Journal of Bridge Engineering

Modern specifications require that connection plates on steel I-girders must be connected to both the flange and the web. While this modern connection detail undoubtedly reduces distortion-induced fatigue compared with past practice, it does not eliminate it. Because fatigue problems may take several decades to develop, the effectiveness of this detail is not fully known at present. Therefore, strain measurements were collected on the girders and cross-frames of a skewed I-girder bridge with modern connection details. This field testing was also used to validate a finite-element model to more thoroughly evaluate the fatigue response and perform a parametric study comparing different skews and inline versus staggered cross-frame configurations. The results support that the modern detailing guidance of using full-depth connection plates connected to both flanges to prevent distortion-induced fatigue is generally effective. The data also indicate three areas where additional consideration may be warranted with respect to modern detailing guidance: the use of a bent plate connection at highly skewed abutments, variable dynamic amplification effects with respect to distortion-induced fatigue, and the use of staggered cross-frames.

Models of bridge deck deterioration with improved predictive power can provide bridge management strategies that will optimize allocation of the available, typically limited, budget of transportation agencies in a more efficient manner. In turn, this will lead to improvement in the overall condition of bridges. To this end, this study developed a novel statistical hazard model of bridge deck deterioration using a generalized gamma accelerated failure time model with bridge deck attributes as covariates. Bayesian inference was used to estimate the parameters of the model and will update these parameters as new inspection data become available. The Markov chain Monte Carlo sampling method was used to estimate the posterior distribution of parameters utilizing both uncensored and censored inspection data. The proposed approach was applied to approximately 30 years of in-service performance data inspected from 1985 to 2015 for more than 22,000 bridges in the state of Pennsylvania. The results showed that the model based on the generalized gamma distribution had a high accuracy. Further, the reliability of different attribute values of the physical makeup of the main span of the structure, the main span interaction type, and the rebar coating are quantified based on the model results. The proposed model can help improve predictions of future bridge deck conditions and provide decision-making tools for infrastructure management.

A prime reason for the restricted use of high-strength steel (HSS) rebar as the main reinforcement in concrete columns in seismic regions is the lack of appropriate seismic design guidelines. Within the context of performance-based seismic design of concrete bridge columns, it is critical to identify drift ratios corresponding to the initiation of different types of damage states. Such drift ratios, which are often referred to as drift ratio limit states, do not seem to exist for concrete columns reinforced with HSS. In this study, a comprehensive analytical program with an overarching objective of proposing simplified expressions to predict drift ratio limit states for circular concrete columns reinforced with HSS was carried out. Factorial analysis was initially performed to quantify the effects of geometry-, section-, and material-related parameters on the drift ratio limit states, and then identify the parameters with significant contributions. To generate a sufficient amount of data for the development of the drift ratio limit states expressions, the Monte Carlo sampling technique was adopted. Three samples each consisting of 1,000 unique columns, were generated for three types of HSS. The columns were analyzed under displacement-controlled quasi-static cyclic loading, and the drift ratios at the onset of the damage states were recorded. The resulting data were first used to establish the drift ratio limit states on a probabilistic basis. Machine learning-based symbolic regression was then employed to establish relationships between the variable parameters and the drift ratio limit states. To maintain simplicity, the proposed expressions contained only parameters deemed significant following the factorial analysis. The proposed simplified expressions for concrete columns reinforced with ASTM A706 Grade 550 had the highest square of correlation coefficients. The proposed simplified expression provided adequate predictions of the drift ratio limit states obtained from the numerical analysis as well as those measured in previous experimental programs.

Load testing is usually used to evaluate the load-carrying capacity of long-span bridges in their service life. However, a large number of calibration trucks should be arranged in complicated loading patterns to deform bridges during load testing, which is labor-intensive and time-consuming. Aiming to solve this problem, this paper proposes a rapid bridge load-carrying capacity evaluation method using limited testing vehicles. The methodology includes three steps: influence line identification from limited vehicle exciting responses; influence line correction based on prior information of the bridge; and influence line–based bridge load-carrying capacity evaluation. To apply this method in engineering practice, standardized truck passage routines are designed. An application example involving a 1,280-m long-span suspension bridge is presented at the end of this paper. The truck routines, extracted bridge influence lines, and predicted load-carrying capacity of that bridge are presented based on the proposed methods. The testing results are also compared with standard load testing conducted 12 years ago to evaluate bridge performance changes. The outcomes of this paper provide a workable and economical method for long-span bridge load-carrying capacity evaluation.

