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An agentic framework for data augmentation in construction defect report classification
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DOI：10.1016/j.aei.2026.104679.png)
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Abstract
En 中文
Construction defect report classification faces critical challenges in data-constrained environments, including severe class imbalance, limited training data, and domain-specific linguistic complexity. While transformer-based models are widely adopted for text classification, their effectiveness diminishes in specialized domains with short, technical reports and limited computational resources. Traditional oversampling techniques like SMOTE fail to generate linguistically coherent synthetic data that preserves domain-specific terminology and reporting conventions.
Keywords:
Data augmentation
Text classification
Class imbalance treatment
Construction quality
Human in the loop
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