###### Abstract  
If the layer of soil surrounding a pile is not taken into account during the engineering detection process, the velocity-time curve might show asymptotic diameter shrinkage or diameter expanding features, which would alter the interpretation of the test findings. In this study, we suggest combining multi-feature extraction and a convolutional neural network (CNN) to increase accuracy in pile defect recognition for layered soil conditions and traditional deep learning flaws. First, numerical simulations are run to create velocity-time curves for foundation piles under layered soil conditions. Then, the data are extracted from three dimensions: time domain, frequency domain, and time-frequency domain, respectively, and fused into a set of feature vectors. Finally, a foundation pile defect identification model combining multi-scale features and CNN is established. The findings demonstrate that the CNN model has 97.8% accuracy while the PNN has 28.6% accuracy, demonstrating that the approach is very reliable. layered soil; multi-features extraction; defect recognition; numerical simulation; convolutional neural network (CNN) +
Footnote †: journal: Applied  
deal of artificiality in selecting the basis function. Many scholars have selected the wavelet basis function with a stroke [3; 4; 5]. The same signal uses different wavelet basis functions to decompose. It will have different effects, which directly affect the discriminant effect of the dynamic signal and result in a reduction in the method's practicality. Several Intrinsic Mode Functions (IMF) are directly separated from the signal using the Empirical Mode Decomposition (EMD) technique [6]. The fact that each IMF is orthogonal to the others is essential. Nevertheless, the only drawback is that modal aliasing occurs quickly in decomposition. Ensemble Empirical Mode Decomposition (EEMD) is an excellent solution to the phenomenon of mode mixing. In 2009, [PERSON] et al. [7] from Florida State University added white noise to EMD. However, it is necessary to set the white noise amplitude and the number of iterations artificially, which results in significant reconstruction errors and poor decomposition completeness. To overcome the reconstruction inaccuracy, [PERSON] et al. [8] developed an entire ensemble empirical mode decomposition with adaptive noise (CEEMDAN). White noise then embraces the signal using the original EEMD approach, effectively reducing the model complexity. However, CEEMDAN still produces a small number of faulty components and residual noise in the decomposition process. Therefore, [PERSON] et al. [9] proposed an Improved CEEMDAN (ICEEMDAN) in 2014. Based on CEEMDAN, the signal is continually divided by adding Gaussian white noise, and the resulting signal is averaged. In contrast to the CEEMDAN signal decomposition technique, this method dramatically inhibits the phenomenon of faulty components in signal decomposition and effectively improves the efficiency of signal decomposition. All of the above signal processing methods could successfully capture the signal's features, and some professors have researched the extraction of eigenvalues. [PERSON] [10] used multi-resolution analysis to capture the feature vector representing the power spectrum's power composition fed into a neural network to identify the pile defect. To obtain the energy mean and power spectrum in the frequency range as feature vectors to input into the neural network, [PERSON] [11] employed wavelet transform. [PERSON], [PERSON] [12] selected Daubechies4 wavelet 5-layer multi-resolution analysis to extract the characteristic frequency spectrum amplitude. In 2015, [PERSON], [PERSON] and [PERSON], [PERSON] [13] of the Harbin Institute of Technology proposed for the first time that quantized information entropy could be used to detect stress wave signals, and the quantized information entropy was proved to be very comprehensive after experiments.