With the increase of bridge-required demolition/dismantling for reconstruction or modification, the early warning of construction emergencies is greatly needed for monitoring the structural safety of bridges under construction. In this paper, based on the multi-source data of nearby construction and demolition construction of a large-span RC arch bridge in China, the Analytic Hierarchy Process (AHP) method is adopted to analyze the multi-source data and set the early warning threshold for bridge safety in construction. According to the analytical results, a reasonable evaluation of AHP factors can improve the accuracy and timeliness of safety early warning in the structural safety monitoring of bridges during the construction process. The weight of the monitoring data in AHP should be assigned according to its reliability, stability, and importance. Bridge safety assessment of nearby construction having harmful vibration should prioritize dynamic bridge monitoring by cooperating with multi-source data, including stress, and deformation monitoring of the bridge is necessary. The assessment results proved that multi-source data, including but not limited to structural stress and deformation monitoring data, vibration data, theoretical prediction data, environment data such as temperature data, and construction/maintenance history data, are necessary for safety monitoring and early warning of construction with specifications related to bridge construction. The early warnings triggered by the evaluation results successfully ensure the safety status of the bridge during nearby construction and demolition construction, which shows the proposed method can provide a guideline for comprehensively evaluating and early warning of the status of bridge construction.

In situ monitoring was conducted throughout the construction period to investigate the abnormal deformation of a bridge under construction subjected to sudden cooling by Typhoon Lekima. An FE model considering the temperature field based on measurement data was also established to reveal the exact causes of the bridge’s abnormal deformation and provide theoretical guidance for rehabilitation measures. The FE model simulation and measurement results showed that (1) the exact cause of the abnormal deformation of the bridge was the inconsistency of the temperature field between the top and bottom plates, and the sudden approach of the typhoon aggravated the inconsistency; (2) the abnormal deformation of the construction bridge caused by the typhoon could be addressed with rehabilitation before forming the bridge; (3) an extreme temperature field should be considered in the design of a beam–arch combination bridge. These results can provide a reference for the design and construction of similar bridges.

Ground vibrations caused by the construction of impact drilling piles may affect adjacent bridges, so corresponding prediction and monitoring are necessary to ensure the safety of bridges. In this study, the construction near an existing long-span double-convex arch bridge is taken as a case study to investigate different layouts of construction machines emanating harmful ground vibrations to the existing adjacent bridge by numerical simulations and monitoring. The peak acceleration and velocity acquired from numerical simulations, and monitoring were successfully used to implement impact drilling pile construction near the existing bridge. The results show that there are good consistencies between the monitoring and numerical simulation results, and the vibration acceleration of the foundation of abutments and piers are mainly derived from the vibration caused by the construction within a distance of 50 m, while the construction beyond 50 m has little effect on the old bridge. The numbers and locations of machines near the same foundation may have a weakening or strengthening effect on the peak acceleration and velocity due to destructive and constructive interference of waves, which can help to control the effects of the vibrations of the existing adjacent bridge.

The construction of long-span continuous steel truss rail-cum-road bridges for high-speed railways presents significant challenges, primarily due to structural complexity, stringent deformation tolerances, and intricate construction sequences. This paper presents a comprehensive construction control methodology developed and implemented for such bridges. Using a real-world bridge project in China as a case study, the methodology integrates mechanical analysis of key construction stages, deformation prediction, real-time monitoring, and adjustment techniques. Furthermore, the application of machine learning (ML) for camber prediction is explored. Key findings indicate that the longitudinal displacement (X-direction) of the top chord at the upper-deck closure segment is highly sensitive to temperature variations, with a differential of about 10–12 mm observed under a 15 °C temperature change. Consequently, closure welding is recommended near the design reference temperature, with field measurements guiding final fit-up adjustments. A comparative analysis between ML predictions and theoretical methods for member elongation revealed that the Extra Trees (ET) model and K-Nearest Neighbors (KNN) model achieved excellent accuracy, with errors within 2 mm, demonstrating the feasibility of ML-based camber setting. The proposed integrated approach, combining finite element analysis, real-time monitoring, and detailed sensitivity analysis of closure accuracy, proves effective in ensuring structural safety and meeting precise alignment requirements, particularly for high-speed railway track. The findings offer valuable insights for the construction control of similar long-span steel truss rail-cum-road bridges.

With the continuous development of science and technology, the state attaches great importance to industry, and the research on materials by researchers is getting more and more in-depth. The steel-wood structure is one of the material structures that has been focused on. The steel-timber composite (STC) structure has the characteristics of good mechanical performance, sustainable attributes, and lightweight. For the steel-timber structure with reinforced layer, its mechanical performance is better, stronger, and more durable. Particle algorithm (PSO for short) is an intelligent search algorithm derived from simulating the foraging behavior of birds. The PSO algorithm uses the information exchange ability of the population to search for the problem to obtain the optimal solution. The purpose of this paper is to study the mechanical properties of steel-timber structural systems with reinforced layers. In this paper, an improved PSO algorithm is proposed, and the algorithm model is used to conduct static tests on steel-timber structures with reinforced layers. The experimental results in this paper show for A and B two different steel-timber specimens with reinforced layers. When the number of reinforced layers of the A specimen is twice that of the B specimen, the stagnation amplitude of the B specimen is twice that of the A specimen. The cumulative energy consumption of A will be relatively more, in every 1 mm of displacement. A specimen is 4.6 kN·mm on average than B specimen, and the damping coefficient of A specimen is 0.028 larger than that of B specimen. This shows that for specimens with more reinforced layers, the higher the ultimate fatigue strength, the stronger the bearing capacity and the better the stability.