A Comparative Study of a Fully-Connected Artificial Neural Network and a Convolutional Neural Network in Predicting Bridge Maintenance Costs

Profile Change Law of Clad Rebars and the Formation Mechanism of Composite Interfaces during Hot Rolling

Rough- and intermediate-rolled composite billets and finished clad rebars were cut using flying shears. The law of metal rheology and the mechanism of composite interface generation during clad rebar formation were then investigated using metallographic microscopy, electron backscatter diffraction, and scanning electron microscopy. The radial deformation trend of the clad rebars was greater than that of HRB400 rebars and "ears" were more likely to appear during the rolling process. The widths of the decarburization and composite zones and diffusion distances of each element decreased as the cumulative reduction rate increased. Furthermore, as deformation increased, the number of oxides on the composite interface significantly decreased, the proportion of recrystallized grains increased, and the grains became more refined. These changes led to increases in the bond and tensile strengths of the composite interface. According to the research above, the pass filling degree should be within 0.85-0.9 and the cumulative reduction rate should be over 80% when rolling clad rebars.

Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition

This study developed an ensemble-learning-based bridge deck defect condition prediction model to help bridge managers make more rational and informed steel bridge deck maintenance decisions. Using the latest data from the NBI database for 2021, this study first used ADASYN to solve imbalance problems in the data, then built six ensemble learning models (RandomForest, ExtraTree, AdaBoost, GBDT, XGBoost, and LightGBM) and used a grid search method to determine the hyperparameters of the models. The optimal model was finally analyzed using the interpretable machine learning framework, SHAP. The results show that the optimal model is XGBoost, with an accuracy of 0.9495, an AUC of 0.9026, and an F1-Score of 0.9740. The most important factor affecting the condition of steel bridge deck defects is the condition of the bridge’s superstructure. In contrast, the condition of the bridge substructure and the year of bridge construction are relatively minor factors.