The use of waste and industrial steel fibers as part of the materials used in concrete can increase resistance and reduce cost and air pollution. It also saves energy. One of the important measures for technical inspections and assessment of the existing condition of structures, especially bridges, which is the most important communication factor, is to check the compressive strength. Considering that the calculation of compressive strength in the laboratory is done with the intervention of human power and is undoubtedly affected by human error, we decided to use it through.
Predicting the mechanical properties of concrete reinforced with steel fibers based on artificial neural network models without the need to conduct any laboratory studies will save money in construction projects. Unlike classical methods in statistical theories, neural networks do not require any specific model or function along with limiting assumptions to linearize problems.
For this purpose, this research was done with the aim of compensating this problem and with the aim of building a neural network with high accuracy that can make the desired predictions with the least error. In this research, this modeling was done using artificial neural networks (ANN) and Levenberg algorithm. The data used to train the neural network was collected from 45 different mixing schemes. Then the compressive strength of the sample is determined experimentally. The parameters considered for the ANN inputs are the values of steel fiber, water, water-cement ratio, cement and superlubricant. The objective data of this study included the compressive strength of each of these mixing designs at the ages of 7, 28 and 60 days. Then, to design the neural network, 75% of the data were considered as training data, 15% as target data and 15% as validation data. The compressive strength of concrete samples made from waste steel fibers increases. One of the reasons for this result is the placement and uniform distribution of fibers in the cement matrix, or in other words, the optimal amount of desired fibers in concrete. For experimental information and data, results can be seen with the help of neural network in data analysis. It was observed that the validation is correlated with a correlation coefficient above 99% and the constructed neural network has sufficient accuracy and validity.
Sichuan kahalo Jinsha River Bridge is a suspension bridge with a main span of 1030 m, and the anchorages on both sides are gravity anchorages. In order to adapt to special terrain and geological conditions, anchorage of Sichuan bank pioneered the use of frame structure as the anchorage foundation. The soil and the frame structure jointly bear the vertical load and resist the horizontal component of the main cable to form a "frame soil" community and fully mobilize the role of the undisturbed soil. In order to ensure the integrity of the frame structure, the indirect head of the slot section adopts a rigid joint. At the same time, the distributed grouting technology is used to strengthen the soil around the frame structure, so as to further improve the safety factor.
This paper introduces the topography and geology of the anchorage position, compares and selects different anchorage foundation schemes, and explains in detail the design concept, structure size and construction technology of the frame foundation.
The research shows that using frame structure as anchor foundation is not only reasonable, safe, good economy, but also environmentally friendly. It has solved the difficult problem of anchorage design under poor terrain and geological conditions, and will provide a good reference for the design of mountainous suspension bridges in similar condition.
In bridge structural health monitoring, the response of the bridge while the vehicle is on the bridge, is called a vehicle-bridge interaction (VBI) response. If the vehicle and the bridge are dynamically coupled, the VBI response depends on the bridge’s and the vehicle’s dynamic properties. Therefore, the damage detection techniques based on the bridge resonances become questionable due to the dynamic coupling between the bridge and the vehicle. This study investigates the influence of vehicle dynamics on the bridge’s time-dependent resonances. Vehicle-Induced Delta Frequency (VIDF) represents the changes in the bridge’s time-varying resonances resulting from the vehicle-bridge interaction, while Damage-Induced Delta Frequency (DIDF) accounts for the additional alterations caused by bridge damage. The dynamic interaction between vehicles and bridges (VBIs) is characterized by the frequency ratio between the vehicle (super-system) and the bridge (sub-system). The vehicle frequency is influenced by its dynamics, particularly the suspension systems. Two vehicle models, single suspension and dual suspension vehicles representing passenger trains and freight trains, respectively, are analyzed to assess the significance of vehicle dynamics on VIDF and DIDF. The results demonstrate that both vehicle models experience resonance, which magnifies the dynamic response to damage. However, not all types of vehicles possess the desired dynamic characteristics for effective bridge health monitoring. Trains with single suspension systems exhibit more pronounced changes in the bridge’s frequency response. This characteristic makes them more suitable for effective bridge health monitoring and damage detection.
