Journal of Bridge Engineering

The similarities and differences between the cylindrical and E-shaped damping devices used in bridge engineering are discussed, considering technical factors such as the configuration pattern, mechanical model, hysteresis capacity, modes of failure, material quantity, and installation requirements. Considering the damping effect, a local fine finite-element model is established to analyze the dynamic response of a typical continuous girder bridge. The displacements of the key points and the shear forces of the key components are both evaluated after taking into account the different earthquake ground motion characteristics and incident angles. The differences between the damping effects of these two devices are quantitatively analyzed. Then, each factor that potentially causes this difference is discussed to realize bidirectional control of vibrations in the seismic design of the bridge structure.

California’s high-speed rail (HSR) system is slated to traverse nearly the entire length of the state, and thus it will be exposed to seismic risks from almost every known major tectonic fault there. The present study deals with the seismic responses of bridge-abutment transition backfills (BATBs), which are essential components of HSR bridges. BATBs provide a gradual variation of vertical stiffness between the bridge deck and nominal engineered backfill, enabling smooth operations for trains traveling at high speeds. All prior investigations focused on this vertical stiffness in order to better characterize the localized vertical differential movements around BATBs under periodic high axial loads from train sets. Lateral behavior of BATBs, which would be important under seismic loads, has not previously been investigated. The present study offers a parametric nonlinear lateral force-displacement backbone curve for BATBs that is verified against three-dimensional finite-element models and validated against data from large-scale tests conducted at Brigham Young University. The parametric curve takes backwall height as well as abutment skew angle into account.

The accuracy of camber and corresponding stresses of precast pretensioned concrete beams (PPCBs) is routinely compromised due to ignoring the thermal effects resulting from continuously changing weather conditions, affecting the deck placement on site. While accounting for the effects of a known temperature gradient down the beam depth is straightforward, the temperature gradient varies by region and meteorological season. Using Monte Carlo simulation to probabilistically determine the thermal effects, this study accounts for these variations in combination with the camber and stresses established for PPCBs subjected to dead load and prestress. To utilize the outcomes of this study in design practice, suitable thermal multipliers are proposed. Recommended multipliers produced more realistic camber predictions because the mean absolute error between the measured and predicted values was $5\%\pm 5\%$. The corresponding error increased to $13\%\pm 10\%$ when the thermal multipliers were ignored. The impact of the thermal effects was relatively less on stresses, but it was observed that the stress on the beam’s extreme compression fiber can exceed the allowable stresses in warm summer days.

Deterioration of girder ends has emerged as a major issue for steel bridges with deck joints. The goal of this study was to evaluate the full-scale performance of a newly proposed repair method for steel girder ends with local corrosion damage and section loss over the bearings. The repair consists of welding shear studs to the undamaged portion of the web and encasing the corrosion damage in ultrahigh-performance concrete (UHPC). The composite action between the studs and the UHPC allows the force to bypass the corroded region of the web and stiffener plates, restoring the end bearing capacity of the girder. Four full-scale experiments were conducted on unrepaired and repaired girders with severe section loss. The objectives of the study were to (1) assess the structural performance of the repair, (2) identify the best design practices, and (3) confirm the feasibility of implementing the repair under service loads. The experimental results showed that the proposed repair design successfully restored the original design capacity of a girder.

Improving the accuracy and efficiency of damage detection of bridge structures is a major challenge in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic sensing technology and applying a deep-learning algorithm to perform structural damage detection. With a scaled-down bridge model, three categories of damage scenarios plus an intact state were simulated. A 13-layer supervised learning model based on the deep convolutional neural networks was proposed. After the training process of original continuous deflection under 10-fold cross-validation, the model accuracy can reach 96.9% for damage classification with the performance outperforming that of the other four methods (random forest = 81.6%, support vector machine = 79.9%, $k$-nearest neighbor = 77.7%, and decision tree = 74.8%). The proposed model also demonstrated its decent abilities in automatically extracting damage features and distinguishing damage from structurally symmetrical locations.

This paper proposes a strain-based performance warning method for bridge main girder monitoring using the long-term data obtained for varying load conditions, i.e., the variations in temperature, wind, and traffic load. Because the temperature field variation of the main girder is easy to measure, a correlation model between temperature and strain of the main girder was first established through a novel representative temperature, namely, the canonically correlated temperature. Based on this model, temperature effects on the main girder strain can be accurately estimated and eliminated. However, the influence of wind and traffic load on the main girder strain was difficult to quantify. Thus, principal component analysis was then employed to model the main girder strain after eliminating temperature effects, and the first two principal components were extracted to represent the effects of wind and traffic load, which could subsequently be eliminated. The remaining principal components were then used to reconstruct the model errors, which were minimally influenced by the varying operating conditions. After this analysis, two warning indexes (i.e., the Euclidean distance and the Mahalanobis distance) were defined for the model errors and the remaining principal components to detect potential performance degradations. In addition, a location index was deduced based on contribution analysis to indicate where the performance degradation occurs. Finally, an engineering application to a cable-stayed bridge was carried out to verify the capability and validity of the proposed method.

More than 6,500 material test observations of historical reinforcing steel bars, structural steel, and concrete were compiled from the Texas Department of Transportation (TxDOT) database. Statistical distributions were fitted to represent the strengths of concrete and different grades of steel from given eras. Strength values to be used in load ratings of historical bridges were determined based on the 5th-percentile strengths from these distributions. For bridges built prior to 1954, the data support a yield strength increase of up to 40% for reinforcing steel bars based on this approach. The compressive strength used for load ratings of bridges built prior to 1959 could be increased by up to 20% compared with the current guideline. Specific recommended values are applicable to load ratings of Texas bridges, whereas the statistical framework is applicable generally.