## 7 Conclusions  
This paper systematically reviews recent advances in PIT methodologies. The fundamental working principles, merits, and limitations of various NDT methods for both cast-in-place and precast piles are critically examined. Method selection typically involves trade-offs among cost, availability, reliability, and operational simplicity. The ideal method would comprehensively detect all critical structural defects and anomalies. However, no single method currently achieves this ideal. Consequently, method selection must consider pile characteristics, hydrogeological conditions, and project-specific requirements. The main conclusions of this paper are as follows:  
1. Low-strain integrity testing remains the most widely adopted PIT method for preliminary screening, with distinct advantages in cost (30-50% lower than acoustic transmission methods) and efficiency. This method is suitable for detecting shallow defects in piles installed in stable, homogeneous soil conditions. However, inherent limitations persist, including restricted detection depth, significant signal distortion from soil-pile interaction (up to 20% error in defect localization), and poor resolution for micro-defects (<5% cross-sectional change).
2. Thermal integrity profiling has emerged as an NDT method for identifying structural defects in cast-in-place piles during the initial curing phase (within 72 h post-pouring) through continuous thermal monitoring. Leveraging distributed fiber-optic sensors, TIP achieves full-length pile coverage (including pile tips) with 3-4% cross-sectional defect resolution, outperforming low-strain methods in anti-interference capability (immune to ambient vibration). However, the detection system is complex and puts higher requirements on signal recognition and interpretation.
3. AI integration has become a transformative force in PIT, with three paradigms showing distinct strengths. Feature-based machine learning excels in small datasets, achieving over 90% accuracy for single-defect classification but relies heavily on expert-designed features. End-to-end deep learning dominates multi-defect scenarios, reaching 99.17% accuracy for six defect types under high signal-to-noise ratios, but requires large-scale labeled data and lacks interpretability. Hybrid methods balance performance and practicality, achieving 97.8% accuracy in layered soil conditions and addressing both data scarcity and noise robustness. Despite progress, AI adoption faces bottlenecks: limited field datasets and regulatory gaps in global standards, with no unified validation protocols.  
Conceptualization, P.Q., X.L., and Z.C.; methodology, X.L. and Z.C.; software, P.Q.; validation, L.Y., Y.X., and X.L.; formal analysis, X.L.; investigation, Y.X. and X.L.; resources, P.Q. and L.Y.; data curation, X.L.; writing--original draft preparation, P.Q., X.L., and Z.C.; writing--review and editing, P.Q., L.Y., Y.X., X.L., and Z.C.; visualization, X.L.; supervision, P.Q. and X.L.; project administration, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Project No. 52078165).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Author [PERSON] was employed by Guangzhou Metro Construction Management Co., Ltd. Authors [PERSON], [PERSON] were employed by Guangzhou Guangzhou Construction Engineering Testing Center Co., Ltd. The remaining authors declare that the researchwas conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.