The most time-consuming processes involved in bridge deck construction are laying and tying conventional reinforcement and verifying the required cover. Thus, there is a need within bridge construction technology to identify opportunities for utilizing steel fibers and replacing more conventional reinforcing bars on bridge decks and this could be a significant step in speeding up bridge construction. Although the bending strength performance of reinforced concrete decks has been the subject of many experiments and research, many considerations still need to be explored. Hence, the current experiment aims to compare and evaluate the bending strength and ductility of two half-scale concrete bridge decks reinforced by steel fiber reinforcement (SFRC) with two half-scale concrete bridge decks reinforced by conventional reinforcement (RC), 27.6 MPa (4000 psi) concrete compressive strength is used in this study, all four decks were tested under flexural loads. Load–displacement curves (P-∆) are recorded as a tool to measure the ductility index (μE) (Spadea et al.). The result showed that the flexural stiffness of the SFRC concrete deck specimens is improved and load carrying capacity increased by 12.3% compared to RC decks. Moreover, crack width and crack are reduced by 14% since the SFRC decks offer more concrete ductility than RC decks, meaning less future maintenance and corrosion. Therefore, the use of steel fiber in concrete mixtures could be a significant step in speeding up bridge construction since it does not require laying, tying, and verifying clear cover, in addition to increasing the lifespan of bridge decks.
This paper presents a numerical model using the boundary element method for determining the hydrodynamic added mass and added damping of an elastic bridge pier with arbitrary cross-section. Combining the Euler–Bernoulli beam theory with the constant boundary element method, the modal superposition method is used to consider the deformable boundary conditions on the surface of elastic piers to couple the interaction between the elastic pier and water, and the equations for the hydrodynamic added mass and added damping of a general section pier considering the effect of pier-water coupling are derived. The accuracy of the developed model is verified by a benchmark experiment. The developed model is calculated for circular piers and compared with the added mass analytical formulation. The effects of oscillating frequency and structure geometry on the added mass and added damping are further investigated. Results demonstrate that the developed model can be used to solve the hydrodynamic added mass and added damping problems of the elastic bridge pier. Compared to the analytical formula, the developed method incorporates the consideration of added damping in the analysis of the pier-water coupling problem. Oscillating frequency and structure geometry have significant effects on added mass and added damping.
In this paper, a new framework is proposed for monitoring the dynamic performance of bridges using three different camera placements and a few visual data processing techniques at low cost and high efficiency. A deep learning method validated by an optical flow approach for motion tracking is included in the framework. To verify it, videos taken by stationary cameras of two shaking table tests were processed at first. Then, the vibrations of six pedestrian bridges were measured using structure-mounted, remote, and drone-mounted cameras, respectively. Two techniques, displacement and frequency subtractions, are applied to remove systematic motions of cameras and to capture the natural frequencies of the tested structures. Measurements on these bridges were compared with the data from wireless accelerometers and structural analysis. Influences of critical parameters for camera setting and data processing, such as video frame rates, data window size, and data sampling rates, were also studied carefully. The research results show that the vibrations and frequencies of structures on the shaking tables and existing bridges can be captured accurately with the proposed framework. These camera placements and data processing techniques can be successfully used for monitoring their dynamic performance.
This paper proposes a weakly-supervised structural surface crack detection algorithm that can detect the crack area in an image with low data labeling cost. The algorithm consists of a convolutional neural networks Vgg16-Crack for classification, an improved and optimized class activation map (CAM) algorithm for accurately reflecting the position and distribution of cracks in the image, and a method that combines superpixel segmentation algorithm simple linear iterative clustering (SLIC) with CAM for more accurate semantic segmentation of cracks. In addition, this paper uses Bayesian optimization algorithm to obtain the optimal parameter combination that maximizes the performance of the model. The test results show that the algorithm only requires image-level labeling, which can effectively reduce the labor and material consumption brought by pixel-level labeling while ensuring accuracy.
In the civil structural health monitoring fields, monitored data suffer from noise and sensor faults. In practice, redundant sensors are usually deployed to monitor structural condition to obtain more accurate and robust information. This paper proposes a beamforming-based spatial filtering method to improve the data quality by using the information redundancy within sensor networks. Data pre-processing is first implemented, including missing data imputation and thermal response separation. Subsequently, short-term Fourier transform is used to transform the measured time sequences into time–frequency domain to obtain more useful features. Finally, signals in the time and frequency domain are processed using the beamforming algorithm. In the beamformers, a linear filter is applied to suppress noise signals, which is formulated as a constrained optimization problem. Herein, interior point algorithm is used to optimize the allocation of the linear filter, wherein the objective function is to minimize the power of the noise component at the beamformer output. The effectiveness of the proposed method is verified by using signals from strain gauges installed on steel deck plates of the 3rd Nanjing Yangtze River Bridge. Results through the case study show that signals after spatial filtering have a satisfactory de-noising, which indicates the effectiveness of the proposed beamforming algorithm. We believe that the proposed beamforming algorithm has substantial potential applications, such as providing high quality data source for further investigations.
