Journal of Bridge Engineering

A bridge monitoring approach is proposed for the distributed detection and quantification of crack opening displacements (CODs) along the lengths of large bridges. The proposed method does not require a priori knowledge of traffic loads and is applicable under normal operational conditions of bridges. A damage index is introduced, using the difference between crack-induced strain peak values and bridge base strains caused by moving loads. The applicability of the proposed approach for quantification of the detected cracks is demonstrated in terms of CODs at a number of locations in a multispan bridge. Six field tests were performed on a 332 m, five-span precast post-tensioned concrete box girder bridge for this purpose. The bridge was tested under operational conditions; thus, random daily traffic loads were employed as external forces. Two distributed optical fiber sensors based on the Brillouin scattering system were adhered to the external and internal surfaces of the box girder bridge. Fiber Bragg grating crack sensors were also employed for verification of the COD measurements, based on direct acquisition of distributed strains over the 332-m length of the bridge. By using this approach, it is possible to detect and quantify cracks with CODs larger than 5 µm.

Sliding bearings are a key component of bridges, and their normal performance is an important prerequisite to ensure traffic safety. To solve the problem wherein a single type of sensor has difficulty in effectively predicting the wear life of sliding bearings, this paper proposes a method to predict the sliding bearing wear life using the multitype monitoring data of the bridge. This method uses the vertical acceleration data of the girder with high sampling frequency to calculate the bearing cumulative dynamic displacement under vehicle load and then processes the data collected by the longitudinal displacement gauge with low sampling frequency to extract the bearing cumulative static displacement under the effect of temperature. Next, the daily bearing cumulative displacement calculated by adding them is taken as the evaluation index, and the sliding bearing wear life is predicted based on the reliability analysis. Finally, the proposed method is verified by a numerical example of vehicle–bridge interaction and real bridge monitoring data. The obtained results show that the proposed method can be used to estimate the bearing cumulative displacement with high accuracy and can effectively predict the wear life of sliding bearings. The prediction results can provide an important reference for bridge evaluation.

The increasing availability of bridge data from the National Bridge Inventory (NBI) offers a great opportunity to perform predictive analytics (such as bridge deterioration prediction) using machine learning (ML) pipelines for supporting bridge asset management. However, data quality issues (e.g., outliers and missing values) can significantly affect ML pipelines, requiring upstream tasks to be performed for ensuring the validity, applicability, and generalizability of pipelines. Among the tasks, outlier removal and missing value imputation are the most challenging due to a highly laborious process, a lack of data governance, and a mixture of heterogenous data quality issues and data types. To address this challenge, this paper proposes an upstream workflow for enhancing the downstream predictive analytics of bridge-related ML pipelines. The proposed upstream workflow was developed based on the NBI data collected for all States in the United States, which includes a total of 617,084 observations/bridges. Existing bridge domain knowledge from multiple sources (such as the bridge design manual and regulations) was leveraged to remove outliers. Then, this study applied and compared 10 statistical and ML-based data imputation techniques to impute missing values. Statistical analysis and imputation evaluation of NBI data indicated that: (1) 19 and 15 out of the total 38 frequently used features or variables had outliers and missing values, respectively; (2) categorical features are generally more prone to data dropping due to inapplicable values, while numeric features are more subjected to outliers; and (3) ML-based data imputation is more suitable than statistical imputation for both numeric and categorical features, especially for features with high missing rate. The proposed workflow was validated on its capability of improving downstream predictive analytics for bridge deck condition prediction, increasing the balanced accuracy by 6.85%–9.76%. This paper contributes to the body of knowledge by offering a novel upstream workflow that can be utilized as a benchmark for guiding researchers and bridge engineering practitioners to handle NBI data quality issues for better preforming predictive analytics using ML pipelines.

The steel–concrete joint (SCJ) of a railway hybrid girder–cable-stayed bridge transmits tremendous internal forces, resulting in complex structure and stress. The existing complex SCJs lead to difficulties in on-site concrete construction, and the complex stress of the SCJ under train load is prone to diseases such as steel concrete fatigue detachment and cracking. To investigate the complex fatigue performance of a novel SCJ for a long-span high-speed railway hybrid box girder–cable-stayed bridge with a main span of 672 m, finite-element analysis (FEA) and a 1:2 scaled fatigue model test with 2.15 million cycles were conducted. The results showed that the numerical fatigue stresses of the SCJ exhibited a significant shear lag effect in the transverse direction with unfavorable fatigue stresses at the side box. After 1.70 million loading cycles, all the steel plates of the test model remained intact, indicating good fatigue resistance during the design life. Cracks appeared at the junction between the horizontal diaphragm and the web of the steel transition segment (STS) after 1.85 million cycles, showing a fatigue life of 118 years. The measured fatigue crack life of the concrete deck over the postanchor position of the prestressed tendons in the STS was 30.5 years for the local concentrated tensile stress, which can be improved by casting the concrete after the tendons are prestressed. No fracture or other abnormalities occurred in the shear studs and perforated rib (PBL) rebars, and the deflection of the test model and the slip between the steel and concrete were insignificant during the fatigue tests, indicating the good fatigue performance of the SCJ, which was better than that of the STS. The thickness of the bearing plate and diameter of the shear studs for the actual SCJ were suggested to be 40–60 and 16–25 mm, respectively, based on a parametric study.