When the conventional response surface method is used to solve the reliability problem in complex structures, the response surface fitting accuracy is low, and the reliability accuracy does not satisfy the requirements of the design specifications owing to the complex structure and highly nonlinear implicit functional function. Therefore, the kriging proxy model is used to construct the response surface of the implicit functional function. In addition, the kriging proxy model is combined with the k-nearest neighbor (KNN) algorithm. By improving the optimization efficiency of the model parameters, the constructed implicit function can be used to simulate the structural limit state function. Therefore, kriging–KNN hybrid analysis to calculate structure failure probability will be proposed. A numerical example will be provided to demonstrate the effectiveness of the proposed method. The results show that the proposed method utilized the kriging proxy model to construct a response surface with high fitting accuracy that used a few samples. In addition, the KNN will be used to address the inadequate accuracy and efficiency of the kriging agent model for classification; therefore, effectively improving the accuracy and efficiency of the structure reliability calculation. Compared with the traditional response surface method, the kriging–KNN hybrid analysis method reduced the error rate and improved the prediction accuracy and calculation efficiency significantly. Furthermore, the model could be easily combined with the existing general finite-element analysis software to analyze the reliability of complex structures.

High-speed trains running over sea-crossing railway bridges can be subjected to earthquake action in deepwater. In analyzing hydrodynamic-induced response on train–bridge interactions, the effect of earthquake-induced hydrodynamic pressure is a critical issue that still needs to be correctly modeled and understood. This study adopts a machine learning-based method for evaluating the earthquake-induced response on train–bridge interactions. A back-propagation neural network (BPNN) surrogate model is established by correlating the environmental parameters with the stochastic responses of train–bridge interactions to improve computational efficiency. Pintang’s bridge, located in China, is selected as a case study. The results show that the proposed method is robust and accurate. The error is less than 1% when compared with the Monte Carlo method (MCM). Moreover, the consideration of earthquake increases the dynamic indices of the bridge girder to about 18%, compared to the case applying only hydrodynamic pressure. Furthermore, this study performed parametric analysis and found that the hydrodynamic pressure reached the maximum value under the action of –90° of incident angle. These results will be helpful for the design of railway bridges in the coastal area.

The deployment of wireless sensor network (WSN) for continuous seismic monitoring system of cable-stayed bridge is presented in this paper. A newly developed WSN that is based on simultaneous high-speed concurrent transmission flooding technique was implemented on the Shin-Nakagawa Bridge, a 530-m single-plane, single-pylon, steel box girder, cable-stayed bridge in Ibaraki prefecture, eastern Japan. The structure is seismically isolated with laminated rubber bearings at the main pylon and piers. Some isolation bearings were partially damaged by the 2011 Great East Japan (Tohoku) earthquake. As a part of monitoring, a WSN with a total of 26 sensor nodes were installed at several locations on the piers, main pylon, girder, and ground. This paper describes the WSN system, the monitoring scheme and procedure, analysis of recorded seismic responses, and their use for structural assessment, especially the performance of isolation bearings during earthquakes. The monitoring system successfully recorded structural responses from 63 earthquakes in the 45 months monitoring period between August 2017 to December 2020, which include the responses from near-field and far-field earthquakes. The comparisons of recorded ground motions with existing nearby seismograph have shown the accuracy and reliability of the WSN. Modal parameters of the bridge were successfully determined from the seismic responses obtained from the WSN using multi-input multi-output time-domain system identifications. Characteristics of seismically isolated bridge responses, such as high-frequency filtering effect and decoupling effect of the girder responses, can be clearly observed from the records of the large earthquakes.

Nonlinear postflutter behaviors of a suspension bridge model with a π-shaped deck section are investigated, focusing on influences of wind yaw angles and low-intensity turbulence. Structural damping properties are first identified by free decaying vibration in still air. The results show that modal damping ratios increase significantly with motion amplitudes. Wind flows with yaw angles are decomposed into components perpendicular and parallel to the bridge deck axis. The flutter instability under wind yaw angles is examined in terms of the perpendicular component. The experimental results show that while small wind yaw angle (10° and 20°) only slightly improve the critical wind speed U_⊥, the largest wind yaw angle (30°) substantially improves the U_⊥ value. Oncoming turbulence of low intensity is found to stabilize the system to some extent, increasing the flutter threshold by about 13% without blurring the demarcation between stability and instability.

