Monitoring structural integrity through accurate crack detection is fundamental to ensuring the safety and longevity of civil engineering infrastructure. Vision-based methods, supported by advancements in deep learning, have gained prominence in structural health monitoring (SHM). However, these methods often suffer from limited performance due to insufficient diversity and scale in crack image datasets, which are costly and challenging to acquire. This study introduces a novel framework that integrates a text-to-image generative model with large language models to synthesize realistic crack images for training deep neural networks. A prompt engineering approach is utilized to generate high-quality textual descriptions, guiding the creation of a large-scale, diverse dataset that simulates a wide range of crack scenarios. The synthesized dataset significantly enhances model training, as demonstrated in two key SHM tasks: crack classification and crack object detection. Neural networks trained with the augmented dataset show up to a 60% improvement in precision over baseline models trained on real-world data alone. These results highlight the potential of generative models to address data scarcity in SHM, enabling more robust and accurate crack detection. This research provides a scalable and efficient solution for improving machine learning-based SHM applications and paves the way for further exploration of generative methods in structural monitoring tasks.
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