# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
## 1 Introduction  
Foundation systems that transfer all or part of the superstructure load from weak soil to deep solid layers are called pile foundations. In superstructures such as high-rise buildings and long-span bridges, it is important to build a pile foundation to ensure structural safety, even if it incurs high costs. Late detection of pile defects may lead to significant expenses and project delays. According to [1], approximately 100,000 km of piles are installed annually in Europe, with 3-5% exhibiting structural defects. Similarly, ref. [2] reported that among 10,000 tested bored piles in the United States and Germany, approximately 15.3% displayed minor defects, with around 5.1% confirmed as having major defects such as fractures or discontinuities. Detecting these defects early can lead to substantial cost savings and improved project efficiency. In addition to cost-effectiveness,safety is a paramount concern in pile integrity testing, as undetected defects can result in catastrophic failures, endangering both the structure and public safety. Pile integrity testing is particularly valuable due to the difficulty of inspecting deep and potentially unstable shafts [3], as it allows for the early identification of issues that could compromise safety. In many cases, the pile head is the only part that can be accessed. To assess pile quality and integrity, engineers depend on non-destructive testing (NDT) methods [4]. Various NDT techniques have been developed for evaluating and analyzing the condition and integrity of piles (as discussed in Section 2). Low-strain pile integrity testing (LSPIT) of pile length and integrity is currently one of the most common NDT methods used for assessing the structural integrity of piles in soil [5]. The method is cost-effective and easy to implement, making it suitable for evaluating concrete or timber piles. It has been widely utilized across many countries for ensuring foundation quality [6]. LSPIT is based on the propagation analysis of elastic waves in the pile body. Elastic waves are produced by a hammer blow to the pile head by hand. The resulting waves move downward along the pile body and are reflected upward from the pile toe. Reflected waves, known as reflectograms, are recorded over time with the help of a digital accelerometer of the \"Pile Echo Tester (PET)\" device. Pile quality is predicted by analyzing these reflectograms. Traditionally, the interpretation of LSPIT reflectograms has relied on expert analysis, which is labor-intensive, time-consuming, and subject to human error. The accuracy of defect detection depends largely on the expertise of the analyst, leading to inconsistencies and inefficiencies. To address these challenges, deep learning-based automated approaches have gained attention as an alternative to manual interpretation. For these reasons, techniques that automate test signal interpretation and significantly reduce LSPIT processing time are highly valued and urgently needed in the industry. Some research on detecting defects in reflectograms primarily utilizes the finite element method, often in combination with wavelet analysis. However, the inherent complexity of geotechnical materials introduces numerous factors that affect the stress-strain relationship, making it challenging to develop a universally applicable model [7]. Neural networks' adaptability and the absence of a need for a predefined model make them suitable for addressing pile analysis problems. For instance, ref. [7] utilized recurrent neural networks to classify low-strain foundation pile testing signals, achieving promising results. A computer-aided reflectogram interpretation methodology was proposed that utilizes wavelet analysis to extract features from raw LSPIT signals, which are then input into an extreme learning machine (ELM) [8]. ANN models trained with back-propagation algorithms were proposed to diagnose pile integrity based on LSPIT data [9].