Steel Bridge Research Articles

Optimization of the One-Size-Fits-All Layout Problem Based on Preparing Material for Steel Bridges

Before the construction of a bridge begins, workers arrange the necessary parts and then cut and process them. The quality of the cutting layout directly affects the material utilization rate and the efficiency of the subsequent processes. During bridge construction, an intelligent part layout can improve work efficiency, save time, and reduce the labor intensity and production costs for the company. In this study, we studied a layout optimization algorithm, focusing on rectangular parts in the material preparation process. A mathematical model for the rectangular layout problem was constructed, and a hybrid genetic whale optimization algorithm is proposed that is a combination of the whale optimization algorithm and the genetic algorithm. Based on the “one size fits all” layout strategy, the materials are divided into strips, which are further divided into stacks, serving as the positioning strategy to determine the positional relationships of the parts. Test cases and actual engineering data were used to compare the layouts generated using different algorithms. The results show that the genetic whale algorithm proposed in this paper results in a high utilization rate and is highly effective.

Structural Condition Assessment of Steel Anchorage Using Convolutional Neural Networks and Admittance Response

Structural damage in the steel bridge anchorage, if not diagnosed early, could pose a severe risk of structural collapse. Previous studies have mainly focused on diagnosing prestress loss as a specific type of damage. This study is among the first for the automated identification of multiple types of anchorage damage, including strand damage and bearing plate damage, using deep learning combined with the EMA (electromechanical admittance) technique. The proposed approach employs the 1D CNN (one-dimensional convolutional neural network) algorithm to autonomously learn optimal features from the raw EMA data without complex transformations. The proposed approach is validated using the raw EMA response of a steel bridge anchorage specimen, which contains substantial nonlinearities in damage characteristics. A K-fold cross-validation approach is used to secure a rigorous performance evaluation and generalization across different scenarios. The method demonstrates superior performance compared to established 1D CNN models in assessing multiple damage types in the anchorage specimen, offering a potential alternative paradigm for data-driven damage identification in steel bridge anchorages.

Numerical investigation on the hot spot stress at fatigue-prone details of UHPC reinforced orthotropic steel bridge decks

Numerical investigation on the hot spot stress at fatigue-prone details of UHPC reinforced orthotropic steel bridge decks

Investigating Oxide Film Degradation at Arkansas Weathering Steel Bridges

Investigating Oxide Film Degradation at Arkansas Weathering Steel Bridges

Numerical assessment of the dynamic load allowance on long-span modular steel bridges considering vehicle-bridge interaction

Numerical assessment of the dynamic load allowance on long-span modular steel bridges considering vehicle-bridge interaction

Automated Defect Recognition Using Spline Interpolation

Existing infrastructure systems are aged and deteriorated rapidly, state agencies started searching for more advanced way to maintain their valuable assets to the acceptable level. One of them is the application of digital image processing. This problem proposes a digital image processing method to assess a steel bridge coating surface. The image processing method was developed based on B-Spline interpolation and can be used to recognizing the existence of bridge coating rust defects. An automated defect recognition method can make a decision whether a given digitized image contains defects by image comparison. Key Words:B-Spline interpolation, Digital Image Processing, Automated defect recognition method, Image comparision

A visual inspection and diagnosis system for bridge rivets based on a convolutional neural network

AbstractRivets are critical mechanical fasteners in steel bridges, and rivet defects may cause catastrophic failure. This study proposes a convolutional neural network (CNN)‐based inspection system for fast rivet identification and diagnosis. Rivet states are classified as normal, rusted, loose, and missing. A CNN‐based training workflow was introduced to develop a reliable rivet diagnosis system. A multiscale moving window searching technique was proposed to solve the challenge of small rivet identification. A continuous dataset enrichment strategy was applied, which improves training efficiency and minimizes training time. The model performance was assessed based on a historical bridge in Gjerstad. The proposed multiscale moving window searching technique significantly enhances the rivet identification rate to 96.3%. The classification accuracy and model robustness were evaluated, and conditions leading to unidentified rivets were discussed and summarized.

Influence of welding parameters on the formation of U-rib fillet welds in single-sided welding of steel bridge panels

It is difficult to reach the requirement of 80% U-rib weld penetration in the practical application of single-sided welding of steel bridge panels. Meanwhile, full penetration has to be avoided. In this study, efforts are made in order to achieve fillet welds with 80% and above penetration consistently. The effect of welding parameters on the formation of single-sided U-rib fillet welds is investigated. This work has revealed that U-rib fillet weld penetration is presenting an upward trend as the welding current increases under specific welding conditions, and full penetration was observed at the current of 325 A. With the increasing distance between the filler wire and the root of the weld, the U-rib penetration reduces. As the torch angle increases during ship position welding, the U-rib penetration will first increase and then decrease. The weld formation of flat position welding is more stable and the U-rib weld penetration has been significantly improved. The optimum U-rib weld with penetration of 84.8% is achieved under the conditions of flat position welding with 285 A welding current, 44∘ torch angle, and 2[Formula: see text]mm distance between the wire and the root.

