved to be accurate and efficient for such prediction [1]. Yet, most of these machine learning-based methods focus on the prediction of the maximum ground settlements and do not accommodate for the dynamic prediction of ground settlements on a daily basis [9]. These studies developed different machine learning algorithms to map the relationships between static parameters, such as soil properties and the depths of foundation pits, and the maximum ground settlement. On the other hand, daily ground settlements may be significantly affected by time-dependent influence factors, such as construction stages and weather conditions.