# Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
## 1 Introduction  
In recent decades, embedded retaining structures have been increasingly used for excavations in urban areas around the world [1, 2, 3, 4]. The cantilever wall is a conventional earth-retaining structure with a relatively simple construction process and is commonly used for excavations no more than 6 m deep. Generally, they provide open excavations and do not require bracing or anchoring. However, excavation work will inevitably cause cantilever wall deflection, i.e., the wall top displacement, and ground movement, ultimately posing a risk to adjacent structures. Therefore, the control of deformation is as important as the safety requirements against collapse in the design of such retaining walls [5, 6]. In this regard, an accurate and practical tool for predicting the cantilever wall deflection should be of great interest to engineers and stakeholders. Numerous studies have been conducted to analyze the cantilever wall deflection using various methods, such as field measurements [7, 8, 9], centrifuge modelling tests [10, 11], numerical analyses [12, 13, 14, 15, 16], and analytical methods [17, 18, 19]. [PERSON] et al. [9] analyzed the monitoring data of diaphragm wall deflection using a Bayesian network. [PERSON] et al. [11] developed a centrifuge modelling system to study the response of a cantilever retaining wall. [PERSON] et al. [12] conducted a series of numerical analyses to investigate the ground movement caused by deep excavations in soft clay. [PERSON] et al. [13] studied the effect of soil spatial variability on the excavation-induced lateral wall deflection through a large number of numerical simulations. [PERSON] et al. [16] developed a simplified polynomial regression model to predict the maximum wall deflection. More recently, [PERSON] et al. [19] developed an efficient probabilistic back-analysis method for braced excavations using walldeflection data at multiple points. Generally, it is noted that the studies of field measurement and centrifuge modelling test are of high cost, and thus it is hard to obtain sufficient datasets. The analytical method is relatively rapid and of low cost, but the existing ones are commonly quite complex and sensitive to the input parameters, which are hard for engineers to validate from case to case. Numerical analysis [15] is most commonly used by the researchers among the above-mentioned investigated methods. However, it requires special soil testing to calibrate the parameters used in soil constitutive models and lengthy computer calculations [16], which are practically unrealistic especially at the preliminary design stage. As an alternative, machine learning (ML) algorithms have recently been introduced in the field of geotechnical engineering because of their advantages in dealing with nonlinear complicated problems [20; 21; 22]. One of the strongest ML algorithms is the so-called artificial neural network (ANN), which is widely used in predicting the ground displacements or the response of retaining structures. In general, traditional prediction methods such as the numerical approach generally need intensive simulations to obtain enough analysis results. Recently developed ANN models can be used as a convenient and reliable substitute for time-consuming field measurements or numerical analyses so that less computational power is needed for further studies. Considering the power of artificial neural networks (ANN) to solve nonlinear complicated problems, the application of ANNs in geotechnical problems is quite promising and can provide powerful tools for civil engineers in practice. In the past decades, many researchers have tried to apply ANN-based models to geotechnical problems. For instance, [PERSON] and [PERSON] [20] proposed a Bayesian neural network-based approach to predict the wall deflections in deep excavations. [PERSON] et al. [21] used the ANN approach to predict the optimized cantilever earth retaining wall. [PERSON] et al.