The second-order blind identification (SOBI) and its variants have been extensively explored for output-only modal identification of civil structures under varied excitations. At the core of these methods is the matrix joint approximate diagonalization (JAD) technique, while their efficiency and accuracy are largely determined by how the target-matrices for JAD are constructed from multi-channel structural responses. This study first formulates the JAD framework for structural identification, where different techniques in formulating the target-matrices are summarized and mathematical tools to conduct JAD are also presented. Then two novel ways stemming from conventional identification methods are presented as alternatives to construct the target-matrices for ambient identification, to maintain a low-order formulation and even avoiding the formation of covariance matrix. Subsequently, in view of the large number of candidate target-matrices which are analytically usable, a guiding principle is proposed for selecting reliable target-matrices, where the closeness of the eigenvectors of the target-matrices are compared beforehand, therefore eliminating of distorted target-matrices and also improving the efficiency of the subsequent JAD. The proposed techniques are applied to modal identification of the Donghai Bridge from monitoring data and the proposed JAD-based methods are compared in this context. The results suggest the effectiveness of the proposed techniques and also provide a performance evaluation of these methods.

The significant load disparity between the two decks of a cable-stayed bridge with separated unequal-width decks results in complex asymmetric static effects in the jointed dual-pylon. To investigate the structural behaviour and reliability of the jointed dual-pylon under asymmetric loads, a 1:30 scaled model test and numerical simulation were conducted based on the world's first road-railway same-level cable-stayed bridge with jointed pylons. The test results indicate when the unbalanced horizontal force of the jointed dual-pylon structure reaches its maximum during the service phase, the stress at the dual-pylon connectivity node remains relatively low, indicating good shear resistance of the dual-pylon connectivity node. Structural failure occurs when the load reaches 1.82 times the maximum shear stress at the tower column merging section, and the railway beam will experience severe cracking and stiffness degradation, ultimately leading to loss of bearing capacity. The calculation results further reveal that under the combined action of dead load, full-span moving load, and lateral wind load, the minimum calculated nonlinear stability coefficient of the dual-pylon connectivity node is 1.65. Moving load and longitudinal wind load have minimal impact on the nonlinear stability coefficient, and the dead load and lateral wind load primarily govern the failure of the dual-pylon connectivity node.

Although the rigid skeleton concrete arch has been widely used in engineering, the research on the failure mode and bearing capacity of this kind of arch is very few, and the previous research on the bearing capacity of arch is mostly concrete arch or concrete filled steel tube arch. To better understand the mechanical properties of rigid skeleton catenary arches, three arch models with a span of 12 m were constructed. The study focuses on the in-plane failure mode and mechanical characteristics of the arches. It analyzes the variation law of the load–displacement curve, the section strain characteristics, the strain behavior of the steel tube and concrete encasement, and the influence of different loading methods and longitudinal reinforcement ratio on the mechanical properties of arch. On this basis, based on the verified ANSYS simulation model, the influence of key parameters such as arch axis coefficient and rise span ratio on the bearing capacity of arch ribs under multi-point loading is analyzed. The load–displacement curve of rigid skeleton concrete arch has experienced three typical stages: elasticity, crack propagation, and yield of reinforcement and steel tube. And the plastic deformation of arch rib is increased in crack stage and steel tube yield stage. Regardless of the load at L/2 or at the quarter point, when the arch rib is damaged, the concrete crack at the loading point develops fully, the concrete is crushed, and the steel tube and reinforcement yield. Compared with the concrete arch without rigid skeleton, the bearing capacity of the arch under L/2 and quarter point loading conditions increases by 45.6% and 43.7%, respectively. The rigid skeleton and concrete can resist external loads together. The contribution of rigid skeleton to the bearing capacity of arch can not be ignored. Under different levels of load, the strain distribution of encased concrete and steel tubes along the section height is coincident. The encased concrete and steel tube of the rigid skeleton arch can work together without sliding. When the arch rib is subjected to multi-point symmetrical loads, the slenderness ratio has the greatest influence on the bearing capacity, followed by the arch axis coefficient. Among them, the slenderness ratio changes from 48 to 90, resulting in a 52.2% reduction in the bearing capacity. When the slenderness ratio changes from 90 to 130, the bearing capacity decreases by 46.4%.

