Bridge Columns Research Articles

Seismic performance of skewed bridges with uniform and non-uniform scour

Seismic safety of highway bridges is one of the critical issues for the functionality of transportation lifelines after severe earthquakes. Skewed highway bridges are found to be more vulnerable due to the irregular distribution of seismic demands on the bridge components. Moreover, scour induced loss of soil material and hence reduction in lateral support around the foundation piles can amplify the seismic effects on the structural components of water crossing bridges. A numerical study was conducted to investigate the seismic performance of skewed and straight highway bridges with varying scour depths and two different scouring types around the bridge piles. Analytical models of straight (0°) and 45° skewed bridges were created using the OpenSees program and a series of nonlinear response history analysis was carried out under the effect of recorded strong ground motions. In the first scouring type which was called as Case-1, scour depth was assumed the same around all piles and varied uniformly. In Case 2, two different non-uniform scouring types with linearly varying scour depth around the piles were investigated. The uncertainty in the earthquake ground motion excitation direction of the two horizontal components was also studied by considering the variable incidence angle of ground motion pairs. The maximum seismic demands of the members at different ground motion incidence angles were evaluated and compared to the demands obtained for the critical ground motion incidence angles suggested by Caltrans. Superstructure displacement, curvature demands of bridge column and shear force demands of pile elements are the seismic demand parameters examined within the scope of the study and were compared for each scour condition for both straight (0°) and 45° skewed bridge.

Optimum Seismic Retrofit of Deteriorating RC Bridge Columns Based on Risk, Cost–Benefit, and Real Option Analyses

Optimum Seismic Retrofit of Deteriorating RC Bridge Columns Based on Risk, Cost–Benefit, and Real Option Analyses

Experimental and numerical study on cyclic behavior of precast segmental railway bridge columns with lap-spliced rebar connections using UHPC wet joints

A novel lap-spliced connection with ultra-high performance concrete (UHPC) was proposed to connect precast railway bridge column segments with footings or cap beams. Two 1:3 scaled specimens were tested under cyclic loads. Specimen PC-LS was a precast column with lap-spliced rebar connection and UHPC wet joints. Specimen CIP was a monolithic column as a reference specimen. The test results revealed that the plastic hinge region of the precast specimen shafted to the upper surface of the UHPC wet joint. The failure mode of the precast specimen was exhibited as the crushing of the core concrete and the buckling/fracture of the longitudinal rebars. Specimen PC-LS experienced larger lateral force, initial stiffness, comparable ductility, and energy dissipation compared to specimen CIP. Additionally, the validated finite-element (FE) models considering the bond-slip effect between concrete and reinforcements were developed. The parametric studies were carried out to further investigate the cyclic behavior of specimen PC-LS. Analytical results demonstrated that the variation of the compressive and tensile strength of UHPC showed little influence on the cyclic behavior of the precast segmental column, which indicated that UHPC with natural curing and lower volume content steel fibers could be used as the wet joint. The precast segmental column had similar lateral force and stiffness with the cast-in-place column when the lap splice length was 10 times of the rebar diameter.

Effects of probability model misspecification on the number of ground motions required for seismic performance assessment

The number of ground motions used in nonlinear response history analysis (NRHA) determines the precision of the parameter estimates obtained in seismic performance assessments. While this issue has been extensively studied in the earthquake engineering literature, the relationship of probability model misspecification to parameter estimation uncertainty, and the implication to the required number of ground motions needed for NRHA, has not been examined. Probability model misspecification has the potential to increase estimation uncertainty and hence requires a greater number of ground motions to achieve the same precision compared to when misspecification is disregarded. This study develops a procedure to determine the required number of ground motions in seismic code-prescriptive and risk-based assessments with possible probability model misspecification. Specifically, we employ the quasi-maximum likelihood estimation (QMLE), which is robust to probability model misspecification, to evaluate estimation uncertainty. The QMLE approach is applied to an archetype California bridge under the two seismic assessment scenarios. In the code-prescriptive assessment, misspecification errors are identified for dispersion estimates of the bridge column ductility demand. In the most extreme case of the risk-based evaluation, misspecification increases the estimation uncertainty of the mean annual frequency of exceeding a limit state by as much as three times, which substantially increases the required number of ground motions. Based on the findings from this study, we advocate for the use of QMLE to detect and rectify the implications of model misspecification to estimation uncertainty and the number of ground motions used in probabilistic seismic performance assessments.