To reliably assess the condition of concrete structures, a surface wave-based comprehensive index has been developed. Considering various factors (e.g., aggregate types) influencing the surface wave-based characteristics, a relative index for concrete condition assessment has presented. To address the conflicts between individual indexes, the weight for each index is determined in accordance with its credibility. The effectiveness of the proposed methodology is verified by using test results from six concrete beams with different void volume ratios. Hereby three characteristics of surface waves (i.e., P- and R-wave velocities, and attenuation coefficient) are used to diagnose the concrete beams. It is found that the comprehensive index has the advantage in handling abnormal measurements of the individual indexes by reducing their weights to lower their influence on the final diagnosis. Comparison of the diagnoses based on the comprehensive index and individual indexes reveals that the comprehensive index performs much better than any other individual index. It matches greatly with the compressive strength of the concrete beams obtained from the destructive test. In conclusion, the proposed methodology presents a single comprehensive diagnostic feature with improved reliability on the fitness of the concrete structures for decision-makers.
A mathematical morphological filter-based de-noising method is developed in this study for bridge cable force monitoring data. Structure elements, one of the most important parameters in the mathematical morphology, dominate de-noising effects. The de-noising effects subject to single structure element and multi-structure element filters are discussed based on the simulation signals. The results indicate that the de-noising effects by using the spherical structure element are better than using the straight line or rhombic structure element. Moreover, the multi-structure element filter outperforms the single one. Through simulation analysis, the de-noising performance of the low-pass filter, wavelet filter and morphological filter is compared. The results show that the performance of the wavelet and morphological filters is better than that of the low-pass filter. For low signal-to-noise signals, the performance of the wavelet filter is superior. With the increase of signal-to-noise ratio, the morphological filters show more advantages. Taking the cable force monitoring data of the 3rd Nanjing Yangtze River Bridge as an example, the de-noising performance of the wavelet and morphological filters is discussed. The results show that both the wavelet filters and morphological filters have satisfactory de-noising effects. The mathematical morphology method can provide an optional and effective de-nosing choice, which enriches the means of de-noising for bridge monitoring data.
Bridge deck condition assessments are typically conducted through visual inspections and by incorporating traditional contact sensors for Non-Destructive Evaluation techniques such as hammer sounding and chain dragging, which require the keen expertise of trained inspectors. The accuracy of these inspections is proportional to the level of deterioration of the bridge deck, as the ability of the inspectors is correlated to the apparent level of damage. This study aims to improve the accuracy of bridge deck inspection processes by utilizing non-destructive evaluation techniques, including analyzing point cloud data gathered via Light Detection and Ranging (LiDAR) as a geometry-capturing tool for identifying surface irregularities. This research aims to evaluate and quantify the effectiveness and efficiency of LiDAR sensors in contributing to the suite of technologies available to perform bridge deck condition assessment. To achieve this, the research proposes to understand the deterioration pattern of New Jersey bridges, evaluate the results gathered from point cloud data collected on a full-scale bridge deck, and quantify the information gained from deploying LiDAR on operating bridges in New Jersey. Two data processing approaches were chosen to measure the gross and fine dimensions of the evaluated bridge decks, such as the Curvature Extraction and Slope Analysis method, and the Least Square Plane Fitting method, resulting in an accuracy of 97.92% in reference to the results gathered from reports generated through the analysis of state-of-the-art NDE technology data and visual inspection.
This paper develops an improved structural health assessment method for cable-stayed bridge to address the issue of neglecting component correlations in existing assessment standards. Firstly, the directed graph of fault transmission between components in the cable-stayed bridge system was constructed. The Pagerank algorithm was used to analyze the degree of correlation between these components, and then the influencing degree of and the influenced degree of each component were determined. Secondly, considering the failure rate of individual components and the influenced degree of other component faults, a condition evaluation method with component correlation for cable-stayed bridge was proposed. Finally, the improved assessment method was applied to a super large-span steel cable-stayed bridge as a case study and compared with the relevant assessment specifications. The results show that main girder alignment, cable force and main tower alignment have a greater degree of correlation with other components and are important indicators for bridge health monitoring. Visual inspection of main girder and bridge bearing are the fault appearance components and should be paid attention to in preventive maintenance. The drainage system and electromechanical facilities are the fault source components and must be kept in good condition in daily inspections. The proposed method considers the interrelationships among components more comprehensively and can provide more reliable bridge health assessment results to support bridge maintenance decisions.