The present work proposes a comprehensive study of the safety evaluation of a reinforced concrete bridge in terms of the actual and expected degradation status. A bridge constructed in the early 1930s over the Cassibile River in Sicily, Italy, is selected for consideration. The study starts with a safety assessment based on the original design codes in use at the time of construction and then follows, by an exegetical approach, the evolution over time of the prescribed loading and design rules, both under the hypothesis of the undamaged and degraded structure. Therefore, the study examines the effects of increased traffic loads with the evolution of the code rules and, specifically, as defined by the Italian Technical Standards (NTC18), on a theoretically undamaged bridge. The results show a reduction in the safety factor across all critical structural components, apart from the lateral beams. These latter beams, in fact, benefit from a more comprehensive consideration of the overall resistance of the section, rather than just the localized stress values. Notably, the transition from the stress-based approach (Allowable Stresses Method, ASM) to a capacity-based evaluation of the full cross-section, as prescribed by NTC18, has minimal impact on elements subjected to purely axial loads, such as the hangers. Overall, the study aims to contribute to the understanding of the expected behaviour of an old structure facing the evolution of acting loads and allows the authors to restate, among other facts, the need to consider the expected degradation phenomena beyond the simple visual findings from surveys.

It is extremely challenging to directly measure the dynamic displacement which is essential in bridge state evaluation. The indirect physical-driven displacement reconstruction methods are restricted by the deviation existing between mechanism model and actual bridge, while indirect data-driven methods are restricted by requirement for a large amount of data. This paper proposes a physics-informed recurrent neural network (PI-RNN) based dynamic displacement reconstruction method. Firstly, the recurrent neural network is established, and the physical equation between data of measured points and target points are derived. Then, the derived physical equation is represented as physical information and added in the loss function of the network. Thus, the loss function contains a physical-based regularization term, which can guide the training direction of the network model, alleviate the over-fitting problem, and improve the generalization ability of the network. Subsequently, the displacement response reconstruction procedure based on PI-RNN is provided in detail. Finally, the effectiveness and superiority of the proposed method are verified by numerical and experimental examples. The results indicate that the PI-RNN is superior to RNN in terms of accuracy and efficiency in reconstruction bridge displacement.

To understand the scouring mechanism around bridges, it is crucial to comprehend the flow pattern in their vicinity. The positioning of the piers along meandering rivers complicates the flow pattern and exacerbates scouring. Therefore, by employing hydraulic structures like submerged vanes to modify the flow pattern around the piers, scouring and further damage can be mitigated. This research investigated the streamlines around one pier and two transverse piers due to the installation of vanes with 25% submergence in a sharp 180° flume channel with a rectangular cross-section. The findings showed that the streamlines in the plan section at the pier locations, from the bed to about 50% of the flow depth at the inlet of the bend, directed towards the inner wall; then, from this level to the water level, the streamlines shifted towards the outer wall. In the plan sections, the maximum positive tangential velocity rose with distance from the bed surface. In the scour hole, radial velocities were negative, while tangential and vertical velocities were positive, resulting in the formation of clockwise vortices in these regions.

Truss structures with obvious periodical repetition in civil engineering are commonly used in long-span bridges and large-span buildings. Based on phononic crystal theory, periodical repetition structures have the potential to develop elastic wave bandgaps, which conduct noise attenuation or novel nondestructive testing. However, there needs to be more current knowledge about the engineering truss structures bandgaps and their potential applications. This study investigates the elastic wave bandgaps and transmission characteristics of two typical engineering truss structures analytically and numerically. We first decoupled the unit cells from the large truss structures. Then, we proposed a standard analytical model to get the dispersion relationship of the truss structures. The analytical dispersion relationship is verified by the finite element method. The results show that truss structure A could generate a bandgap from 1032 to 2065 Hz, while truss structure B could generate bandgaps from 982 to 1980 Hz. The mode analysis further reveals that the bandgap mechanism is Bragg scattering rather than local resonator. We verified the elastic wave transmission characteristics through frequency domain analysis, which agrees well with the bandgaps. To exhibit how the bandgap of the truss structures conducts noise attenuation and potential applications in nondestructive testing, we employ two case studies to illustrate the propagation of noise waves and the novel nondestructive testing for periodical truss structures. The results show that the two truss structures could attenuate noise waves. Defects in truss structures could conduct abnormal transmission, which could be applied in novel nondestructive testing.

