A deep learning-based approach for assessment of bridge condition through fusion of multi-type inspection data

Abstract

Bridges typically undergo regular inspections to assess their structural conditions. However, relying solely on numerical data overlooks valuable information from other data types, reducing assessment reliability. Although data fusion is an effective solution, existing methods poorly handle defective data scenarios (sparse, imbalance, and loss). To address this issue, this study proposes a novel deep learning-based assessment model, the Bridge Information Fusion Network (BI-FusionNet). The features of the developed BI-FusionNet: (1) Feature extraction and processing layers can be compatibility with processing networks for various data types, extracting and unifying the key features of these data; (2) Innovative fusion technology combining SENet and the random fusion matrix, enabling deep fusion from data types to feature information. Appropriate extraction models mitigate sparse data effects via extracting critical features; the novel feature fusion strategy exploits cross-type information, resolving the imbalanced and missing data issues. The experimental results verified that the BI-FusionNet model achieved an accuracy of 0.9687 in assessing bridge conditions using normal dataset. In the effectiveness test, the model accuracy was 0.8377 by using the defective dataset, outperforming baseline methods. Therefore, the proposed BI-FusionNet can alleviate the issue of performance degradation from defective data and facilitates multi-type inspection data application in bridge condition evaluation.