## 1 Introduction  
To transform the load from superstructures to the hard stratum, pile foundations have been widely designed in the construction of modern structures [1, 2, 3, 4]. The stability of structures mostly relies on the health situations of the pile foundations. Due to its importance, the health monitoring of pile foundations is always of special interest in engineering [5]. As a passive non-destructive testing (NDT) technique, acoustic emission (AE) has been successfully used for the health monitoring of pile foundations [6, 7]. An advantage of AE techniques is that in-service structures can be monitored continually without any disturbance [8, 9]. The detection of damage locations using the AE technique is an important research topic in NDT studies. AE refers to the elastic waves generated from the cracks in a failed material [10]. When failure occurs, elastic waves propagate inside the material and can be received by the AE sensors installed on the outer faces of the material [6, 11]. The elastic waves are collected by the AE data acquisition system and processed to detect the damage locations or evaluate the damage degree of the material [12]. Several applications of AE technique for detecting damages to concrete piles have been studied in recent years. [PERSON] et al. [3] conducted an experimental study to recognize and classify corrosion damage in concrete piles using an AE detection technique. [PERSON] et al. [5] studied the AE characteristics of failure process and discussed the feasibility of using AE for the damage monitoring of shallow pile foundations. [PERSON] et al. [13] proposed a wave propagation-based NDT technology for deep concrete piles. Artificial neural networks (ANNs) are one of the most popular machine learning algorithms that simulate the human brain's neural networks in terms of information processing [14; 15; 16]. ANNs are computational systems that are connected by a large number of elements [15]. ANNs have a strong ability to reveal the unknown relations between variables and predict the probable output by training the given variables [17]. ANNs have been successfully applied in the engineering field and have shown good intelligence ability [14]. Over the recent years, several applications of ANNs for predicting damage locations on plate-like structures have been reported [18; 19; 20]. However, the application of ANNs for predicting damage locations on real structures (three-dimensional structures) such as pile foundations has not yet been reported. Moreover, how to evaluate the prediction accuracy of damage locations using ANNs for real structures is another urgent issue. Accuracy metrics in ANNs are used to evaluate the goodness of predictions. Mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) are the most popularly used accuracy metrics for the evaluation of ANN prediction models in the weather forecasting, medical and engineering fields [21; 22; 23; 24; 25; 26; 27]. However, different accuracy metrics are based on different types of measurements. For example, the calculations of MSE, RMSE and MAE are based on squared errors and absolute errors. The calculations of MAPE and SMAPE are based on percentage errors. Different accuracy metrics show different kinds of goodness. As different accuracy metrics have their own advantages and disadvantages, for a specific prediction model, some accuracy metrics may not be appropriate. Thus, the selection of an appropriate accuracy metric to evaluate ANN prediction models is a very important issue. The purpose of this research was to build an ANN prediction model for damage locations of deep piles and propose a novel selection method for an appropriate metric to evaluate the accuracy of predictions.