This study investigates the longitudinal (namely along the bridge length direction) seismic performance of a novel prefabricated concrete-filled steel tube (CFST) bridge pier. The novel CFST pier, constructed using prefabricated components and assembly joints, ensures a simple and efficient construction method along with significant cost benefits. Previous research indicates that CFST piers exhibit superior lateral seismic performance compared to conventional RC piers, with lower sensitivity to seismic fragility. However, the longitudinal seismic response of CFST piers under varying pier heights and connection types remains to be clarified. This study designs 14 continuous girder bridge cases, including six conventional RC pier cases and eight corresponding CFST pier cases. Through dynamic and seismic fragility analyses, the longitudinal seismic responses of the novel CFST piers are investigated. The results demonstrate that the longitudinal maximum displacement and curvature ratio of CFST piers are comparable to, or even lower than, those of conventional RC piers, especially when integrated with a rigid beam-pier connection system. Additionally, CFST piers demonstrate lower seismic fragility in the longitudinal direction. An assessment method integrating both curvature ratio and maximum drift is recommended for these CFST piers.

In Western China, the mountains are high, the canyons are deep, and the wind field features are complex. For the large span bridge in reservoir area, coupled with the large fluctuation of water level, the complexity of wind field of bridge site is further aggravated. The numerical wind tunnel can be used to investigate the effect of water level change on the mountain wind field. However, due to the scale effect, man-made cliffs and the validity of verification, the analysis accuracy remains to be verified. Taking a long-span bridge across a reservoir in a mountainous area as the engineering background, this study systematically compares the influence of transition curve and terrain range, verifies the rationality of numerical simulation through field measurement, and further explores the influence of reservoir filling on the average wind characteristics of the bridge site. The results show that the wind characteristics are strongly affected by the local terrain, the wind direction is basically the same as the strike of the gully, the bridge location is affected by the contraction of the section, and the canyon wind and effect of negative attack angle are significant. After impounding the bridge site, the wind characteristics change obviously, and the canyon wind effect and effect of negative attack angle weaken obviously.

Energy-based seismic design is an innovative approach that systematically incorporates energy-related demands of ground motion to analyze and design structures, particularly in near-field regions. This study investigates the seismic behavior of three multi-span continuous concrete box-girder (MSCC-BG) bridges subjected to 328 ground motions, including pulse-like and non-pulse records, using the OpenSees framework. Twenty-eight energy-related, residual, and displacement-based demands and thirty-six intensity measures (IMs) from horizontal and vertical earthquake components are analyzed. Key correlations between these demands and various IMs are identified, focusing on the most critical demands under pulse-like earthquakes. A multi-variable probabilistic seismic demand model (PSDM) is developed using Lasso and stepwise regression for critical demands, such as column hysteretic energy and residual drift ratio. While the multi-variable PSDM demonstrates improved prediction accuracy compared to single-IM models, the improvement for the examined demands is modest. These findings highlight the importance of incorporating horizontal and vertical ground motion IMs in PSDMs to enhance predictive accuracy and provide a foundation for further refinement in energy-based seismic design methodologies.

The operation safety and stability of trains is closely related with the wind speed. However, given the intricate nature of its characteristics, which encompass linearity, nonlinearity, nonstationarity etc., accurately predicting the short-term wind speed presents a notable obstacle. To this end, this paper presents a novel forecasting approach using the hybrid of enhanced variational mode decomposition (EVMD), auto-regressive integrated moving average (ARIMA), fully convolutional neural network (FCN), and physical auxiliary mechanism (PAM). This method not only can provide the accurately deterministic prediction, but also can produce the desired probabilistic prediction. Specifically, EVMD is developed based the mode aliasing problem for performing the data decomposition and reconstruction. Then, the combination of ARIMA and FCN is used to perform linear and nonlinear predictions. Finally, PAM is introduced into the above established model for realizing the desired deterministic and probabilistic predictions where the relationship among the wind speed data recorded at various time intervals and the data variability are considered. Numerical examples, utilizing two sets of measured wind speed data, underscore the efficacy and advantage of the developed method. For example, the proposed method can realize the reduction of the average of mean absolute error from 1.08 to 0.73 in comparison with ARIMA-FCN-PAM. Hence, the proposed method stands as a viable and efficient alternative for forecasting the short-term wind speed.

