Journal of Bridge Engineering

Scour can increase the earthquake-induced damage in pile group foundations. Quantifying the parameter sensitivity of the seismic performance for scoured pile group foundations is essential for the optimal design and retrofit of bridges located in seismic-prone regions. Such quantification requires numerical models that are computationally efficient and accurate in describing the mechanical behavior associated with the complex soil–foundation–structure interaction of these systems. This study proposes an efficient finite-element model (FEM) of pile groups based on a beam on the nonlinear Winkler foundation approach. This FEM uses asymmetric p-multipliers to describe the different soil resistance exerted on leading and trailing piles when applying cyclic lateral loads. The proposed FEM is validated by comparing the numerical response with the experimental measurements taken from a quasi-static test available in the literature and is used to perform an extensive parametric sensitivity analysis to quantify the response sensitivity to 11 structural and soil parameters. Tornado diagrams are employed to identify an importance ranking of these parameters on the seismic performance of scoured pile groups. The obtained results indicate that the proposed FEM is able to capture both the global and local structural responses of pile group foundations. The parametric sensitivity analysis shows that pile group foundations have considerable ductility capacity. Pile diameter and axial load ratio of piles are the most important parameters for the seismic performance of pile groups. Increasing the pile diameter is the most efficient approach to improve the seismic performance of a pile group when considering scour effects. The seismic performance of a scoured pile group deteriorates with increasing piles’ axial load ratio. For a deep pile group foundation, seismic performance is very little sensitive to pile length and relative density of sand. Based on the results of the parametric analysis, recommendations are proposed for the seismic design of pile group foundations with scour effects.

Bridge management systems are a critical component in the toolbox of those who are responsible for maintaining a population of bridges. Deterioration models are generally incorporated in bridge management systems, but minimal consideration is paid to how those models work and how the assumptions inherent to the model might influence the prediction. This paper identifies, synthesizes, and assesses typical bridge deterioration model approaches from the stochastic family of models. Each model considered is applied to two data sets for bridges in Texas and compared. A novel modeling approach that considers all models together is described. The novel approach demonstrates the value of considering multiple models when attempting to predict a future condition or behavior. It was found that a simple multiple model approach inherently and transparently reduces the uncertainty of a single model approach.

Abstract

Inspection data are usually utilized to assess bridge situations for directing further maintenance and preservation. However, due to the complexity of inspection data, mining and fusing valuable information to assess bridge situations remains challenging. To address these issues, a novel inspection data analysis framework was proposed in this study. The framework integrated a gated recursive unit (GRU) model, a semantic segmentation (Seg) model, and a Yolo V4 object detector to analyze both time-series data and images. Seg and Yolo were used to detect defective pixels, which were then evaluated using refined fuzzy inference systems (RFISs) to determine the deterioration grade. The GRU and RFIS models were employed used to infer the probability of bridge deterioration grades. These probabilities were then fused by the novel fusion technique to determine the final deterioration grade. A verification showed GRU, Seg, and Yolo detectors to have 0.9299, 0.9580, and 0.7967 accuracy values for analyzing time-series data and images, respectively. RFISs also performed well in determining concrete and steel deterioration grades with R-values of 0.9968 and 0.9962. Compared with Dempster–Shafer and its two variants, the proposed fusion technique improved the accuracy rates by 11.65%, 2.19%, and 3.38%, respectively. Prototype models also demonstrated abilities to clearly understand deterioration grades and the spatial relationship of defects. Overall, the proposed method could sufficiently mine inspection data and more reasonably assess bridge situations.

Practical Applications

The practical application of this study lies in the fact that it presents a framework for thoroughly mining bridge inspection data, including time-series data and member surface images, to improve deterioration assessments. Combining the gated recurrent unit, you only look once (Yolo) V4 detector, convolutional semantic segmentation (Seg) model, refined fuzzy inference systems, and a novel information fusion technique, the framework provides a powerful solution for mining and integrating information to determine a reasonable deterioration grade, outperforming Dempster–Shafer and its variants. In addition, this study includes 3D prototype models of real bridges to showcase the deterioration situations of bridge components and help understand defect spatial relationships. In practice, once the inspection records are obtained, the programming code can automatically process them to determine the final deterioration grade and visualize the results in 3D mode. This is of great significance in ensuring the longevity, safety, and functionality of a bridge, because the inspection records are difficult to be processed manually over the long operation and maintenance period.

Excessive residual displacements to the bridge structures were repeatedly observed after many previous major earthquakes, which may make the bridge structure lose its functionality and may have to be demolished and rebuilt. Reducing the residual deformation of bridge structures after a severe earthquake is a key research direction in earthquake engineering. Applying self-centering energy dissipation (SCED) braces to dissipate seismic energy and reduce the residual displacement is considered a good solution. Some research works have been carried out to investigate the seismic performance of double-column bridge piers equipped with SCED braces. However, these studies focused on the quasi-static behavior of the bridge piers only, and no previous study investigated their real dynamic response. This study investigates the seismic performance of reinforced concrete (RC) double-column bridge piers with SCED braces through shake table tests. Three 1/5 scaled bridge piers were designed, fabricated, and tested. In particular, the SCED brace was first designed based on the quasi-static performance of the bridge pier. The designed brace was then installed onto the bridge pier, and shake table tests were performed to explore the dynamic response of the bridge pier. For comparison, a bare bridge pier without a brace was also tested. The experimental results showed that the SCED brace could effectively protect the bridge pier from damage and minimize residual displacement.

