## 1 Introduction
As a time-frequency analysis method, wavelet transform can extract both time-frequency and frequency features using its multi-resolution analysis. It can portray the local features of the signal and provide more feature information for pattern recognition [13]. Refs. [14; 15] compared the simulation results of the time-domain signal of pile foundation dynamic measurement with the actual measurement results to demonstrate the feasibility of processing the test signal with wavelet transform. [PERSON] demonstrated that using wavelet transform to analyze the local features of the time-frequency domain signal could improve the interpretation accuracy of the abnormal segment of the pile foundation [16]. The field of signal recognition applied the superior feature extraction ability of the convolutional neural network (CNN). The traditional 2D-CNN is better at processing image-like 2D data [17]. [PERSON] et al. trained CNN models using velocity time-domain images of pile foundations to achieve integrity classification of single-defect piles [18]. [PERSON] recombined 400 \(\times\) 1 one-dimensional velocity signals of pile foundations and wavelet-transformed signals into 20 \(\times\) 20 \(\times\) 1 two-dimensional data as the input of 2D-CNN, which improves the performance and robustness of the CNN classifier [19]. The 1D-CNN is capable of one-dimensional processing data of time series class. [PERSON]. [20] proposed a 1D-CNN model for raw environmental vibration signals, which can automatically extract the best damage-sensitive features from the raw acceleration signals for structural damage detection. [PERSON]. [21] used a compact 1D-CNN to achieve online condition monitoring and severity identification of ball bearings. [PERSON] used a 1D-CNN model to evaluate the comprehensive damage assessment after an earthquake [22]. Recently, other types of signal recognition have well used TC-CNN's powerful and flexible feature extraction capability. However, the multi-defect identification of piles has not applied this feature extraction technology. [PERSON] input the rolling bearing vibration spectrum signal and the corresponding generalized S transform time-frequency diagram into 1D-CNN and 2D-CNN, respectively. Then the future fusion layer spliced feature vectors output by the two CNN models. Finally, under the data imbalance condition, the model obtained a better fault diagnosis effect and robustness [23]. [PERSON] takes the 1D time-domain features of the local discharge pulse and the corresponding wavelet time-frequency map as the input of TC-CNN, and then sends them to the classifier for classification. Finally, a model capable of identifying fault information is obtained [24]. This paper proposes a new method for identifying multi-defect types of concrete piles based on TC-CNN and LSPIT signals. After solving the problem of adaptive meshing of the multi-defect pile-soil model, the secondary development of finite element software can quickly obtain the time domain signals of multi-defect piles with different defect degrees. The time-domain signal is processed using the wavelet packet denoising technique. The 1D time-domain signal, before and after denoising, and the corresponding 2D wavelet time-frequency map are input to TC-CNN to retain more data information and prevent overfitting. This data processing operation taps more potential multi-defective pile features and improves classification accuracy.