Scour around complex bridge piers (CBP) caused by sediment erosion due to steady flow is a critical challenge in hydraulic engineering, often leading to structural instabilities and failures. The accurate estimation of maximum scour depth is crucial for ensuring bridge safety and optimizing design. Traditional empirical methods and physics-based models, while widely utilized, often struggle to capture the complex interactions between hydrodynamic forces, sediment transport, and varying pier geometries, leading to conservative or inaccurate predictions. This study presents a one-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) deep learning models for predicting the maximum scour depth around CBP under steady current conditions in a clear-water environment. The proposed model leverages the ability of 1D CNNs to process high-dimensional input dataset, capturing intricate non-linear relationships between influential parameters, such as flow velocity, pier configurations, sediment properties, and water depth. The dataset was transformed into non-dimensional forms using the Buckingham Pi theorem to enhance model generalization. The 1D CNN model was trained and validated using an extensive dataset, and its performance was benchmarked against established empirical models, including FDOT, HEC-18, and Coleman’s equation. Results show that the proposed 1D CNN model significantly outperforms traditional approaches, achieving higher coefficient of determination (R ² = 0.85) values and lower root mean squared error (RMSE = 0.1125), mean absolute error (MAE = 0.1078), and scatter index (SI = 0.1149). Moreover, the model's bias (B = -0.0194) and standard error (SE = 0.1147) remain minimal across unseen datasets, demonstrating robust predictive capability. This research highlights the potential of deep learning as a reliable and precise tool for scour depth prediction, contributing to improved risk assessment and sustainable bridge design under steady flow environments.

Concret-filled-steel-tube arch bridges often employ solid trial-assembly for the arch ribs to confirm matching accuracy and overall alignment. However, these methods often suffer from issues such as large site occupation, multiple assembly cycles, and prolonged construction periods. This paper proposes a virtual trial-assembly technology of steel-pipe-arch ribs based on limited perception, which achieves rapid virtual trial-assembly without the need for physical segment matching. By obtaining joint control point data through limited measurement perception, the method virtually assembles the control points according to the theoretical manufacturing configuration. It extracts the flange position parameters between the arch rib segments under the ideal configuration condition, ultimately guiding the adjustment and installation of the flanges. Additionally, a self-holding device for steel structure joints is designed, which achieves precise positioning and reliable installation of flanges through parameterized adjustment. A virtual trial-assembly experiment of the steel pipe arch rib joint was conducted using the proposed method. The results of the experiment indicate that the method has high control precision and good technical performance. It has overcome the technical barriers to the application of virtual trial-assembly technology in the construction process and has good potential for promotion and application in similar bridge types.

To address the urgent need for rapid traffic restoration after bridge collapse, a novel lightweight fabricated GFRP (Glass Fiber-Reinforced Polymer) emergency bridge with a broken-line prestressed cable system was developed. Full-scale four-point bending tests and initial deformation measurement tests caused by dead load and clearance effect were conducted to determine the flexural deformation of the bridge. It was demonstrated that the broken-line prestressed cable system substantially enhances the structural stiffness while maintaining the advantages of modular assembly. The experimental results revealed that SLYP (Single Lug and Yoke Plate) joints serve as critical load transfer components, and the deformation caused by the clearance effect of SLYP joints cannot be ignored. The calculation method for the equivalent flexural stiffness, distinguishing GTAL (GFRP tube and aluminum alloy deck) part and SLYP joint part, was given. The flexural deformation caused by dead load, live load, prestressing, and clearance effect, considering the axial deformation and spatial angle reduction effect of the steel wire cables, was proposed based on the flexibility method. The validated analytical model exhibited excellent agreement with experimental data. The main parameters influencing the flexural deformation, such as the equivalent flexural stiffness, clearance between the pin and pinhole, height of the segment, length of the vertical stay and turning component, and SLYP joint arrangement, were discussed in detail based on the proposed method.

