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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.

Orthogonal experiments were performed to study the flexural strength of an eco-friendly concrete containing fly ash (FA) and ground granulated blast-furnace slag (GGBFS). The effects of different test parameters, such as water-binder ratio (W/B), FA content, GGBFS content, sand ratio, gravel gradation, and curing time, on the flexural strength of the concrete were analyzed. The significance level of each influencing factor and the optimal mixing proportion of the concrete were determined by range analysis and hierarchy analysis. It was found that the W/B ratio had the greatest influence on the flexural strength of the concrete. The flexural strength of the concrete decreased gradually with the increase of W/B. The GGBFS content and the sand ratio had a greater influence in the early stage of concrete curing. The middle and later stages of concrete curing were mainly affected by gravel gradation and the FA content. A flexural strength prediction model of the concrete was developed based on a backpropagation neural network (BPNN) and a support vector machine (SVM) model. It was noticed that the BPNN and SVM models both had higher accuracy than the empirical equation, and the BPNN model was more accurate than the SVM model.