The study presents the design forces of simply supported single-cell reinforced concrete (RC) curved box-girder bridges using a finite element method (FEM) based CSiBridge v.20 software. An existing model has been used to validate the present modelling approach. Models subjected to vertical loading, i.e., dead load (DL) and Indian road congress live load (LL), are considered for investigation. An intensive parametric study examines the maximum values of bending moment (BM), shear force (SF), torsional moment (TM), and vertical deflection (VD) in both girders of bridges. The influences of curve angle and span are considered in the study. The effect of curve angle, up to 12°, is negligible on forces and deflections, and thus such bridges can be analysed as a straight one. Finally, non-dimensional equations are derived for evaluating forces and deflections, so that one may predict these quantities for curved bridges based on straight bridge’s results. Engineers and designers may consider present work valuable in analysing, and designing curved box-girder bridges.
Scour is the gradual erosion of the sediment around a bridge foundation and is one of the leading causes of bridge failure. This erosion is caused by turbulence and sediment transport mechanisms and worsens during high-water flow, such as flooding. A severely scoured bridge is a safety concern for commuters. Monitoring systems are sometimes used to provide indications of the scour extent. Most scour monitoring systems require underwater installation, which is inherently difficult to implement for existing structures. Data obtained from such systems may not necessarily be accurate due to factors such as site temperature fluctuations, or the presence of large debris in the channel causing faulty readings during times of high flooding. Inaccuracy in this data is a problem because it could display erroneous results, leading to a false sense of security. Researchers worldwide are exploring vibration-based techniques to monitor scour to overcome this challenge. These techniques can possibly monitor scour without any underwater installation and may be more efficient than the traditional underwater technologies currently implemented. This review piece aims to present a summary of the several types of scour monitoring techniques traditionally used to monitor scour of bridge structures and the advancement in technology for existing monitoring techniques based on the vibration characteristics of bridges. The importance of monitoring scour progression focused on vibration-based techniques will be discussed as well as providing a fair appraisal of these techniques. This review piece shows evidence through laboratory and field experiments that monitoring a structure based on vibrational changes due to scour is possible, and with the advances in technology over the most recent decade, it is now possible to design cost-effective and accurate scour monitoring systems for future field implemented structural health monitoring projects. This evidence is relevant to future researchers for the implementation of prospective bridge vibration-based systems.
Appropriate modeling of an experimental technology is necessary in order to estimate the aerodynamic characteristic of railway trains and infrastructure (e.g., bridges). Simulation of the earth’s wind characteristics of nature is a well-established practice by using an atmospheric boundary wind tunnel. However, in the mountainous area, the wind characteristics are strikingly different from those of the plain area, the amplitude variation of wind is related to complex terrain. Compared with atmospheric boundary layer winds, which are customarily treated as stationary, winds associated with gust winds originating from mountain areas exhibit rapid changes during a short period. A lack of available field test data and testing techniques has hindered such knowledge of the effect of mountain wind on railway-related applications. To simulate the characteristics of gust winds and prepare for follow-up studies of the impact on the railway-related structures, a gust wind generator was developed in an atmospheric boundary wind tunnel — the CSU wind tunnel. Further, the performance of the gust-wind generator was studied and analyzed under the condition of the combined operation between a gust-wind generator and a wind tunnel.
In earthquake-prone regions, the evaluation of seismic impacts on bridges is crucial to mitigation, emergency, and recovery planning for highway networks. The degree of bridge damage determines the cost and time required for repairs and the level of post-earthquake functionality including disruption of transportation network, increased costs due to reduction of traffic flow and restricted access to emergency routes. The article presents the methodological development and implementation of an interactive web application for rapid geospatial assessment and visualisation of earthquake damage scenarios of municipal highway bridge networks based on open access datasets. The proposed framework consists of the following successive models: hazard, inventory, damage, and impact. The seismic hazard model generates spatial distribution of the shaking intensity for earthquake scenarios in terms of ground motion intensity measure using ground motion prediction equations based on seismic hazard model for Eastern Canada. The shaking intensities are then modified with local site amplification factors based on the Canadian highway bridge design code values. The inventory model provides a database of existing bridges based on open-access data which are then classified according to their seismic vulnerability. The damage model assesses seismic performance of classes of bridges by applying respective fragility functions represented as probabilistic relationships between the intensity measure and the degree of expected damage. The impact model evaluates the post-earthquake traffic-carrying capacity of the highway network based on the predicted damage including repair cost as a percentage of replacement cost of bridges and inspection priority. The web-application is demonstrated with a bridge network in Quebec City including 117 bridges subjected to 180 earthquake scenarios. The proposed methodology is particularly useful to facilitate direct communication of potential impacts to emergency managers and city transport officials.