Coastal bridges play a vital role in supporting transportation and economic activities in coastal regions but are increasingly vulnerable to extreme wave events, exacerbated by climate change and rising sea levels. Traditional engineering solutions, such as seawalls and breakwaters, offer protection but are often expensive and environmentally disruptive. Vegetated coastlines, such as mangroves and salt marshes, have emerged as a sustainable alternative, capable of attenuating wave energy and providing natural protection. However, the mechanisms through which vegetation reduces wave impacts and its practical application to protect coastal bridges remain inadequately understood. This study addresses these gaps through laboratory experiments that investigate the attenuation effects of vegetation on extreme waves under varying initial wave states and vegetation densities. The experimental data are used to perform stochastic analyses to quantify bridge vulnerability under extreme wave scenarios, with and without vegetation protection. The paper presents the experimental design, methods for estimating wave-induced loads on bridge superstructures, and a probabilistic vulnerability model to assess bridge performance. Comparative results highlight the effectiveness of vegetation in mitigating wave loads and reducing bridge vulnerability. Findings from this study could contribute to advancing sustainable coastal protection strategies and provide critical insights for integrating vegetation-based solutions into coastal bridge design and management practices.

Structural health monitoring (SHM) apparatuses rely on continuous measurement and analysis to assess the safety condition of a target system. However, in field applications, the SHM framework is often hampered by practical issues. Among them, missing data in recorded time series is arguably the most common and most disruptive challenge that can arise. Therefore, imputing missing values is necessary to maintain the integrity and utility of the SHM data. This research work investigates the use of Gaussian Process Regression (GPR) for imputing missing data in ordered time series. In particular, this approach is here proposed and tested for Vibration-Based Monitoring (VBM) and ambient monitoring, with applications to modal parameters and air temperature. Both punctual missing-at-random (MAR) and prolonged missing-not-at-random (MNAR) gaps in the time histories of recorded natural frequencies are analysed. The performance of the proposed GPR-based approach is evaluated on real-life data from field tests on a well-known case study, the KW51 rail bridge. The method is first tested to actual missing values in the dataset. Then, the accuracy is tested using artificially removed data, and the imputed values are compared to the ground truth (i.e., the actual measured data). In the first case, the results show that the complete time series are deemed qualitatively similar to what would be expected by an expert user. The outcomes of the second part quantitatively demonstrate that GPR can accurately impute missing data in modal parameter time series, preserving the statistical properties of the data.

Carbon fibre reinforced polymer (CFRP) are widely used in bridge reinforcement projects. However, delamination at the CFRP-concrete interface caused by frequent fires significantly impacts structural safety, severely restricting the further and extensive development of CFRP in bridge engineering. In this paper, the sand filling method is used to quantitatively evaluate the roughness of the concrete beam's surface, and the interfacial normal and tangential bonding stresses between CFRPs and concrete after exposure to elevated temperatures were investigated. The strength grade of the concrete, concrete surface roughness and temperature were analysed to explore the behaviour of the CFRP composites. First, before the CFRP sheets were pasted, the concrete interfacial roughness was quantitatively evaluated, and 135 CFRP-concrete interfacial bonding tests were carried out. Then, two bonding models based on an elevated temperature field were proposed. Finally, the interfacial bonding failure mechanism was analysed by scanning electron microscope (SEM). The research results showed that the concrete surface roughness more significantly affects the interfacial bonding stress than does the strength grade of the concrete. The interfacial separation between CFRPs and epoxy resin occurs at 110 °C, and the glass transition temperature (T_g) is the critical factor determining the decrease in the bonding performance of CFRP composites. The two models proposed in this study exhibit high prediction accuracy and certain safety reserves and are applicable to the prediction of CFRP reinforcement design and construction after exposure to high temperatures. These models also have additional potential applications.