Analysis of Pedestrian Steel Bridge Subjected the Seismic Load And Wind Load Using Damper at Different Span

A Pedestrian steel truss bridge model was created by using structural analysis by software. With the similar loading and support condition the 3D model was analyzed for different span like 20m, 25m,30m the basic emphasis has been given decreases the response of structure member by providing damper at different span I section Girder, steel plate thickness. Steel girder and M40 grade of concrete is used for column section in Etab software. The dynamic loading caused by moving pedestrian and earthquake and wind load, Excessive vibration in footbridges usually occurs when they are subjected to rhythmic dynamic loads caused by human activities like walking and running. In order to mitigate the human induced vibrations in footbridges, an increase in the damping ratio is often suggested as the most economical approach. Tuned mass dampers have been widely used for this purpose, however, their installation and maintenance costs are high. This has encouraged researchers to examine the efficiency of the other types of dampers. Viscoelastic dampers have been successfully employed for vibration mitigation of structures against wind and earthquake loads.

Seismic performance of slab-on-girder steel bridges equipped with semi-connected endplate diaphragm

In the steel plate end diaphragm, welding the infill plate to the bearing stiffeners impose local damage on the main girders and their bearing stiffeners. To address this deficiency, a semi-connected-endplate diaphragm is proposed with the idea of semi-supported steel plate shear walls. To identify slab-connected steel plates with the maximum capacity for reducing seismic demand in composite steel/concrete bridges, 36 slab-connected steel plates with different thicknesses, lengths, and material properties were studied. Protecting the main girders from damage and reducing seismic load transmitted from the superstructure to the substructure were two main criteria to identify appropriate diaphragms. These criteria were quantified via seismic parameters such as ductility ratio, force reduction factors, and overstrength factor. The results showed that thin steel plates with small lengths cannot protect the main girders and also thick plates with large lengths transmit the whole seismic load from the superstructure to the substructure. In the next section of this study, the energy dissipation capacity of the optimised diaphragms was studied through cyclic analysis. The result of the time history analysis indicated that an appropriate end diaphragm could considerably mitigate the substructure seismic demand.

Digital fatigue cracking test and fatigue life assessment of rib-deck joints in orthotropic steel decks

The orthotropic steel decks (OSDs) are vulnerable to fatigue fractures, which will inevitably impair the normal service of the steel bridge panels. To determine the impact of the initial cracks on the fatigue life, this study constructed an evaluation model of the OSD rib-to-deck joints crack based on the fracture mechanics principle. And combined it with the multi-scale finite element analysis model, the digital fatigue test model of the whole bridge was established. Research shows: A typical I-II-III composite crack with the supremacy of type I crack can be seen in the OSD rib-to-deck joints. Fatigue cracks developed at the deck weld toe dominate the fatigue failure mode. Further research revealed that as the depth of cracks increased, the stress on the cross-section was redistributed. The intensity factor then displayed a trend of growing and then gradually flattening or even dropping. Based on the relationship between cracks of different depths and the remaining life of the OSDs, recommendations are given for bridge maintenance. The multi-scale digital fatigue test can provide analysis and simulation methods for the fatigue crack propagation in the steel bridge deck of the bridge in operation.

Fire effect of steel bridge structure with consideration of leaked combustion fuel on asphalt bridge deck

In this paper, a fire load model is introduced for steel bridge decks considering asphalt pavement layers, aimed at exploring the consequences of fire arising from fuel leakage on the bridge pavement. Temperature data, mass loss rates, and radiation measurements from the pavement during fire tests are collected. The fire process on the bridge deck is categorized into four distinct stages, based on the mass loss rate. The temperature-time model is constructed by fitting the temperature data from the first two stages on the top surface. This model is then validated by comparing the temperature data obtained from various temperature-time load inputs with the experimental data from the middle and bottom surfaces. Additionally, a finite element numerical model is developed for a steel box girder bridge, enabling a comparison of different temperature-time models to establish the rationality of the bridge deck temperature-time model. Finally, the thermal mechanical coupling method is employed to investigate the impact of fire on the bridge's mid-span, revealing a close alignment between the simulation temperatures derived from the bridge deck temperature-time curve and the experimental results.