Based on the three-stage fatigue crack growth model, a corrosion fatigue life prediction method considering the coupling effect of corrosion and fatigue is proposed in this paper. In this case, stress factor amplitude was claimed considering the coupling effect of corrosion and fatigue. Three push-out tests in corrosion conditions were conducted to study the failure mode of studs. The crack propagation of studs, obtained through the push-out tests, was simulated in FRANC 3D to establish a library of adequate stress factor amplitude. According to the corrosion degree of the specimens, the corrosion dissolution rate formula was formed, and the corrosion fatigue life of the specimen was predicted. Results show that the error between the predicted and experimental values is approximately 25%.
This article aims to study the influence of random crowd loading on the perceived vibration response of pedestrians. Firstly, a vertical vibration response analysis method considering pedestrian perception was established based on the random crowd walking model. Secondly, change rules of maximum vibration response of pedestrians, occurrence time and position interval under different random walk models were compared and analyzed. Finally, the vibration response reduction factor was defined by studying the correlation between the maximum vibration response of pedestrians and the peak acceleration of the structure, and the approximate calculation method of the maximum vibration response of pedestrians was proposed. The results show that the maximum acceleration perceived by pedestrians obeys the normal distribution under the four crowd walking models, the response distribution of ordered arrangement model (OAM) is larger than that of the other three models; The location and occurrence time of the maximum response depend on the distribution of pedestrian locations on the footbridge, and there is no significant change with the increase of population density. In addition, the distribution of OAM and stochastic arrival model (SAM) are consistent, which is concentrated in the middle of the total time-history. In contrast, the distribution of stochastic distribution model (SDM) and dynamic equilibrium model (DEM) are relatively uniform. The maximum error between the calculated acceleration maximum value and the actual acceleration value felt by the pedestrian is less than 5%. These results can provide reference for quantitative evaluation of pedestrian-induced vibration comfort.
Stress/force monitoring of prestressing tendons is challenging but crucial to the evaluation of the safety of structures in which they are used. To this end, a smart elasto-magneto-electric (EME) sensor based on elasto-magnetic (EM) and magneto-electric (ME) effects is proposed for noncontact field monitoring of the absolute stress in these steel tendons. In this paper, our research in design, implementation, and application of the EME sensory system for non-destructive monitoring of prestressing tendons is overviewed. The results confirm that the developed EME sensor possesses high repeatability, ease of operation and maintenance, corrosion resistance, and long expected service-life. It is demonstrated that the proposed EME sensory technology is feasible for the stress/force monitoring of prestressing tendons in both new and existing structures and the EME sensory system is reliable and stable.
Prestressed girders often deteriorate over time due to environmental and man-made stressors, lowering the strength and serviceability of bridge structures. Although structural repairs are implemented to improve the load carrying capacity of the structure, the presence of numerous unknowns leads to high uncertainty in estimating the adequacy of repairs. For instance, the cross-section of the remaining strands, material properties, applied external loads, and workmanship assumptions made throughout the repair process introduce ambiguities when estimating the adequacy of the repairs. This study evaluates the efficiency of re-tensioning repairs of prestressed concrete bridge span girders. The repairs include field splicing, re-tensioning, of deteriorated or damaged strands by torquing a splicing coupler. The evaluation in this study considers component, system reliability, and load ratings while accounting for several uncertainties, such as structural repair, material properties, and external loads. This paper introduces an approach to account for prestressing strands damage and repair uncertainties while also accounting for other uncertainties. In this regard, five cases are studied: as-built, repaired, and three varying degrees of damage cases. First, the distribution for structural demand and capacity accounting for uncertainty in loads, material properties, and repair process is defined for each girder in the prestressed concrete bridge span. In doing so, Monte-Carlo simulation is employed to determine the distributions. Accordingly, the limit state function of the girders is defined from the obtained distributions. Then, the component reliability of each AASHTO (American Association of State Highway and Transportation Officials) Type II girder is calculated from the obtained reliability indices based on the determined limit state functions. Finally, a system reliability model of the span is developed from the component reliability of each girder. Some advantages and disadvantages of using component and system reliability index versus load rating in damaged and repaired prestressed concrete bridge girders are also discussed. Several critical conclusions are made regarding the uncertainties in structural repair, material properties and external loads, and their impact on the load rating and the component and system reliability of the prestressed concrete bridge structure girders.
