The Near-Surface-Mounted (NSM) technique for Fiber-Reinforced Polymer (FRP) strengthening is widely applied in the seismic retrofitting of concrete structures. The key aspect of the NSM technique lies in the adhesive performance between the FRP, adhesive layer, and concrete. In order to accurately predict the bond strength of embedded reinforced NSM FRP–concrete, this study constructs the relationship between the influencing factors of bonding performance and bond strength based on four machine learning (ML) algorithms: Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB). A unified and interpretable prediction method for FRP–concrete interface bond strength based on SHAP values and ML algorithms is proposed. The results indicate that the ML models exhibit good predictive performance, with the R² of the test set ranging from 0.8190 to 0.9621, showing higher accuracy than empirical calculation formulas. Among them, the RF algorithm demonstrates the highest overall accuracy and optimal performance. Additionally, the SHAP (Shapley additional explanations) method quantitatively confirms that the width of the FRP strip has the most significant impact on bond strength. The newly developed hybrid ML model has the potential to become a new choice for accurately assessing the bond strength of NSM FRP strengthening technology.

As one of the core systems of a city, urban rail transit plays a pivotal role in ensuring the safe, rational, and efficient operation of the city. Therefore, it is of great significance to ensure the safe operation of urban rail transit network to improve the operation efficiency and economic level of the city. The prerequisite to ensure the safety of urban rail transit network is whether the risk situation of urban rail transit network can be reasonably and accurately evaluated. In order to evaluate the risk level of urban rail transit network reasonably and accurately, firstly, with full consideration of the characteristics of urban rail transit, the risk evaluation system of urban rail transit network was established in this paper based on the three levels of regional economy, social resources, and rail transit. Secondly, based on the entropy-TOPSIS-coupling coordination model, the single-factor influence and multifactor coupling influence in the index system are calculated and analyzed, respectively; thus the coupling coordination degree of urban rail transit system is obtained, so as to quantitatively evaluate and analyze the risk situation in urban rail transit network. Finally, based on the actual data of Shanghai from 2000 to 2016, the case simulation and analysis are carried out. The results show that the two indicators of regional economy and social resources are more likely to affect the safety state of urban rail transit. At the same time, the safety factor of urban rail transit coupling system is increasing year by year and gradually develops from disorder to order. This is more in line with current urban rail transit condition, demonstrating the rationality and accuracy of the entropy-TOPSIS-coupling coordination model proposed in this study.

For the purpose of determining whether vacant taxis are supposed to use high-occupancy vehicle (HOV) lanes, this paper builds a framework to explore the performances of a network before and after vacant taxis use HOV lanes and to detect the optimal deployment of HOV lanes. We use a variational inequality (VI) to describe travelers’ mode choice following a logit model and the route choice abided by the user equilibrium principle on the network. A simple network is adopted to explore the network performances when vacant taxis are allowed to use/forbidden from using HOV lanes. Then we propose a framework presented by a bi-level model for the optimal deployment of HOV lanes in which the lower level is the variational inequality and the upper level aims at maximization of social welfare. This bi-level programming with a genetic algorithm combined with a surrogate assistant approach is applied to the simple network and a Sioux Falls network to analyze the network performances in the cases allowing/forbidding vacant taxis from using HOV lanes. The results reveal the paradox that social welfare may decrease when allowing vacant taxis to use HOV lanes compared to when forbidding vacant taxis from using HOV lanes. Considering optimal HOV lane deployment, this paradox still exists. However, it does not always happen due to the different parameters, e.g., flag–fall price. Therefore, the qualification of vacant taxis to use HOV lanes depends on the topology, attributes, and travel demand of the network. The results display that the models and the algorithm are effective in reality.