The present research investigates the use of Fe-based shape memory alloy (SMA) strips for seismic retrofitting of rehabilitated RC bridge pier models. Four severely damaged bridge pier models were first rehabilitated by RC jacketing to retrieve at least their lost strength. Three configurations of Fe-SMA strips namely hoop (H), end-anchored (EA), and a combination of previous two approaches (EAH) were adopted. Prestrained Fe-SMA hoops, placed in the plastic hinge region of the bridge pier model, aims at utilizing the recovery stress developed upon thermal activation for application of active confinement pressure on the test specimen. The EA Fe-SMA strips utilize precompression developed in the concrete in the plastic hinge region of the test specimen. The EAH reinforcement scheme was studied for both precompression and active confinement. To compare the seismic performance of the various Fe-SMA retrofitted test specimens with respect to the control specimen, hybrid simulation (pseudodynamic) of an existing highway bridge located in Tripura, India was carried out with the scaled bridge pier test models as experimental elements. Earthquake excitations of different intensity levels were applied in a sequential manner to analyze the behavior of the structure over a damage spectrum ranging from minor cracking to major damage. To determine the ultimate capacity of the test specimens, cyclic tests were carried out after completion of a hybrid simulation. Bridge pier specimens with Fe-SMA in the form of EA reinforcement exhibited enhanced load-carrying capacity but marginal improvement in ultimate displacement while Fe-SMA hoops improved the ultimate displacement of the test specimen with marginal improvement in the peak lateral load. A combination of EA and hoop Fe-SMA reinforcement was found to be more efficient in improving all the seismic performance parameters such as lesser damage and higher load-carrying capacity, stiffness, and energy-dissipation capacity.

Abstract

The load rating factor is a measurement used to describe the load carrying capacity of a bridge, issue permits to heavy trucks, and determine load postings on bridges. The Bridge Engineering Center (BEC) at Iowa State University (ISU) has developed a method to improve a nondestructive load rating method using continuous structural health monitoring (SHM) data coming from an actual bridge site that does not require traffic disruptions. In the current load rating factor calculation approach, the nominal moment capacity (M_n) is usually calculated utilizing the nominal section dimensions and material properties of the bridge and might not represent the actual capacity of the bridge or its elements. In this work, the rating factor calculation process is further improved by estimating an improved flexural strength for concrete slab on steel girder composite sections. To achieve the objective, a hypothesis to estimate the moment capacity is proposed. To validate the hypothesis, an experimental program was conducted on four steel–concrete composite sections to obtain the moment of inertia of each section and the flexural strength. The experimental results show that the moment of inertia and the flexural strength of a steel–concrete composite section calculated based on nominal material properties are significantly different from the actual moment of inertia and the flexural strength of the section. In the absence of actual material properties, a Monte Carlo simulation along with I_exp from the calibrated load rating model may significantly improve the rating factor of a bridge.

Practical Applications

This work further improves the rating factor calculation process by estimating an improved flexural strength for concrete slab on steel girder composite sections. This method improves a nondestructive load rating method using continuous structural health monitoring (SHM) data coming from an actual bridge site that does not require traffic disruptions. It overcomes existing approaches where reference is made to the design configuration of the bridge, e.g., the cross section, but not to the current or actual state of the bridge.

During the past two decades, more and more rail-cum-road bridges have been built to meet the ever-increasing demand of highway and railway transportation in China. The rail-cum-road bridges are expected to withstand multiple types of loadings during normal operation, including wind, road vehicle, and train. Accurate quantifications of the coupling dynamics among bridge, road vehicle, and train are essential to ensure the safety and reliability of rail-cum-road bridges during their lifetime. The present study develops an analytical framework to simulate the complex wind–road vehicle–train–bridge (WRVTB) system. In addition, a scalable multiple-time-step (SMTS) algorithm is proposed to overcome the computational efficiency issue of traditional single-time-step (STS) algorithms for solving the WRVTB interaction. The SMTS algorithm utilizes domain decomposition techniques to enable a fine and a coarse time-step to be assigned for a road vehicle–train subsystem and bridge subsystem, which greatly improves the computational efficiency for solving the WRVTB interaction. A numerical simulation of coupled WRVTB analysis is conducted based on a prototype rail-cum-road bridge to demonstrate the computational efficiency and accuracy of the proposed method. The results indicate that both the MTS and the SMTS algorithms are able to achieve better accuracy and efficiency simultaneously in comparison with the STS algorithm. Although the SMTS algorithm does not exhibit obvious superiority over the MTS algorithm in terms of the overall accuracy and computational efficiency, the major advantage of the proposed SMTS algorithm over the MTS algorithm is the flexibility. When the SMTS algorithm is adopted, a refined time-step can be assigned to a subsystem of interest, while relatively coarse time-steps can be assigned to the remaining subsystems. As such, the desired accuracy of the subsystem of interest is guaranteed at a low computational cost.

Various solutions can be applied for seismic upgrading of bridges based on the improvement of strength or ductility. However, the use of advanced technologies as passive control systems by seismic isolation or dissipative devices can be more convenient because there is a definite reduction in the action transferred to foundations, which leads to the avoidance of expensive interventions. In this paper, a comparison between various retrofitting techniques is proposed considering the case study of a prestressed existing bridge that has a double cantilever configuration. The results confirm the efficiency of the passive control systems; furthermore, the maintenance of the base isolation system appears more onerous than that of the dissipative device system realized with an external contrast structure.