Modal analysis of KRK Bridge by using ansys workbench

The modal analysis of KRK bridge is presented in this paper. The solid model and finite element model of KRK bridge are established by ANSYS WORKBENCH 2023 R2. The model is a whole, not made up of multiple parts. Due to the limited finite element number of WORKBENCH, there may be some deviation between the model studied in this paper and the reality. The mass of the model used in this paper is consistent with reality. The modal analysis is carried out under the boundary condition that the bottom of the bridge column is fixed and the two ends are not fixed. The model does not shift in any direction. The aim is to find six modes that have their own natural frequencies and modal modes. The natural frequency of the model is obtained, and the resonance and collapse of the model are avoided during use.

Probabilistic curvature limit states of corroded circular RC bridge columns: Data-driven models and application to lifetime seismic fragility analyses

Reinforcement corrosion has been recognized as an influential factor in the seismic fragility, both demand and capacity models, of aging reinforced concrete (RC) bridges. For capacity models, accurate and applied prediction tools accounting for aging effects are yet to be well established. Current practices usually perform numerical analyses to obtain time-variant capacity models, which are time-consuming particularly when multi-source structural and environmental uncertainties are considered and sometimes even suffer computational non-convergence. To address these issues, this study leverages a rigorously optimized artificial neural network architecture to develop data-driven models for rapid estimates of probabilistic curvature capacity of corroded circular RC bridge columns with flexural failure modes. An extensive database of multi-level curvature limit states (i.e. slight, moderate, severe, and complete) is created through experimentally validated moment–curvature analyses. A new threshold for the moderate limit state is defined based on the strain of core concrete, rather than cover concrete, to account for the potential full erosion of the cover with drastic corrosion. The data-driven probabilistic capacity models are applied to aid the lifetime seismic fragility assessment of a typical highway bridge, where the spectral acceleration at 1.0 s (Sa-10), peak ground velocity (PGV), and Housner intensity (HI) are found consistently, for the first time, as optimal intensity measures for probabilistic demand modeling of RC columns with different extents of aging effects. For the ease of application, the database and code for the data-driven probabilistic capacity models are accessible at https://bit.ly/3uAa8EY .

Conceptual shaking table tests for resilient segmental bridge columns with resettable sliding joints

The ever-increasing demand for accelerated construction of urban bridges or viaducts calls for more prefabrication with various joint schemes, while satisfying the need for seismic resilience. On the other hand, segmentation into components of various shapes and morphologies is common for multifunctional structures found in living organisms and ancient Chinese timber structures, which also inspires the development of resilient segmental bridge column design. To provide further insights into the cost-effective resilient design of precast bridge columns, this paper suggests the use of resettable sliding joints (RSJ) comprising lubricated gently-inclined joint interface with partially debonded tendon arrangement, essentially forming a hybrid sliding and rocking seismic isolation system of precast segmental structures. The deformability of the proposed design is high. Similitude analysis was conducted considering the rigid body behavior of individual segments under seismic input. It presents the conceptual shaking table testing of four 1:12 prestressed segmental column models fabricated with special three-dimensional (3D) printed plastic molds and interface treatment. A motion capture system was installed for accurate 3D displacement data acquisition and five typical major near-fault earthquake records were selected. Among all specimens, the RSJ segmental column could reach a high damping ratio of about 20 % during the tests, and it outperformed the others in many aspects: (i) a tolerable maximum top drift of about 1 %; (ii) a moderate percentage of transient rocking of about 20 %; (iii) an evenly distributed sliding displacement among all sliding joints; and (iv) negligible residual sliding drift at sliding joints. These superior attributes of RSJ segmental column are consistent under excitation of consecutive rounds of different major earthquake records, indicating high robustness as well as high seismic resilience in the design of RSJ segmental columns.