The fatigue performance of rigid pavements on steel bridge decks remains an underexplored area, with most existing research focusing on flexible pavement systems and simplified macroscopic models. This study presents a refined mesoscale numerical framework for analyzing fatigue crack propagation in steel fiber reinforced concrete (SFRC) pavements using fracture mechanics and the extended finite element method (XFEM). A three-dimensional local model of an SFRC-orthotropic steel deck system was developed, incorporating moving load simulations to determine critical stress locations. Parameters such as steel fiber volume content, yield strength, and aspect ratio were systematically varied to evaluate their effects on crack propagation behavior and fatigue life. Model predictions were validated against experimental fatigue test results, showing strong agreement in crack path and fatigue life estimates. The findings indicate that increasing steel fiber content from 0.5% to 2.0% progressively enhances fatigue resistance, with simulated fatigue life improvements of 51%, 28%, and 20% over the 0.5%-1.0%, 1.0%-1.5%, and 1.5%-2.0% intervals, respectively, while higher fiber strength and optimized aspect ratios further improve performance. The proposed methodology provides a reliable tool for optimizing SFRC pavement design and offers practical guidance for extending the fatigue life of steel bridge decks.

Atmospheric corrosion is one of the major factors leading to the deterioration of steel truss bridges. In order to overcome this problem, protective coatings are generally applied to steel members. Since coatings also can deteriorate over time, investigating the time-dependent structural performance becomes essential. In this paper, a method was developed for the time-dependent advanced analysis of steel truss bridges with and without coatings. To achieve this goal, material and geometric nonlinearity were accounted for evaluating the structural load capacity of the bridges, and consequently, the whole structural behavior was monitored. The effect of atmospheric corrosion was modelled using a relationship based on ISO 9224 for members without coating. Besides, two different coating degradation models were applied for the members with coating. For the evaluation of the proposed method, four steel truss bridges from the literature were accounted and time-dependent structural load capacities were calculated under atmospheric corrosion exposure for 100 years. In order to represent the effects of different environments, each steel truss bridge type was assumed to be built in different countries, and atmospheric corrosion data for each case was acquired from international corrosion databases. Numerical analysis results revealed the importance of detrimental atmospheric corrosion effects in terms of structural load capacity in steel truss bridges. In some cases, reductions in structural capacities were significant and even unexpected failures occurred in aggressive environments. Besides, when coatings were applied to steel truss bridges under atmospheric corrosion, a satisfactory delay in the decrease in load-carrying capacity was achieved throughout the structural lifetime.

Wind-induced vibration (WIV) is an essential factor in the safety and serviceability of long-span bridges, and aerodynamic and mechanical methods are two main countermeasures for WIV control of bridge girders. This paper presents a systematic review of the WIV control of bridge girders, focusing on the technical progress and application prospects. Firstly, the research on aerodynamic and mechanical methods for WIV control is reviewed from the perspectives of passive, active, and semi-active control, and the characteristics, limitations, and application conditions of each technology are summarized. Then, the recent advances in optimal design methodologies for WIV control systems are reviewed. Some perspectives for future research and application on WIV control in bridge engineering are also presented. It is expected that the review can facilitate further research and the practical application of WIV control technologies.

With the progress of construction technology and the application of high-performance materials, arch bridges are constantly breaking the span records. This study conducts parametric design and numerical analysis on upper-support thrust-bearing concrete arch bridges (UTCAB) with spans ranging from 450 to 2000 m, utilizing concrete of different strengths to explore the feasibility limits of spans. Through parameter sensitivity analysis, the study determines the reasonable parametric design of UTCAB with different spans. The results of static wind response analysis indicate that as the span increases, wind load gradually becomes the control load, but after comprehensive consideration, it is unnecessary to install installing tuyeres on the main arch to reduce the wind load. Ultimate bearing capacity analysis is conducted, and the results confirms that all parametric designs meet the requirements. Research on the impact of nonlinearity reveals that material nonlinearity has a much greater impact on ultimate bearing capacity than geometric nonlinearity. Considering the construction feasibility, the recommended feasible maximum span is 1200 m. This study can provide valuable reference for the future design of super long span upper-support thrust-bearing concrete arch bridges.