In order to clarify the risk of demolition construction of large-span continuous rigid structure bridge and put forward an intelligent safety assessment method to ensure the safety of demolition construction of the closure segment. Taking a concrete continuous rigid bridge as an example, this paper uses the combination of finite element analysis, theoretical calculation and actual measurement verification to study the influencing parameters and construction safety assessment methods of the long-span continuous rigid bridge in the demolition construction stage of the closure segment. The results show that the parameters that have a great influence on the stress state of box girder and pier during the demolition stage of the closure segment are mainly the self-weight of the structure, tendon prestress state and construction temperature difference. Through the influence envelope analysis of each parameter, it is clear that the ultimate failure mode caused by the most unfavorable parameter combination in the demolition stage of the closure segment is the crushing of the bottom plate of the box girder in the middle span, and the cracking of the piers on the side span at the top and the variable section. In order to further accurately evaluate the construction safety in the demolition stage of the closure segment, based on the long-term down-warping state inversion analysis of the box girder, the identification method of cross-section damage and prestress loss of the box girder and the calculation results of engineering examples are given. Finally, a safety assessment method of the most unfavorable section based on the principle of influence matrix is proposed. Through the analysis of an example, the safety of the closure segment demolition construction is clarified, and the correctness of the analysis is verified by intelligent monitoring means.
Bridges are vital to modern transportation infrastructure, providing convenient and efficient access to different locations. However, these structures are susceptible to forces that can cause significant damage and pose a hazard in the event of seismic activity. A country's economy relies heavily on its bridge infrastructure, but many older bridges built before 1970 are showing signs of deterioration due to climate change and other factors. At the time of their construction, seismic design codes did not provide sufficient guidance on proper design and detailing to ensure ductility and capacity, resulting in deficient bridges. This paper provides a brief overview of the literature on the seismic behaviour of bridges and the analytical methods used to evaluate their performance. Various factors that influence the behaviour of different types of bridges are also discussed. This paper aims to establish a theoretical foundation for selecting appropriate methods to analyze bridge structures, prioritizing retrofitting, pre-earthquake planning, and loss measurement tools. The seismic design philosophies and analytical methods are elaborated in-depth, including the methodology to develop fragility curves. The paper also discusses the fragility analysis of retrofitted bridges.
This study employs the finite element-based CSiBridge v.20.0.0 software to examine the response of a single-cell prestressed box-girder bridge subjected to Indian loading conditions. The analyses is carried out on a simply supported bridge considering the specifications of Indian Road Congress (IRC) 6:2017, IRC 18:2000 and IRC 21:2000. An existing model of prestressed skewed bridge is validated with the published one. A convergence study is conducted for determining the model’s mesh size. An extensive parametric study is carried out to gain a better understanding of the response of a skewed prestressed bridge. The parameters variables are: Skew angle (0°, 10°, 20°, 30°, 40°, 50°, and 60°); Span (35, 40, 45, 50, 55, and 60 m); and Span-depth ratio (10, 12, 14, 16 and 18). The results of this study are presented as ratios of Bending moment, Shear force, Torsional moment, and Vertical deflection. Finally, equations for estimation of these ratios for different span and span-depth ratio are also deduced from the statistical approach so that the results of skewed bridges may be evaluated directly. It is determined that the skewed bridge outperforms the straight bridge because of its higher span-depth ratio, which results in less bending moment development. Evidence suggests that the skewness may help to lessen the prestress load’s dominance. The findings of this study may be helpful to engineers and designers in the analysis and design of prestresssed skewed box-girder bridges.