Probabilistic Postearthquake Vertical Load-Carrying Capacity Loss Model and Rapid Functionality Assessment for Reinforced Concrete Circular Bridge Columns

Probabilistic Postearthquake Vertical Load-Carrying Capacity Loss Model and Rapid Functionality Assessment for Reinforced Concrete Circular Bridge Columns

Accuracy and uncertainty of predicted maximum and residual displacements of RC bridge columns under earthquake excitations

Fundamental to the performance-based seismic design is the accuracy of predicted responses under earthquake excitations. This study evaluates the accuracy and uncertainty of predicted maximum and residual displacements of concrete bridge columns subjected to different types of ground motions. A series of nonlinear time-history analyses considering a wide range of element formulations and model parameter combinations was performed to reproduce measured responses from previous shake table tests. Variations in element formulation had little influence on the accuracy and uncertainty of predicted maximum displacements. The gradient inelastic force-based element, however, predicted bar tensile strain profiles across the plastic hinge with higher resolution, but at a higher computational cost than displacement-based and beam with hinges elements. Models with tangent stiffness-based Rayleigh damping produced the most accurate predictions of the maximum drift ratios with an RMSE of 0.011, indicating that the maximum displacement (or drift ratio) can be predicted with reasonable accuracy. Residual displacements, on the other hand, were predicted with unreasonable levels of error and uncertainty. A data-driven model was thus proposed to correct predicted residual displacement. Following correction, the RMSE of the predicted residual drift ratios was reduced by 43 % to 0.006.

Advanced analysis of intact, fire-damaged, and CFRP retrofitted bridge pier columns under vehicle collisions: Empirical equivalent static force equation and framework

A series of numerical simulations were completed to investigate the behavior of intact, fire-damaged, and Carbon Fiber-Reinforced Polymers (CFRP) retrofitted reinforced concrete (RC) bridge columns of varying sizes subjected to vehicle collisions. Three-dimensional finite element models of isolated RC columns and their foundation systems surrounded by soil volumes were developed using LS-DYNA. A comprehensive parametric study was carried out to investigate the effects of nine demand and design parameters on the performance of bridge columns. Studied parameters included: column diameter, column height, unconfined compressive strength, steel reinforcement ratio, fire duration, CFRP wrap thickness, wrapping configuration, vehicle’s mass, and vehicle’s speed. For each studied scenario, Peak Twenty-five Milli-second Moving Average (PTMSA) was employed to estimate the Equivalent Static Force (ESF) corresponding to each vehicle collision scenario. Resulting ESFs were then utilized to assess effectiveness of the current ESF approach available in the American Association of State Highway and Transportation Officials Load and Resistance Factor Design (AASHTO-LRFD) Bridge Design Specification for analyzing and helping design bridge columns under vehicle collision. Multivariate nonlinear regression analyses were used to derive an empirically based, simplified equation to predict the ESF that corresponds to a vehicle collision. Rather than constant design force, this equation established a correlation between ESF and kinetic energy, column axial capacity, and column height. Results indicated that the proposed equation is reliable and can accurately predict ESFs over a diverse range of collision scenarios that included intact, fire damaged, and CFRP retrofitted columns. To facilitate realistic implementation of the derived equation, an ESF assessment framework was also devised.

Performance-based seismic design of Ultra-High-Performance Concrete (UHPC) bridge columns with design example – Powered by explainable machine learning model