Precast segmental columns, as one structures, have attracted increasing attention in bridge and building construction. To understand the performance and dynamic characteristics of precast segmental columns subjected to earthquake excitations, this paper develops a novel approach for time-varying system stiffness identification of precast segmental columns under seismic excitations, based on the adaptive unscented Kalman filtering (AUKF) method. In the proposed approach, a precast segmental column model is considered as an equivalent single-degree-of-freedom (SDOF) system with time-varying system parameters. The AUKF method is employed to identify the time-varying stiffness parameters by using measured acceleration responses and then calculate dynamic displacement responses of the SDOF system to verify the accuracy of the identified stiffness parameters. Since the system parameter identification process and measurement noise covariance matrices are updated at each time step, the estimated results can be reliably obtained to evaluate the conditions of segmental columns subjected to seismic excitations. To verify the feasibility and effectiveness of using the proposed approach for time-varying system stiffness identification of segmental columns, numerical studies on a planar precast segmental column subjected to different intensities of ground motion excitations are conducted. Shake table tests on a scaled precast segmental column and a scaled monolithic column subjected to earthquake excitations are conducted in the laboratory to further verify the performance of the proposed approach. Measured acceleration responses are used for time-varying system identification. Both numerical and experimental results demonstrate that the proposed approach can successfully identify the time-varying stiffness values of precast segmental columns subjected to earthquake excitations, and the displacement responses of these columns are predicted accurately as compared with the measured ones.

A Duo-pile foundation system can be an effective alternative where control of lateral deflection under eccentric lateral loads is stringent, e.g., in elevated highways and railways. Three-dimensional (3D) numerical analysis of such a system is more realistic but complicated and time-consuming. An equivalent one-dimensional (1D) analysis is desirable in practice, which is the focus of this article. First, a 3D model of Duo-pile has been analyzed in different scenarios to understand the interaction of piles and the connecting beam’s role. Next, 3D and 1D models of various components and their interactions have been analyzed independently to find equivalent spring constants by comparing results. It includes pile–soil–pile interactions in longitudinal, transverse, and vertical directions. The Duo-pile system transfers moment in the transverse direction of the viaduct through the connecting beam, making its behavior different from the longitudinal direction. The proposed model covers the piles’ reaction against the moment couple besides lateral and vertical pile interactions. The performance of the proposed 1D model of Duo-pile is assessed in different scenarios such as the presence of pier capping beam, soil heterogeneity, and soil nonlinearity under design loads. This model offers a simplified alternative with reasonable accuracy by realistically capturing the necessary interactions for serviceability conditions.

This paper presents an experimental investigation of a 64-year-old, full-scale, simply supported, prestressed bridge girder retrofitted with external posttensioning. The specific retrofit can be used to increase the flexural strength of corrosion-damaged prestressed beams. The test specimen, taken from a bridge located in Virginia, was subjected to monotonically increasing loads until the occurrence of a diagonal compression failure in the vicinity of one of the external tendon anchor blocks. The experimental test was accompanied by finite-element analyses, aimed to elucidate the failure mode and quantify the impact of the repair on the capacity of damaged beams. Simplified calculations based on strut-and-tie models and design code guidelines are found to give satisfactory estimates of the failure load, while also describing the principal aspects of the force transfer between the external tendons and the beam and the stress state in the immediate vicinity of the anchor blocks.

North America’s growing demand for rail transportation capacity, in terms of both train weight and length, has raised concerns regarding the performance of older bridges and has presented the need for improved assessment technologies. A monitoring system composed of discrete and distributed strain sensors combined with a finite-element analysis (FEA) may be able to provide critical insights for assessment. One specific area of concern in older railway bridges is their ability to resist longitudinal forces generated by the braking and acceleration of modern diesel-electric engines. To investigate this, two experimental campaigns were undertaken on a steel railway bridge in North Bay, Canada, using data from sensors and an FEA to study longitudinal force transfer and the impact of rehabilitation with traction bracing. Data from both pre- and post-rehabilitation tests indicated that most axial force in the rail is transferred along its length to a reaction point off the bridge, rather than being transferred immediately into the superstructure. Furthermore, data from the post-rehabilitation tests suggested that the added traction bracing did not significantly affect bridge behavior. Overall, the results demonstrate how field monitoring using conventional and fiber optic strain sensors can be used to assess force transfer between the rail and a superstructure, which could be used as a first step in assessing whether a bridge requires longitudinal strengthening prior to a more comprehensive assessment.