In the present study aims to produce high-strength fiber concrete containing microsilica and metakaolin. Eight concrete mixing samples have been defined. The samples include the control concrete with ordinary Portland cement, replacing 10 percent of the weight of cement with microsilica. The amount of microsilica was kept constant in the next six designs. Three samples with the addition of Forta fibers at the rate of 0.2, 0.5, and 0.8 percent. Finally three samples with 0.5% Forta fibers and 8, 10 and 12% metakaolin were subjected to compressive, tensile and elastic modulus tests at the ages of 7 and 28 days. The addition of Forta fibers and the replacement of microsilica and metakaolin in concrete reduced the slump of concrete up to 5 cm. The highest compressive strength, tensile strength and elastic modulus at the age of 28 days of design 8 (concrete containing 10% microsilica, 0.5% Forte fibers and 12% metakaolin) are respectively equal to 73.6 MPa, 5.55 MPa and 37.49 MPa with The increase was 19.43%, 32.77% and 15.21% compared to control concrete without pozzolan and additives. Also, the relationship between compressive and tensile strength were presented. In total, all samples containing microsilica and fibers had a favorable effect on the resistance properties of concrete compared to the control design. The constant concern of bridge engineers, especially concrete bridges, is the production of concrete with high-strength and very low permeability in the face of their surroundings. Therefore, the result of this research can be a significant contribution to improving the quality of concrete used in bridge constructions.
Computational fluid dynamics (CFD) was used to reproduce wind fields around a twin-box girder. Wind tunnel tests and field measurements were conducted to verify the accuracy of the CFD results. Variations in wind speed at different heights and crosswind reduction effects with different barriers were also examined using CFD simulation; the barriers had significant reduction effects. The reduction effectiveness was closely related to the barrier height and position; porosity was also a crucial factor. The wind speed profiles of a twin-box girder and a single box girder were analysed to determine why the wind speeds above the downstream deck were lower than above the windward deck of the twin-box girder. The results show that the incoming flow leaked downward through the slotted parts of the bridge and formed regulation vortices. Wind speeds were lower above the downstream deck than above the upstream deck as a result of leakage effects. The gap width also influenced the wind environment around the bridge deck.
This article aims to solve the problem of the vehicle bumping at bridgehead which caused by uneven settlement in the transition section of the road and bridge. Firstly, a new adaptive device was established. In addition, the mechanism of the device was revealed. Secondly, analytical calculations were performed for the hitch plate equipped with the adaptive device, the deflection equation was derived, and the analytical solution was compared with the numerical solution. Finally, the adaptive device was applied to the actual project, the elongation of the device was analyzed with the settlement of the hitch plate. The results show that the adaptive device can realize the smooth transition of the road-bridge connection section by self-adjustment. Thus, the bridge head jump can be avoided in the case of soil foundation settlement, overall settlement and slap slope. The analytical and numerical solutions of the maximum deflection at the end of the slab are 2.571 cm and 2.263 cm. The corresponding longitudinal slope change rates are 2.8‰ and 2.5‰. One year after the completion of the actual project, the elongation of the two devices is 1.63 cm and 1.97 cm, and the settlement of the end of the slap is 3.74 cm. The adaptive device can provide a reference for solving the problem of jumping traffic at the bridge head.
The use of precast prestressed concrete bridge piers is rapidly evolving and widely applied. Nevertheless, the probabilistic behavior of the bending performance of precast prestressed concrete bridge piers has often been overlooked. This study aims to address this issue by utilizing actual precast bridge piers as the engineering context. Through the implementation of the Monte-Carlo simulation and Gradient Boosted Regression Trees (GBRT) algorithm, the stochastic distribution of the bending performance and their critical factors are identified. The results show that the normal distribution is the most suitable for the random distribution of bending performance indicators. The variability of the elastic modulus of ordinary steel bars, initial strain of prestressed steel hinge wires, and constant load axial force has little effect on the bending moment performance, while the yield stress of ordinary steel bars, elastic modulus of concrete, compressive strength of unrestrained concrete, and elastic modulus of prestressed steel hinge wires have a greater impact on the bending performance. Additionally, the compressive strength of unrestrained concrete has a significant influence on the equivalent bending moment of the cross-section that concerns designers.
