Multimodal Learning for Integrity Classification of Building Foundation Piles Using Low-Strain Reflection Testing

Low-strain reflection testing is widely used for the rapid screening of pile integrity, but its interpretation still relies heavily on manual judgment. This study proposes a dual representation learning framework for classifying the integrity of building foundation piles from low-strain testing records. A dataset containing 1139 piles from engineering projects was established and divided into four integrity classes. Each record was represented in two complementary forms: structured features extracted from engineering parameters and waveform characteristics, and a redrawn waveform image generated from coordinate point data. Support vector machine (SVM), random forest (RF), and convolutional neural network (CNN) models were used as single modality baselines, and their performance was compared with that of a multimodal neural network (MNN) trained on paired structured and image inputs. The multimodal model achieved the highest overall accuracy on the main evaluation subset, reaching 84.65%, whereas the random forest achieved the best Macro-Recall and Macro-F1. This result suggests that multimodal fusion mainly improved overall robustness rather than consistently enhancing performance across all classes. Clearly intact piles and severely defective piles were easier to identify, whereas Class II remained the most difficult category because of its borderline signal characteristics. In the supplementary external validation set, the same ranking of model performance was observed, and the multimodal model achieved an accuracy of 85%. These results indicate that the proposed framework has strong potential for computer-assisted screening of building foundation piles.