Crack detection on bridges is an important part of assessing whether a bridge is safe for service. The methods using manual inspection and bridge-inspection vehicles have disadvantages, such as low efficiency and affecting road traffic. We have conducted an in-depth study of bridge-crack detection methods and have proposed a bridge crack identification algorithm for Unet, called the CBAM-Unet algorithm. CBAM (Convolutional Block Attention Module) is a lightweight convolutional attention module that combines a channel attention module (CAM) and a spatial attention module (SAM), which use an attention mechanism on a channel and spatially, respectively. CBAM takes into account the characteristics of bridge cracks. When the attention mechanism is used, the ability to express shallow feature information is enhanced, making the identified cracks more complete and accurate. Experimental results show that the algorithm can achieve an accuracy of 92.66% for crack identification. We used Gaussian fuzzy, Otsu and medial skeletonization algorithms to realise the post-processing of an image and obtain a medial skeleton map. A crack feature measurement algorithm based on the skeletonised image is proposed, which completes the measurement of the maximum width and length of the crack with errors of 1–6% and 1–8%, respectively, meeting the detection standard. The bridge crack feature extraction algorithm we present, CBAM-Unet, can effectively complete the crack-identification task, and the obtained image segmentation accuracy and parameter calculation meet the standards and requirements. This method greatly improves detection efficiency and accuracy, reduces detection costs and improves detection efficiency.

In order to study the safety state of the structure of a cross-sea cable-stayed bridge during its operation period, this paper proposes a combined long-term traffic prediction model based on the XGBoost (eXtreme Gradient Boosting) model and LSTM (Long Short Term Memory) model in the context of a cross-sea cable-stayed bridge in Qingdao. XGBoost is an optimized distributed gradient enhancement library. LSTM is a neural network for processing long sequence data. The LSTM model and the XGBoost model were first built separately, and then a genetic algorithm was used to select the optimal weight parameters to combine the two models for prediction. Based on the traffic prediction results of the combined LSTM-XGBoost model, a finite element model was established using numerical analysis. The effect of different traffic volumes on the deflection and stresses in the span of the main beam and the stresses in the diagonal cables was analyzed using the time course analysis method. From the point of view of structural safety, the maximum of future traffic limits and more reasonable average traffic speeds are given to provide a basis for the later management of the bridge.