To correctly manage the road infrastructure before and after an earthquake, it is necessary to estimate and even predict the seismic performance of the bridge. The quantification of the bridge's seismic performance response was present in terms of displacement and also based on previous research of reinforced concrete bridge pier models. The displacement did define from a force lateral-displacement response diagram corresponding to the capacity curve, calculated through a non-linear static pushover analysis of the reinforced concrete bridge pier model for each limit state, from intact state to collapse. Thus, six defined displacements correspond to the cracking displacement, the yielding displacement, the spalling displacement, the crushing displacement, the buckling displacement, and the fracturing displacement. The six defined limit states correspond to the cracking limit state, the yielding limit state, the spalling limit state, the crushing limit state, the buckling limit state, and the fracturing limit state. Also, parametric analysis did carry out to evaluate the influence, relative importance, and trend of the input parameters in response to the seismic performance of the reinforced concrete bridge pier model. Eleven input parameters did analyze as the concrete compressive strength, the yield stress of reinforcing steel, the concrete cover thickness, the pier aspect ratio, the configuration of the transverse reinforcement, the spacing of the transverse reinforcing steel, the transversal diameter of the transverse reinforcing steel, the longitudinal reinforcement ratio, the transversal diameter of the longitudinal reinforcing steel, the axial load ratio, and coefficient of subgrade reaction.
The city development is closely related to the performance of the transportation network system. Bridges and roads are important parts of the transportation system, and are also inseparable components of the transportation network. However, the effect of the correlation between bridges and roads on the network system has not been studies thoroughly in the literature. Therefore, it is necessary to analyse the vulnerability of the road network when both bridges and roads are involved. In this paper, the urban road network is modeled into the form of network connection and node, based on the analysis of the related research results of road network vulnerability in the literature. Taking the urban roads at all levels as the connection and the transportation hubs (including bridges) as the nodes, the paper puts forward the corresponding measurement indexes and calculation methods, and establishes the importance and correlation analysis model of roads and bridges in the urban road network. At last, the model is applied to the road network which is 5 × 3 km2 besides Yangpu Bridge of Shanghai for verification, the importance and correlation of specific roads and bridges in the analyzed urban road network are calculated, which provides a certain basis for dealing with various emergencies leading to the decline of urban road network vulnerability. In this paper, the importance analysis of urban road network is extended to the bridge correlation analysis, so that the proposed model of the vulnerability assessment of the urban road network system is more suitable for the increasingly demand of road and bridge construction in China, and provides a certain basis for dealing with the decline of road network vulnerability caused by various emergencies.
To avoid expert inspection, this study develops a decision-making system for large-span bridge inspection intervals based on dynamic fuzzy neural networks (DFNN), which can find knowledge from existing inspection data. A sliding window is introduced to enable the system to learn incrementally so that the system can update along with the bridge degradation. Tsing Ma Bridge is adopted as a prototype bridge while its rating system established based on the fuzzy-analytic hierarchical process (Fuzzy-AHP) method is employed to generate training and testing samples. The capability of the system in finding the relationship between the rating indexes and the rating scores as well as renewing itself with the bridge degradation is then verified. And the influence of the length of the window is investigated. The research shows that the method can make accurate decisions for bridge inspection intervals after being trained by existing data.
In order to identify damage of Chi River bridge’s superstructure, a damage identification indicator is implemented in the field test, which involves the wavelet packet energy analysis with the feature of dynamic response signals caused by vehicle excitation. On the basis of the field test, a series of numeric models with varied service conditions were developed. The wavelet packet analysis method was utilized to decompose the bridge’s acceleration signals at both healthy and damaged status, and the values of Wavelet Packet Energy Variance Change Rate (WPEVCR) were obtained. Then, according to the acceleration signal data measured from the field test, the damage assessment of the condition of Chi River bridge was performed by means of the obtained WPEVCR. The results demonstrate the capability of WPEVCR in localizing and quantifying the bridge damage status. Moreover, another damage indicator based on the Hilbert-Huang Transform (HHT) has been also employed to verify the assessment of WPEVCR, and both damage identification approaches indicate that the Chi River bridge is in a healthy service condition.
This study proposed a yield curvature prediction model considering axial compression ratio with exponential function which is improved on the basis of the specification effective yield curvature prediction model. Parametric moment–curvature curves approach was used to verify that the yield curvature is greatly affected by the section size, the yield strength of longitudinal steel bar and the axial compression ratio. On the basis, the yield curvature under different levels was obtained by Xtract and parametric moment–curvature curves approach, the result shows that with the increase of axial compression ratio, the yield curvature also increases, which is roughly a linear relationship. Subsequently, combined with the specification and considering the influence of axial compression ratio, a new yield curvature prediction model is proposed based on a massive sample space obtained by parametric moment–curvature curves approach. Moreover, by comparing with the experimental database of PEER, the accuracy of new prediction model is verified, and the result shows that the new prediction model is suitable for estimating yield curvature of square section columns.