# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
## 1 Introduction
In this approach, two ANN models were developed; one for identifying defect patterns and another for assessing the severity of defects by computing changes in equivalent cross-sectional area. An artificial neural network (ANN) classifier is employed as the primary tool for the automatic interpretation of results from low-strain integrity testing [10]. To generate the initial dataset, an experimental site was set up featuring ten CFA piles with known defects. The Fourier transform was applied to the collected signals. Based on the results, it was concluded that the ANN-classifier can be used as the primary tool for automatic interpretation of low-strain integrity testing results. A method of pile integrity classification and recognition based on CNN was proposed, which includes one input layer, four convolutional layers, four pooling layers, two fully connected layers, and an output layer [11]. A convolutional neural network (CNN) was proposed to classify five types of defects, including neck defect, bulge imperfection, weak concrete, crack, or broken pile [12]. A two-channel CNN was introduced to process both one-dimensional time-domain signals and two-dimensional wavelet time-frequency maps derived from LSPIT data [13]. A major challenge in many of these applications is the limited availability of sufficient training data [14]. Furthermore, the substantial imbalance in real-world data collected from the field negatively impacts model performance. This imbalance exists because the number of intact piles significantly outweighs the number of defective piles. Ref. [15] highlighted the potential of deep learning models to handle class imbalance in datasets, a common issue in defect detection. Refs. [16; 17] demonstrated the effectiveness of using synthetic data and simulators to train deep learning models, bridging the reality gap by domain randomization. These studies underscore the potential of artificial intelligence to improve the accuracy and efficiency of pile defect detection. Despite previous applications of CNNs to pile integrity testing, significant gaps remain concerning their generalizability to real-world, noisy, and heterogeneous data. This study hypothesizes that CNN architectures enhanced with transfer learning can robustly generalize from limited real reflectogram data, outperforming traditional methods and providing more objective, repeatable results. Previous research has quantified expert variability in LSPIT interpretation, highlighting the subjective and inconsistent nature of manual analysis [8]. Unlike prior works that predominantly relied on synthetic or laboratory data, our study leverages real-world reflectogram signals obtained from field tests, enhancing the practical relevance of the results. Furthermore, we benchmark multiple state-of-the-art pre-trained CNN architectures using a standardized and reproducible pipeline. The evaluation is carried out on an unseen test set, highlighting the generalization ability of each model, which has not been extensively explored in previous studies. Finite element method (FEM)-based and wavelet-based methods, although theoretically robust, typically require extensive computational resources and specialized expertise, making them less practical for rapid, on-site evaluation compared to data-driven approaches like CNNs. In this study, we address these challenges by utilizing a dataset of 328 reflectograms collected from real construction sites, comprising 246 intact and 82 defective piles. To mitigate the class imbalance issue, oversampling techniques were applied to improve model training. The CNN model used in our approach leverages transfer learning to extract features from reflectograms, aiming to provide an accurate and efficient solution for pile defect detection.