Study on the performances of epoxy asphalt binders influenced by the dosage of epoxy resin and its application to steel bridge deck pavement

The concentration of epoxy resin can outstandingly affect the performance of hot-mixed epoxy asphalt binders. A standard measuring procedure of tensile performance for plastics is applied to investigate epoxy asphalt binders influenced by different dosages of epoxy. The rheological performances and microscope structure evolution process influenced by hot mixed epoxy dosage are investigated by dynamic shear rheometer and laser scanning confocal microscopy respectively. The consequences manifest that the flexibility and anti-rutting ability of epoxy asphalt binders is raised accompanied by raising the dosage of epoxy resin are founded by long time of creep, creep and recovery. Dynamic shear rheometer temperature scans showed that epoxy asphalt binders exhibited excellent thermal stability within the scope of −20 °C and 120 °C, when the amount of epoxy resin is no less than 40%. It was observed by laser scanning confocal microscopy that the three-dimensional crosslinked network structure was gradually shaped when the epoxy resin concentration was no less than 40%. These experimental results demonstrated that the excellent performances of epoxy asphalt binders are obtained when the concentration of epoxy resin is no less than 40%. From the viewpoint of epoxy asphalt mixtures, the optimal mixture performances are acquired, when the concentration of epoxy resin is no less than 40%. Finally, when the concentration of epoxy resin is 40%, the excellent cost-performance of epoxy asphalt is obtained, which can be used in steel bridge deck pavement.

A holistic life cycle assessment of steel bridge deck pavement

Transportation serves as a cornerstone of economic development and is a significant contributor to carbon emissions. This study established a life cycle assessment model that incorporates refined carbon emission calculation parameters, streamlining the computation process while maintaining precision. A case study was conducted to quantify the life-cycle CO2 emissions of steel bridge deck pavements, a pivotal component within the transport network. The study suggests using the average annual CO2 emissions as a metric to gauge the carbon emission potential of steel bridge deck pavements with different service lives. This study addresses a void in the existing work by standardizing the grading of typical pavement diseases and quantifying the carbon emissions of the corresponding maintenance measures. It reveals that the pivotal determinant of life-cycle CO2 emissions for steel bridge deck pavements is the service performance of the paving material. Comparative analysis indicates that epoxy asphalt concrete could be deemed a low-carbon material, achieving a 77.6% reduction in life-cycle carbon emissions compared to gussasphalt concrete. Emissions during the maintenance phase constitute 66.7%–71.4% of the total life-cycle emissions, predominantly due to end-of-life and traffic delay units. The methodology and calculated data presented herein can inform subsequent carbon reduction strategies in transportation and promote the development of resilient infrastructure, thus making a substantial contribution towards carbon neutrality and climate change mitigation.

ДОСВІД ЕКСПЛУАТАЦІЇ ТА ОСОБЛИВОСТІ РОБОТИ ВОДОПРОПУСКНИХ СПОРУД ІЗ МЕТАЛЕВИХ ГОФРОВАНИХ КОНСТРУКЦІЙ НА АВТОМОБІЛЬНИХ ДОРОГАХ

Introduction. Road culverts made of metal corrugated structures despite of more than a hundred years of application history, in Ukraine they are a fairly new type of construction and are widely used in transport construction as an alternative to traditional steel or concrete bridge structures. The well-known advantages that justify the choice of such a solution are mainly a short term and relatively low cost of construction, construction provided that the appropriate technological regime is followed and does not cause any particular problems. However, the modern process of design and construction of road culverts from metal corrugated structures is imperfect and is constantly being improved. Therefore, there is a need to conduct an analysis conditions of operation and features of the operation of road culverts from the metal corrugated structures with the determination of approaches to improving design and construction to further increase the durability of the structure as a whole. Problems. From the literature analysis, it was established that road culverts are in difficult operating conditions, which causes their premature destruction. Goal. It consists in the analysis of operating experience and features of the operation of culverts made of metal corrugated structures on highways with the installation of measures to increase durability.