Two ultra-long-span cable-stayed bridge schemes, identical in deck configuration and main span length of 1500 m but differ in steel and carbon fiber reinforced polymer (CFRP) stay cables, are first designed based on the equivalent strength principle. Finite element analyses are then conducted to investigate wind-resistant performances of both schemes, including static structural behaviors, mean wind-induced deflections, buffeting responses, flutter instability, wind-induced cable resonance, vortex-induced cable vibrations and wind-induced local bending deformations of the stay cables. The results indicate that the using of CFRP cables reduces significantly the wind loads, which account for a major part in the total wind loads developed on the entire structure. As a result, mean wind-induced global deflections of CFRP scheme are notably reduced compared with the steel scheme. In terms of buffeting, results of the CFRP scheme are 12%, 14%, and 28% lower than those of the steel one in vertical, torsional, and lateral directions, respectively. No substantial difference is observed between the two schemes regarding the bridge deck flutter stability. As far as wind-induced cable resonance is concerned, the CFRP scheme is obviously superior to the steel one, exhibiting a much lower likelihood of buffeting-induced resonances due to much higher natural frequencies of stay-cables. As far as the vortex-induced vibration is concerned, however, CFRP stay-cables are less favorable than steel ones. Finally, aiming at the inherent shortcoming of CFRP cables, wind-induced bending deformations at anchorage ends are analyzed. The results show bending angles of the CFRP cables are significantly lower than those of the steel cables. With a wind speed as high as 52.97 m/s considered, CFRP stay cables experience only low-to-moderate bending angles, resulting in no significant strength reductions and posing no substantial threat to the structural safety.

This paper presents a novel artificial intelligence (AI) and Internet of Things (IoT) framework for structural health monitoring (SHM) of masonry bridges. The system utilises the Single Shot MultiBox Detector (SSD) MobileNetV2 model within the TensorFlow Object Detection API to automatically detect critical defects such as spalling, section loss, missing masonry units, and open joints. The model achieved a mean Average Precision (mAP) of 87.4% and an F1-score of 0.89, demonstrating its reliable performance in classifying and localising defects. Through detailed analysis using TensorBoard, the study demonstrates the reliable performance of the model in classifying and localising defects, enabling timely maintenance interventions. By automating defect detection and data analysis, this approach improves monitoring efficiency, reduces operational costs, and improves safety compared to traditional manual inspections. The paper also discusses the potential for future optimisation and real-world deployment to support sustainable management of masonry bridge infrastructure.

Rockfall hazards pose a severe threat to the safety of mountain bridges. As one of the predominant bridge types in such regions, hollow thin-walled rigid-frame bridges still lack sufficient research on their impact resistance mechanisms against rockfalls. To address this, this study establishes a refined finite element model using LS-DYNA to systematically investigate the dynamic response and damage mechanisms of these bridges under rockfall impact. Numerical analyses reveal that: (1) For a given impact energy, increasing the rockfall diameter significantly amplifies pier displacements, with shear damage becoming more severe when the impact location is closer to the pier base; (2) Higher reinforcement ratios can localize damage to the front wall, reducing side panel shear failure risks; (3) Decreasing stirrup spacing effectively reduces concrete damage zones; (4) A power-law relationship model (R2 = 0.94) between peak impact force and impact energy is proposed based on numerical results. The findings provide theoretical support for the impact-resistant design of mountain bridges.

Current constitutive models of shape memory alloys (SMAs) used in structural seismic design are predominantly derived from quasi-static experimental tests. However, as a thermomechanically responsive material, the stress–strain behavior of SMAs exhibits significant temperature dependence. During seismic events, SMAs experience rapid dynamic loading conditions, resulting in substantial deviations between their actual in-service stress–strain response and the predictions based on conventional experimental analysis. This study investigates the thermomechanical behavior of SMA cables through differential scanning calorimetry (DSC) to determine their phase transformation temperatures and employs an MTS universal testing machine to characterize their stress–strain relationships under varying loading frequencies. By incorporating theoretical analysis based on martensite fraction evolution, the research establishes the phase transformation stresses corresponding to different loading frequencies. Finally, a comprehensive case study was conducted on a typical bridge structure to assess the impact of thermomechanical effects during seismic events. The analysis revealed that neglecting these thermomechanical considerations leads to significant underestimation of forces in substructures, potentially compromising the structural safety and integrity of bridge systems.