Ultra-high-performance concrete (UHPC) has rapidly become a material of choice in modern construction owing to its favorable properties, such as superior strength, toughness, and durability. Yet, accurate quantification of damage states using appropriate engineering demand parameters (EDPs) remains a significant challenge in the performance-based seismic design (PBSD) of UHPC columns. This gap is especially evident in the scarcity of research focused on the PBSD of UHPC columns. Therefore, this study aims to develop an explainable machine learning (ML) powered predictive model for the drift ratio limit states of UHPC bridge columns across four damage states. To achieve this, a fiber-based numerical model was developed and then validated against large-scale experimental results of UHPC columns tested under reverse cyclic loading. The Latin hypercube sampling method is employed to generate a comprehensive database of UHPC bridge columns (10,000 UHPC columns), considering uncertainties across a spectrum of design factors, including column geometry, material characteristics, reinforcement ratio, and axial load ratio. The drift ratios at the initiation of four damage states are determined using the numerical model. Subsequently, the power of the ML algorithm is leveraged to introduce drift ratio limit states for UHPC columns that capture the onset of four different damage states. The efficacy of the developed model is assessed using a range of statistical metrics, including the composite fitness score, DX% metrics, coefficient of determination, root mean squared error (RMSE), normalized RMSE, and mean absolute error, which demonstrated high predictive accuracy of the drift ratio limit states on both the training and unseen or test datasets. Furthermore, a model-agnostic Shapley Additive exPlanation (SHAP) is used to explain the output of the model and examine the influence of various design factors on drift ratio across the damage states. Notably, the axial load ratio and aspect ratio are found to be major factors in determining the drift ratios across the damage states. The developed model is deployed into a software tool in order to enable its practical application. Additionally, the implementation of the developed model in the context of PBSD is illustrated through a design example. Furthermore, the study proposed capacity uncertainty parameters to aid the fragility assessment of UHPC columns. Finally, the proposed model, along with capacity uncertainty parameters, is used for the fragility assessment of UHPC columns reinforced with varying steel bar grades. The results demonstrate the less vulnerability of UHPC columns reinforced with higher bar grade, yet the effect of bar grade lessens at severe damage levels.

Effects of enhanced confinement on cyclic behavior of concrete bridge columns with partially unbonded seven-wire steel strands

The effects of enhanced confinement on the proposed bridge columns under lateral cyclic loading were investigated in this research by testing large-scale column specimens. The proposed column used nonprestressed partially unbonded seven-wire strands as elastic elements to increase the post-yield stiffness of the column to reduce the residual displacement under seismic loads. The enhanced confinement included rectilinear transverse reinforcement with an increased amount (specimen CSCR) and reduced spacing or innovative five-spiral reinforcement (specimen CSCS). Test results showed that all the specimens exhibited a positive post-yield stiffness ratio of 3.5–6.4 %. However, the enhanced confinement reduced the post-yield stiffness by 42–45 % but increased the lateral strength of the column, which is beneficial for controlling the residual displacement. The enhanced confinement did not affect the drift ratio when the positive post-yield stiffness ended (6 %) but significantly increased the deformation capacity by 15–31 % and the energy dissipation capacity by 37–63 % of the column. Even with a smaller amount of transverse reinforcement, the specimen with the innovative five-spiral reinforcement showed deformation and energy dissipation capacities better than the other specimens with conventional rectilinear transverse reinforcement. A pushover analysis model was developed and modified to better account for the effect of the enhanced confinement and the strand slip, and to consider the end point of positive post-yield stiffness. The comparison with the test result showed that the proposed pushover model well captured the envelope responses of the specimens. Finally, a simplified method was proposed for estimating the moment strength of the proposed column.

Innovative Methods to Improve the Seismic Performance of Precast Segmental and Hybrid Bridge Columns under Cyclic Loading

This paper investigates the seismic performance of prefabricated segmental bridge columns (PSBCs) with hybrid post-tensioned tendons and energy dissipation (ED) bars under cyclic loading. PSBCs with unbonded and hybrid bonded prestressed tendons and columns incorporating ED bars are designed to improve the lateral strength, energy dissipation, and limit the residual drift. The PSBCs under cyclic loading were investigated using the three-dimensional finite element (FE) modeling platform ABAQUS. The FE model was calibrated against experimental results, with an overall error of less than 10%. The seismic performance of the proposed PSBCs was evaluated based on critical parameters, including lateral strength, residual plastic displacement, and the energy dissipation capacity. The results show that bonding the tendons in the plastic hinge region as opposed to the overall bonding along the column leads to a better cyclic performance. The lateral strength, and recentering abilities are further improved by bonding tendons up to 2/3 of the length in the plastic hinge region, along with 100–300 mm in the footing. It was also found that selecting a longitudinal length of ED bars crossing multiple precast segmental joints and having a circumferential spread of 70–90% of core concrete results in a higher bearing capacity and energy dissipation compared to ED bars crossing the single joint.