Critical distance-based fatigue evaluation of welded joints in steel bridge under bending loading

Welded joints are prone to fatigue cracking under bending loading in steel bridges. However, certain design curves for these joints primarily focus on tensile or bending-tension loads, potentially leading to inaccurate assessments. This study conducted bending fatigue tests on fillet welded joints (FWJs), evaluating the suitability of specification methods for assessing their fatigue performance and attempting to determine the strength curves required for fatigue assessment. Furthermore, it validated the feasibility of the traditional theory of critical distance (TCD) in predicting the fatigue life of FWJs under bending loading. On this basis, a TCD assessment method incorporating the welding residual stress (WRS) was proposed. The results indicate that the fatigue design curve of the specifications provides conservative predictions for the fatigue performance of FWJs under bending loading, and FAT112 from Eurocode3 may be suitable for fatigue assessment using hot-spot stress and nominal stress methods. Furthermore, predicting fatigue life for FWJs under bending loading with traditional TCD is feasible. Compared to traditional TCD, the proposed method significantly improves prediction accuracy, with a minimum scatter factor of just 1.5. The proposed TCD not only considers the WRS effect but also reduce the need for numerous fatigue tests to establish the design curve for a specific welded joint.

Digital twin-based probabilistic prediction of microcrack initiation and propagation in the weld zone of orthotropic steel deck

Orthotropic steel deck (OSD) has become the primary bridge deck structure for steel bridges, but it is vulnerable to fatigue cracks in the weld zone of OSD under traffic loading. This study proposes a digital twin (DT)-based framework to give the probabilistic prediction of microcrack initiation and propagation in the weld zone of OSD. The DT-based framework couples crystal plasticity finite element model (CPFEM) simulations with fatigue tests through Dynamic Bayesian Network (DBN). The fatigue tests of the specimen (physical entity) cut from the weld zone of OSD and sliced to the centimeter scale are carried out using the standard dynamic test system together with optical microscope. The CPFEM simulations are used to develop a multiscale virtual entity to map the physical entity. The role of the DBN is to integrate the uncertainties into a hierarchical network. After completing the updating of uncertainties in the virtual entity via DBN, the microcrack propagation is predicted probabilistically by the DBN inference, and the microcrack initiation is predicted probabilistically by the inverse DBN inference. The results demonstrate the feasibility and accuracy of the DT-based framework for a full prediction of microcrack initiation and propagation in the weld zone of OSD.

Feasibility of using superelastic shape memory alloy in plastic hinge regions of steel bridge columns for seismic applications

AbstractThis paper contributes to the further development of seismic resilient infrastructure by introducing a novel prototype bridge which integrates tubular steel piers with superelastic shape memory alloy (SMA). The proposed bridge pier is composed of a circular steel tube which is bonded to a superelastic SMA tube in regions of high stress. The pier is distinguished by its ability to minimize residual drifts following inelastic deformations induced by cyclic loading. Three‐dimensional continuum finite element (FE) models are utilized to examine its lateral behavior. Experimental data is used to demonstrate the effectiveness of the continuum FE procedure in replicating the cyclic response and capturing both global and localized behaviors. A novel composite material model is proposed to represent the degradation of strength and accumulation of irreversible strains in the cyclic response of superelastic SMAs. An iterative procedure for the calibration of this material is presented. Investigations, employing the calibrated FE procedure, focus on the quasi‐static cyclic response of steel piers with superelastic SMA in the plastic hinge zone, aiming to identify the optimal length of the SMA tube for achieving a self‐centering response with reduced residual deformation. The study is then further expanded to examine the seismic response of a bridge structure incorporating such piers. Development of the FE model for the prototype bridge includes the modelling of the piers using continuum elements, while the superstructure, bearing units, abutment walls, and backfill material are modelled using discrete elements. Nonlinear time history analyses are undertaken to investigate the effects of column wall thickness and materials used in the plastic hinge zones of the piers. Dynamic FE study results indicate that bridges employing such piers are capable of returning to their original position, provided the SMA tube is of adequate length.

Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning

AbstractRegional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios.

Investigation on temperature effect of local structural stress during non-uniform cross-section steel bridge deck pavement paving

During the process of gussasphalt concrete paving on the orthotropic steel bridge deck of a non-uniform cross-section box girder bridge, the high-temperature asphalt mixture often produces temperature stresses that can affect bearings safety. In this study, a refined model of a steel box girder bridge with a 660 m scale was developed and numerical simulations were conducted using the finite element software. The temperature field was applied to the orthotropic steel bridge deck in a quasi-dynamic fashion, leading to the determination of a safe width for concrete paving. To assess the accuracy of the computational predictions, field paving was performed under the safe width condition determined from the calculations, and the temperature and local stress data were measured in the field. The results demonstrate that the finite element predictions are in good agreement with the yield strength of the bearing’s local structure, and the measurements also show a close correspondence with the structural yield strength, validating the reliability of the quasi-static simulation approach. The proposed method offers a computational tool and a reliability criterion for future finite element analyses of large-span non-uniform cross-section steel box girder bridges, as well as a safe width example for subsequent gussasphalt mixture paving on orthotropic steel bridge decks.