The serviceability of pedestrian bridges may be affected by the discomfort due to vibrations felt by the users crossing the deck. Design guidelines recommend avoiding critical frequency ranges and provide acceptance criteria for maximum accelerations to assess user discomfort for serviceability conditions. The paper presents selected results from an experimental campaign on a two-hinged steel arch pedestrian bridge, analysing its dynamic response to both ambient and human-induced vibrations. Activities such as walking, running, and jumping are investigated. In this field, many research works are available in the scientific literature, but the vibration analysis of a steel arch footbridge with tapered truss cross-section is still missing. The first six vibration modes of the bridge are identified using the Frequency Domain Decomposition technique, while damping ratios are estimated through Enhanced Frequency Domain Decomposition. Walking and running tests reveal a small shift in the bridge's forced response frequencies compared to its free response. Jumping tests are analysed by isolating specific modal responses and estimating modal damping ratios based on free vibration data after the jump. The study also compares the maximum vertical accelerations recorded during these tests with the acceptance limits provided by technical standards. Overall, the paper provides insight into the vibration assessment of a steel arch footbridge with tapered truss cross-section and offers practical indications for field-data interpretation, as well as reference values of natural frequencies, damping ratios and accelerations for this common kind of infrastructures.

This study presents a machine learning-driven comparative analysis to predict frost resistance in rubberized concrete, focusing on the interpretability and performance of three models: Stepwise Linear Regression (SLR), Stepwise Polynomial Regression (SPR), and Classification and Regression Tree (CART). Leveraging historical experimental data, the methodology integrates rigorous preprocessing via Interquartile Range (IQR) outlier detection and tenfold cross-validation to ensure robustness. Key variables, including rubber content (0–20% fine aggregate replacement), water-cement ratio, and freeze–thaw cycles, were analyzed to balance durability and sustainability goals. The CART model offered interpretable decision rules (test R² = 97.01%) identifying critical thresholds like rubber content ≤ 15%, providing a clear guideline for maximizing durability. Comparatively, the study advances prior Artificial Neural Network (ANN)-based approaches by delivering mathematical transparency (SLR, SPR) and actionable insights (CART). These outputs directly guide mix design: the SPR equation enables precise prediction of frost resistance for specific combinations of mix parameters, while the CART rules establish safe application limits. This moves beyond prediction to offer practical tools for optimizing sustainable concrete mixes under frost exposure, prioritizing both accuracy and implementable insight.

The issue of exposed reinforcement, especially in bridges subjected to aggressive environmental conditions, truck impacts, and patch repairs, is an important concern of the civil engineering community due to the increasing number of ageing reinforced concrete infrastructures and the important economic resources directed to bridge repairs. This paper presents a comprehensive review of the current research focused on the structural performance of reinforced concrete beams with exposed or unbonded reinforcement. It evaluates experimental investigations, analytical modelling efforts, and finite element simulations to understand the effects of bond loss on flexural strength, stiffness, ductility, and failure mechanisms. Results from over 20 key studies indicate that flexural strength can be reduced by up to 55% when the length of exposed reinforcement exceeds 80–90% of the beam span particularly in simply supported beams tested under a central point load, and especially in beams with a high reinforcement ratio of approximately 1.5%. In contrast, lightly reinforced beams under similar exposure conditions have shown strength reductions ranging from 0% to 30%, highlighting the importance of reinforcement ratio and test setups. The ductility of beams with 50% exposed length has been reported to decrease by as much as 60%, and stiffness losses were found to be approximately proportional to the exposed length. A comprehensive review of the current literature reveals that in certain configurations where exposed bars remain adequately anchored, the beams maintained an appropriate proportion of their original flexural capacity, suggesting that conservative assumptions in design may lead to unnecessary and costly interventions. The findings emphasise the need for improved predictive models and design guidelines to assess performance and ensure safety during repair and continued service. This systematic review integrates more than twenty key studies, identifies the principal parameters influencing the flexural behaviour of beams with exposed reinforcement, and highlights the current lack of normative provisions, hence offering a consolidated foundation for future experimental research and code development.

The dampers of cable-stayed bridges play a crucial role in bridge seismic resistance. Traditional research requires a large number of trial calculations of damper parameters, and the application of machine learning methods to optimize the seismic performance of dampers in cable-stayed bridges has a great significance. This article is based on the parameter analysis data of dampers for a single tower cable-stayed bridge. Firstly, the advantages and disadvantages of central composite design and comprehensive experimental method were compared and analyzed. Then, the response surface fitting method was optimized using support vector egression. Finally, the optimal damper parameters were studied using particle swarm optimization algorithm. Analysis shows that there is significant nonlinearity in the structural response under earthquake action. The use of support vector machines and particle swarm optimization algorithms can accurately and efficiently fit and optimize damper parameters. From this, it can be concluded that the machine learning method combining support vector machine and particle swarm optimization has good accuracy and applicability in optimizing the seismic performance of cable-stayed bridge dampers, and can be further extended to other research fields.