Traffic accidents such as vehicle collisions with bridge guardrails occur frequently. These accidents cause damage to the driver and the vehicle as well as the bridge. A new type of assembled anti-collision guardrail is proposed in this study. LS-DYNA is a nonlinear display dynamic analysis software used to evaluate the safety of a new type of assembled anti-collision guardrail. A specific, numerically analyzed model of vehicle–guardrail collision is established using LS-DYNA. The energy distribution–time curve of the vehicle collision process is obtained. After comparison with measured data from the vehicle collision test, the model of vehicle–guardrail collision is verified as being correct. Based on this, we analyze the process of a vehicle collision on the assembled anti-collision guardrail. The result shows that the assembled anti-collision guardrail proposed in this paper can better change the trajectory of a moving vehicle and can prevent the vehicle from falling off the bridge. From the car body collision results, the assembled anti-collision guardrail for bridges proposed in this paper can reduce vehicle damage and can protect the driver effectively. From the analysis of the main girder stress on the bridge, an anti-collision guardrail installed on an existing bridge will not cause damage to the main girder during a collision. In order to study the influence of the four parameters on the anti-collision effect, we carried out a comparative calculation of multiple working conditions. The results show that the new type of assembled anti-collision guardrail has good protective performance under different working conditions.

Epoxy adhesives derived from bisphenol A diglycidyl ether (BADGE) are widely utilized in segmental construction—particularly in precast concrete structures—and in building structural strengthening, owing to their outstanding adhesion properties and long-term durability. These materials constitute a significant class of polymeric adhesives in structural engineering applications. However, BADGE-based epoxy adhesives are susceptible to aging under service conditions, primarily due to environmental stressors such as thermal cycling, oxygen exposure, moisture ingress, ultraviolet radiation, and interaction with corrosive media. These aging processes lead to irreversible physicochemical changes, manifested as degradation of microstructure, mechanical properties, and dynamic mechanical properties to varying degrees, with performance deterioration becoming increasingly significant over time. Notably, for the mechanical properties of concern, the decline can exceed 40% in accelerated aging tests. A comprehensive understanding of the aging behavior of BADGE-based epoxy resin under realistic environmental conditions is essential for predicting long-term performance and ensuring structural safety. This paper provides a critical review of existing studies on the aging behavior of BADGE-based epoxy resins. This paper summarizes the findings of various aging tests involving different influencing factors, identifies the main degradation mechanisms, and evaluates current methods for predicting long-term durability (such as the Arrhenius method, Eyring model, etc.). Furthermore, this review provides recommendations for future research, including investigating multifactorial aging, conducting natural exposure tests, and establishing correlations between laboratory-based accelerated aging and field-exposed conditions. These recommendations aim to advance the understanding of long-term aging mechanisms and enhance the reliability of BADGE-based epoxy resins in structural applications.

To address the fatigue damage issues in continuous beam bridges under vehicle loads, a method using vibration-mixed steel fiber-reinforced concrete to improve critical vulnerable areas of the bridge is proposed, thereby enhancing the bridge’s fatigue resistance. Fatigue performance and micro electron microscopy tests were designed for vibration-mixed steel fiber-reinforced concrete, analyzing its damage conditions and microstructural changes under 0 to 2 million cyclic loads, and the key mechanical parameters of the concrete were determined. Based on this, a numerical analysis model was established to simulate the fatigue damage of continuous beam bridges under moving vehicle loads. The results show that piers made with vibration-mixed steel fiber-reinforced concrete exhibit a 56.86% reduction in compressive damage compared to conventional piers, a reduction in stiffness damage range, and a 29.35% increase in fatigue life.

Survival analysis is a data-driven approach that is widely used in various fields of biomedical prognostic research, and it is highly reliable in the processing of time-event data. This study developed a method for evaluating the service performance of bridge superstructures using the built-in acceleration sensor of smartphones and the prediction of survival analysis theory. It will be used to assist in the daily maintenance and repair of small and medium bridges. The effects of the upper load-bearing structure, upper general structure, bearings, deck paving, expansion joints, and frequency ratio on the deterioration of the bridge superstructure were investigated. The results show that the first-order vibration frequency of the bridge can be effectively detected by the built-in acceleration sensor of the mobile phone, but its low sensitivity and high output noise make it impossible to accurately detect the higher-order frequencies of the bridge. The upper load-bearing members, the upper general structure, the bearing, the deck pavement, and the frequency ratio are all related to the changing trend of the technical condition level of the bridge superstructure.

Aiming to address the shortcomings of existing semi-active control algorithms with poor robustness and the limited generalization ability of current evaluation methods based on deterministic analysis, a novel approach based on probability density evolution is proposed. This method is designed to assess the seismic reliability, enabling a more comprehensive evaluation of the control effectiveness of aqueduct structures. Building upon this, an intelligent semi-active control algorithm leveraging machine learning is introduced. The algorithm is further validated through engineering case studies to investigate semi-active control strategies in response to random seismic events. The results show that the seismic reliability of the machine learning-based semi-active control algorithm is significantly higher than that of the uncontrolled state for the same failure threshold under random seismic effects.

Research on the seismic performance of slopes is of great significance for ensuring the long-term stability of engineering projects, protecting human life and property, and reducing economic losses. Previous studies have shown that when studying slope stability, it is essential to consider the dependence structure among soil parameters. Therefore, in this study on the seismic reliability of reinforced slopes, the Copula function was introduced to represent the dependence structure between soil cohesion and the internal friction angle. In combination with the stochastic ground motion model of engineering physics, soil samples and ground motion samples were generated based on the Generalized F-discrepancy (GF-discrepancy) point selection method, and their assigned probabilities were obtained simultaneously. Subsequently, the dynamic response of the slope under the combined stochastic effects was simulated, and the Generalized Probability Density Evolution Method (GPDEM) was used to quantitatively evaluate the reliability of the reinforced slope structure under different failure thresholds. The results indicate: (1) The folded geogrid reinforced slope demonstrates superior seismic performance; (2) The Frank Copula function can better characterize the dependence structure between the soil parameters selected in this study, providing a sufficient number of effective samples for stochastic research; (3) Based on GPDEM, the structural reliability curves under different thresholds can be obtained rapidly. The research findings have important theoretical implications for the seismic reliability analysis and structural design of reinforced slopes.