Experimental and numerical investigations of the impact resistance of socket and pocket connections for precast bridge columns

This study investigates the dynamic performance of precast bridge columns under pendulum impact testing, including socket connection, pocket connection, and cast-in-place (CIP) columns. The study focuses on comparing the impact behavior of CIP and precast columns regarding failure modes, impact force-time, and deflection-time histories. Furthermore, the mechanism by which the socket or pocket connection configurations affect the impact resistance of the precast columns is discussed. The results indicate that the development of cracks in the precast and CIP columns is consistent under the same impact conditions. Notably, the shear resistance mechanisms and failure modes vary significantly at the bottom of the two columns. For the precast specimens, substantial local damage occurs at the connections with consecutive impacts. Specifically, punching shear failure is observed at the pocket connections, whereas the connection sections of the socket columns may pull out from the grooves. Local damage to the column-footing connections reduces the global stiffness of precast columns, leading to higher deformation, greater residual displacements, and lower impact forces than those of CIP columns. A simplified finite element modelling method is proposed to simulate the characteristics of precast column connections, and its accuracy is verified using the test results. The numerical findings show that increasing the friction coefficient at the connection interface promotes the formation of diagonal compression struts within the connection, effectively resisting impact loads and preventing excessive shear stress produced by sliding at the interface. Furthermore, amplifying the minimum embedment depth of the socket-connected precast column to 1.0 D is recommended to ensure safe performance under impact loading.

Experimental and analytical study on performance of anti-collision device using titanium steel and tires under impact load

Anti-collision devices can reduce the damage of bridge columns under ship collision, and a new type of device is proposed in the paper using a combination of titanium steel and recycled tires. The use of titanium steel is to improve the corrosion resistance, and the use of recycled tires is to improve self-floating capacity as well as save the cost. Three test specimens were designed and tested to investigate the behavior of the device under impact load and reveal the failure mechanism; finite element models were conducted to analyze and compare with the experimental results. Also, parametric study was conducted to reveal the effect of parameters on the ultimate capacity of the device. The performances of different types of the anti-collision device are compared, and the failure mechanism is studied. The results show that the proposed device effectively improves the performance of buffering energy dissipation and durability under impact load.

Capacity of Reinforced/Prestressed Concrete Compression Members Strengthened/Repaired Using Ultra-High-Performance Concrete Encasement

Currently, 36% of bridges in the United States need major repair work or replacement and 7% of them are classified as structurally deficient. Most of these bridges can be repaired or strengthened to meet the current load demands and withstand the environmental impacts for the remaining service life. Ultra-high-performance concrete (UHPC) has shown immense potential as a repair and strengthening material thanks to its exceptional characteristics such as high workability, excellent strength and durability, and remarkable energy absorption capacity. This paper presents an analytical approach to predict the capacity of reinforced or prestressed concrete compression members with UHPC encasement (jacket) under combined axial and bending effects. The approach is based on strain compatibility and uses idealized UHPC material models in tension and compression according to the new AASHTO Guide Specifications. The approach uses integration to develop accurate interaction diagrams for any section with complex geometry, which overcomes the approximations of the lamina approach. The paper also provides a comprehensive literature review on UHPC usage in repairing and strengthening concrete bridge columns and piers. A design example of a circular reinforced concrete column is presented to illustrate the proposed approach and to calculate the nominal and design capacities of the composite section. This example has shown that increasing the thickness of the UHPC jacket has a prominent effect on enhancing the axial capacity but only a slight effect on the flexural capacity of compression members.