The construction deviations of super-long span bridges in high-speed railways during the construction phase directly affect the track smoothness after bridge completion, thereby impacting the operational quality of high-speed trains. This study analyzes the main influencing factors of smoothness control in super-long span bridge construction based on their technical characteristics, proposes a construction-phase bridge smoothness control strategy combining segmental assembly control with holistic assessment and adjustment, and investigates smoothness control methods during segmental assembly using the virtual chord measurement method, supported by case studies. The research demonstrates that implementing smoothness control during construction is essential to ensure post-completion track smoothness of super-long span bridges. The proposed strategy effectively translates smoothness control objectives into key construction phases. The virtual chord measurement method proves highly operable and effective for segmental assembly smoothness control. Post-completion multidimensional evaluations using chord measurement and other techniques can guide track smoothness adjustments. Conducting construction-phase smoothness control for super-long span bridges lays the foundation for achieving high track smoothness objectives on high-speed railway bridges.

This study aims at exploring the seismic response of high-speed railway bridges equipped with tuned mass dampers (TMDs) by evaluating their fragility curves. To achieve this, a benchmark Chinese bridge with single-column concrete piers and concrete T-beam decks served as the case study. Different sources of uncertainties were considered in fragility analysis including site hazard characteristics, structural geometries, and material properties. A total of 1800 non-linear time-history analyses were performed on the bridge with or without TMDs in three different site classes, each subjected to 20 sets of ground motions. The simulations were performed using the open-source finite element framework OpenSees. The results showed that at site class B, the median fragility of columns was reduced by 42% at the extensive level when TMDs were employed, compared to the non-TMD case. Similarly, deck displacement showed a significant reduction, especially at site classes C and D, with 21% and 15% reduction at moderate levels, respectively. Generally, the results suggested that TMDs can effectively reduce engineering demands, particularly for single-column piers. Moreover, the rate of reduction in responses differed among components based on their application.

This study investigates six types of prediction methods for estimating extreme bridge traffic load effects, aiming to establish a correlation between prediction accuracy and data quality. Accurately determining the distribution functions of maximum values is crucial for assessing bridge safety under traffic loads. Methods including the Peaks Over Threshold, the block maxima approach, fitting to a Normal distribution, and the Rice formula based level crossing method, are investigated. Additionally, Bayesian Updating and Predictive Likelihood techniques, integrated with the block maxima approach, are explored. The performance of these methods is assessed using two distinct datasets. The first dataset is generated from a known distribution, allowing the estimated distribution parameters and extreme values derived from each method to be compared with the true values. The analysis is then extended to more realistic scenarios, where long-run simulations provide benchmark results for evaluating the accuracy of each method. Based on the findings, recommendations are provided for selecting the most suitable prediction method, considering factors such as sample size, time interval, and the type of load effect. This work offers practical insights for improving the reliability of extreme value prediction methods in bridge safety assessments.

This study presents a comprehensive comparative analysis of two stochastic system identification methods—SSI_data and SSI_cov—for extracting flutter derivatives (FDs) of a trussed deck suspension bridge section under turbulent wind conditions. Numerical simulations and wind tunnel experiments were conducted to evaluate each method's robustness to noise, identification accuracy, and computational efficiency. Results show that while both methods reliably estimate natural frequencies, SSI_data achieves greater precision in damping ratio identification and offers a significant computational advantage—reducing runtime compared to SSI_cov. A detailed signal-to-noise ratio (SNR) analysis reveals that SSI_data excels in vertical mode identification, while SSI_cov is more resilient in estimating torsional damping under low SNR conditions. Experimental validation confirms that the critical flutter wind speeds estimated using both methods under turbulent flow conditions closely match the results observed in wind tunnel tests. These findings provide practical guidance for selecting suitable identification strategies and underscore the potential of SSI methods—particularly SSI_data—for real-time aeroelastic analysis of long-span bridges operating in complex, turbulence-prone wind environments.