#5077. Effective multilayer hybrid classification approach for automatic bridge health assessment on large-scale uncertain data

July 2026publication date
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Journal’s subject area:
Industrial and Manufacturing Engineering;
Information Systems and Management;
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Abstract:
The health level of the bridge is critical to the safety and maintainability of the bridge. In order to better assess the health of the bridge, we adopt a multi-layer hybrid method to iteratively determine the uncertain labels of the target dataset, evaluate the confidence of the large-scale uncertain labels, add high-confidence data to the training set, and correct the low-confidence data. Finally, we get an effective classification model with the optimized training dataset. This paper studies the learning problem of classification model on labeled data with large-scale uncertain labels, and proposes an effective hybrid classification model (HCM), which can establish a supervised classifier under the condition of detecting uncertain labels and realize error label correction. In order to measure the HCM label assignment problem, we introduce a new penalty function, which can evaluate the label consistency problem of two basic classifiers. Experiments conducted on synthetic data, benchmark data and real bridge datasets show that the proposed method is superior to other methods and provides an effective and convenient solution for bridge health assessment.
Keywords:
Bridge health evaluation; Hybrid classification model; Large-scale uncertain label; Penalty function

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