Feasibility of using superelastic shape memory alloy in plastic hinge regions of steel bridge columns for seismic applications

AbstractThis paper contributes to the further development of seismic resilient infrastructure by introducing a novel prototype bridge which integrates tubular steel piers with superelastic shape memory alloy (SMA). The proposed bridge pier is composed of a circular steel tube which is bonded to a superelastic SMA tube in regions of high stress. The pier is distinguished by its ability to minimize residual drifts following inelastic deformations induced by cyclic loading. Three‐dimensional continuum finite element (FE) models are utilized to examine its lateral behavior. Experimental data is used to demonstrate the effectiveness of the continuum FE procedure in replicating the cyclic response and capturing both global and localized behaviors. A novel composite material model is proposed to represent the degradation of strength and accumulation of irreversible strains in the cyclic response of superelastic SMAs. An iterative procedure for the calibration of this material is presented. Investigations, employing the calibrated FE procedure, focus on the quasi‐static cyclic response of steel piers with superelastic SMA in the plastic hinge zone, aiming to identify the optimal length of the SMA tube for achieving a self‐centering response with reduced residual deformation. The study is then further expanded to examine the seismic response of a bridge structure incorporating such piers. Development of the FE model for the prototype bridge includes the modelling of the piers using continuum elements, while the superstructure, bearing units, abutment walls, and backfill material are modelled using discrete elements. Nonlinear time history analyses are undertaken to investigate the effects of column wall thickness and materials used in the plastic hinge zones of the piers. Dynamic FE study results indicate that bridges employing such piers are capable of returning to their original position, provided the SMA tube is of adequate length.

Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning

AbstractRegional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios.

Predicting low-cycle fatigue-induced fracture in reinforcing bars: A CNN-based approach

In low-damage structures such as rocking columns in bridges where the column ends are protected from spalling through confinement, the longitudinal reinforcing bars undergo reversing strain cycles, leading to fracture due to low-cycle fatigue. Several challenges arise due to the unsuitability of strain gauges to measure high strain ranges, in addition to the inability to visually inspect reinforcing bars after seismic events due to the use of confining details at the ends. Building on previous research that identified the fracture of reinforcing bars using column ends’ rotation to estimate reinforcing bars’ strains in conjunction with low-cycle fatigue models, this paper presents substantial development to predict the low-cycle fatigue-induced fracture of reinforcing bars using convolutional neural networks (CNNs) solely from strain time series data. The fracture was identified through strain measurements measured from a shake table testing of a quarter-scale, two-span resilient bridge specimen subjected to seismic excitations at the Earthquake Laboratory at the University of Nevada, Reno. The developed CNN model utilizes the Markov Transition Field technique to encode the strain time series into images and then uses the encoded images as input for channels in the input layer. These images are stacked in sequence to create a 3D array with 11 channels, one for each of the 11 different earthquake excitations that caused the damage. A three-layered CNN architecture with Adam optimizer was employed in training the model, achieving an accuracy of 100 % during training and more than 96 % during testing. To evaluate the model's performance, three distinct training/testing scenarios are proposed. The results demonstrate the efficacy of using CNNs to detect and characterize damage in structural elements using strain data. This approach has the potential to revolutionize the estimation of material fracture due to low-cycle fatigue in scientific fields using only the recoded strains.

Impact resistance of bridge columns with energy-absorbing crash dampers under vehicle collisions

Bridge structures are often vulnerable to vehicle collisions, necessitating innovative solutions to enhance safety. This paper introduces a concept employing energy-absorbing crash dampers to safeguard bridge structures. The primary objective is to optimize energy dissipation while ensuring uninterrupted bridge functionality. Utilizing the finite element modeling approach, and incorporating realistic constitutive relationships and boundary conditions, simulations were performed to encompass X-shaped damper impact tests and collisions involving trucks with steel-reinforced concrete columns. The study affirms the effectiveness of the numerical model in simulating the dynamic response of steel dampers and column structures under vehicle impacts. Leveraging this modeling approach, this research compares the anti-collision performance of urban bridge structures, both with and without the proposed bridge pier. The methodology involves two key steps: first, a response surface optimization analysis is conducted for the primary energy-absorbing component, the anti-collision hollow diamond-shaped damper, determining optimal dimensions for the device at varying vehicle collision speeds. Second, a comparative study explores failure modes, collision capabilities, deflection changes, and energy absorption of bridge structures with and without the energy-absorbing crash dampers under vehicle impacts. Subsequently, this study performs an optimization design and calculation for the bridge structures. The outcomes reveal that the bridge with the pier experiences a substantial reduction in impact peak force. The damper effectively absorbs part of impact energy, with the optimized damper demonstrating superior energy absorption.