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Abstract: The rapid and accurate detection of concrete sand moisture content (MC) is crucial for ensuring concrete quality. However, existing unimodal detection methods are constrained by limited representative features and lack robustness. Multimodal operations often involve simple concatenation of features from different modalities, lacking potential interactivity among features. To address this issue, a novel robust cross-modal integration fusion model, which uses five branches to extract the features of images, near-infrared spectrum, and dielectric constant and a multilevel cross-modal integration fusion network to fuse these features, is proposed for the rapid detection of MC in concrete sand. Specifically, the multilevel cross-modal integration fusion network comprises a feature attention module, a cross-modal self-attention fusion module, and an integrated output module. The feature attention module enhances the feature representation from each modality, reducing the interference from redundant features and noise. The cross-modal self-attention fusion module employs a residual self-attention mechanism to deeply mine and fuse interactions between modalities while retaining low-level features, improving model accuracy and stability. The integrated output module is utilized to obtain more robust prediction results. The results show that the proposed model outperformed unimodal, traditional multimodal, and cross-modal methods on our concrete sand dataset, achieving excellent and robust prediction results for both machine-made sand (RMSE = 0.458, R2 = 0.983, RPD = 7.9) and natural sand (RMSE = 0.705, R2 = 0.984, RPD = 7.931). The detection time was within 71 seconds, significantly enhancing the detection frequency and efficiency, which provides a reliable solution for the rapid detection of MC in concrete sand.