## 2 Literature Review
### Machine Learning-Based Methods for the Prediction of Ground Settlements  
Differing from empirical and numerical methods that rely on a thorough understanding of the complex, highly nonlinear, and uncertain responses of underground structures in soils, machine learning-based methods intend to learn patterns from existing data, collected from field instrumentation or historical cases [1]. Specifically, machine learning-based methods aim at establishing the highly nonlinear relationships between inputs and the target output, i.e., ground settlements. Four machine learning algorithms have been widely used for the prediction of ground settlements: ANNs [3; 20; 28; 29; 30; 31; 32], support vector machines (SVMs) [9; 33; 34; 35], random forest methods (RFMs) [36], and decision trees (DTs) [37]. [PERSON] and [PERSON] [20] developed an ANN to predict the intensity and the location of the maximum ground settlement in multiple construction stages based on three inputs: excavation areas, construction methods, and excavation depth. [PERSON] et al. [37] adopted ANNs, SVMs, and DTs to predict the maximum ground settlement of urban tunnels based on three operational parameters, including tunnel width, tunnel depth, and excavation methods, and three soil parameters, including cohesion, friction angle and elasticity modulus. Among these machine learning algorithms, ANNs generally perform best when the relationships between inputs and outputs are highly non-linear, and, therefore, ANNs are especially suitable for solving ground settlement prediction problems where relationships between input parameters and ground settlements are highly non-linear [1].  
A majority of existing studies focus on predicting the maximum ground settlements using static input parameters and neglect the capability of their methods to perform dynamic predictions [38]. Such static results fail to fulfill the needs regarding the dynamic updates of ground settlements over time [9]. Accordingly, some researchers intended to utilize real-time obtained field settlement-monitoring data, which directly reflect the comprehensive effect of all the influential variables and can provide deep insight into the future settlement, to realize dynamic predictions. [PERSON] and [PERSON] [39] developed a back-propagation neural network (BPNN) to predict the ground settlement of a foundation pit in the next stage based on the monitoring settlement data in the last four stages. [PERSON] et al. [3] focused on the development of an ANN-based approach to predict the daily ground settlement by utilizing real-time settlement monitoring data as the inputs of an ANN to consider the influence of time series.  
In addition to static influence factors and previous settlement monitoring data, time-dependent construction stages, such as the increase in excavation depth, the casting of bottom slabs, and the installation and removal of braces, have a significant influence on the change of daily ground settlements [40; 41]. Moreover, weather conditions may also affect daily ground settlements by affecting the level of underground water. For example, continuous heavy rain may increase the level of underground water and, therefore, may result in the change of earth pressure on supporting structures. Nevertheless, time-dependent influence factors were generally neglected in current machine learning-based methods and failing to do so will limit the settlement-prediction accuracy of ANNs. For this reason, time-dependent influence factors--such as construction stages and weather conditions--should be considered in ANNs to facilitate accurate and dynamic prediction of daily ground settlements surrounding a foundation pit.