This study examined the influence of nonuniform wind fields in valleys on the dynamic responses of high-speed trains and long-span bridges. The wind field in a valley is considerably different from a theoretical uniform wind field in terms of mean and fluctuating wind characteristics because of the influence of the speed-up effect of crosswinds. The spatial nonuniformity of a valley wind field along a bridge span is equivalent to a “sudden change” load effect for running trains, which increases the complexity of the wind fields in the train–bridge system and may diminish the running performance of trains. To better understand the nonuniform characteristics of wind fields in valleys, a multipoint synchronous field measurement along the main girder of a long-span suspension bridge was conducted. Based on the measured wind field characteristics, a refined numerical wind field was established for conducting the coupling vibration analysis between train and bridge. It is shown that the valley induces an obvious nonuniformity in the mean wind speed, turbulence intensity, and turbulence integral along the bridge span. A higher mean wind speed and lower turbulence intensity occurs at the midspan of the main girder than at the two side spans. A refined numerical wind field can be established to identify the most unfavorable time for a train to cross the bridge from the perspective of the wind speed field experienced by the train. Compared with a uniform wind field, the nonuniform wind field has a more obvious effect on the lateral deformation of a bridge than on the vertical deformation. When a train crosses a bridge at an unfavorable time, the running safety of the train decreases, but the running comfort coefficient of the train is less affected. The limited influence of nonuniform crosswind on train-running comfort is caused by the difference of lateral bridge deformation between two wind fields.

Bridge cables are the safety lifelines of cable-supported bridges. However, many suspenders in cable-supported bridges were inaccurately evaluated and suffered from premature damage under complex environmental and load actions. A multifactor competition model was proposed and validated to consider the coupling effect of multifactor actions. Furthermore, a framework for corrosion-fretting fatigue analysis was established based on the competition model, stochastic finite-element method, and time-dependent reliability. The degradation process in the wires and suspenders of a bridge was analyzed, and their service lives were accurately predicted. In addition, the suspenders were quantitatively investigated for multifactor coupling and single-factor effects. This paper found that the coupling effect of corrosion, fretting, and fatigue significantly aggravates wire damage and reduces the service lives of short suspenders.

This paper aimed to study the girder behavior and load sharing of multigirder prestressed concrete (PC) bridges using an efficient nonlinear analysis tool based on finite-element (FE) modeling. A newly developed nonlinear beam element, which considers flexure–shear coupling, was adopted to model each girder. Nonlinear macroelements were adopted to model the shear connectors between adjacent girders. Nonlinear beam element modeling was validated by the experimental tests from one interior girder of the bridge considered in this study. The load–deformation relationship for the macroelement was obtained from a three-dimensional continuum-based FE model of shear connectors. The nonlinear analysis apparatus for multigirder bridge systems was applied to study the girder behavior and load sharing of the studied bridge under both flexure-dominant and shear-critical loading scenarios. The advantage of the nonlinear analysis tool, using a nonlinear beam element that considers flexure–shear coupling, was demonstrated through comparison to the model using the conventional nonlinear beam element that neglects shear deformation or flexure–shear coupling. Moreover, the influence of shear connectors on load-carrying capacity and load sharing was quantified by comparing the bridge system behavior of shear-connected girders and that of disconnected girders (with isolated individual girders). It is found that shear connectors play a significant role, but less under shear-critical scenarios than under flexure-dominated scenarios. The influence of losing shear connectors (e.g., due to corrosion damage) and increasing shear connectors on the bridge system behavior (e.g., during rehabilitation or upgrading) was studied. The results indicated that one single shear connector loss out of five between two adjacent girders could result in an overall load-carrying capacity loss of up to 14.32% for the considered multigirder bridge while adding more shear connectors between adjacent girders (e.g., from 5 to 9) can effectively improve the overall load-carrying capacity.

Hangers or suspenders in cable-supported bridges are key load transfer components. These members are vulnerable to sudden fracture due to accidental damage, corrosion, fatigue, or during extreme events. Examples of collapse or damage in cable-supported bridges that are initiated by the sudden loss of hangers have been observed recently. However, specific design provisions that address sudden hanger loss for tied-arch bridges are largely absent. This paper focuses on the sudden loss of hangers in tied-arch bridges, a topic on which there are very limited studies. In this paper, the behavior of a tied-arch bridge that is subjected to single and multiple hanger losses was investigated computationally through a high-fidelity finite-element model (FEM) of a bridge. The simulation results showed that the network tied-arch bridge exhibited robustness against the failure of a single hanger. With the sequential loss of multiple hangers, the loss of hangers that slanted backward had a much larger effect than that of hangers that slanted forward. Inelastic arch buckling precipitated a full system collapse after the loss of nine hangers.