• Structural Engineering and Mechanics
• /
• v.91 no.1
• /
• pp.103-121
• /
• 2024

The smoothed particle hydrodynamics (SPH) method is a numerical technique used in dynamic analysis to simulate the fluid-like behavior of materials under extreme conditions, such as those encountered in explosions or high velocity impacts. In SPH, fluid or solid materials are discretized into particles. These particles interact with each other based on certain smoothing kernels, allowing the simulation of fluid flows and predict the response of solid materials to shock waves, like deformation, cracking or failure. One of the main advantages of SPH is its ability to simulate these phenomena without a fixed grid, making it particularly suitable for analyzing complex geometries. In this study, the structural damage to a masonry arch bridge subjected to blast loading was investigated. A high-fidelity micro-model was created and the explosives were modeled using the SPH approach. The Johnson-Holmquist II damage model and the Mohr-Coulomb material model were considered to evaluate the masonry and backfill properties. Consistent with the principles of the JH-II model, the authors developed a VUMAT code. The explosive charges (50 kg, 168 kg, 425 kg and 1000 kg) were placed in close proximity to the deck and pier of a bridge. The results showed that the 50 kg charges, which could have been placed near the pier by a terrorist, had only a limited effect on the piers. Instead, this charge caused a vertical displacement of the deck due to the confinement effect. Conversely, a 1000 kg TNT charge placed 100 cm above the deck caused significant damage to the bridge.

• Earthquakes and Structures
• /
• v.11 no.5
• /
• pp.841-859
• /
• 2016

Bridge bearings are important connection elements between bridge superstructures and substructures, whose health states directly affect the performance of the bridges. This paper systematacially presents a new method to identify the bridge bearing damage based on the neural network theory. Firstly, based on the analysis of different damage types, a description of the bearing damage is introduced, and a uniform description for all the damage types is given. Then, the feasibility and sensitivity of identifying the bearing damage with bridge vibration modes are investigated. After that, a Radial Basis Function Neural Network (RBFNN) is built, whose input and output are the beam modal information and the damage information, respectively. Finally, trained by plenty of data samples formed by the numerical method, the network is employed to identify the bearing damage. Results show that the bridge bearing damage can be clearly reflected by the modal information of the bridge beam, which validates the effectiveness of the proposed method.

• Steel and Composite Structures
• /
• v.52 no.6
• /
• pp.695-711
• /
• 2024

Suspension bridges are critical to urban transportation, but those in earthquake-prone areas face unique challenges. In the event of a moderate or strong earthquake, conventional linear theory-based approaches for detecting bridge damage become inadequate. This study presents an efficient method for identifying damage in suspension bridges using time history nonlinear inelastic analysis. A practical advanced analysis program is employed to model cable-supported bridges with low computational cost, generating a dataset for four hybrid models: PSO-DT, PSO-RF, PSO-XGB, and PSO-CGB. These models combine decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with particle swarm optimization (PSO) to capture nonlinear correlations between displacement response and damage. Principal component analysis reduces dataset dimensions, and PSO selects the optimal model. A numerical case study of a suspension bridge under simulated earthquake conditions identifies PSO-XGB as the best model for predicting stiffness reduction. The results demonstrate the method's robustness for nonlinear damage detection in suspension bridges under earthquake excitation.

• Steel and Composite Structures
• /
• v.44 no.2
• /
• pp.241-254
• /
• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Earthquakes and Structures
• /
• v.11 no.4
• /
• pp.649-664
• /
• 2016

A seismic damaged bridge may be hit again by a strong aftershock or another earthquake in a short interval before the repair work has been done. However, discussions about the impact of the unrepaired damages on the residual earthquake resistance of a steel bridge are very scarce at present. In this paper, nonlinear time-history analysis of a steel arch bridge was performed using multi-scale hybrid model. Two strong historical records of main shock-aftershock sequences were taken as the input ground motions during the dynamic analysis. The strain response, local deformation and the accumulation of plasticity of the bridge with and without unrepaired seismic damage were compared. Moreover, the effect of earthquake sequence on crack initiation caused by low-cycle fatigue of the steel bridge was investigated. The results show that seismic damage has little impact on the overall structural displacement response during the aftershock. The residual local deformation, strain response and the cumulative equivalent plastic strain are affected to some extent by the unrepaired damage. Low-cycle fatigue of the steel arch bridge is not induced by the earthquake sequences. Damage indexes of low-cycle fatigue predicted based on different theories are not exactly the same.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2006.05a
• /
• pp.194-197
• /
• 2006

This study represents results of fragility curve development for 4-span continuous bridge. 2 type bridge model is chosen frame type and 2-roller 1-hinge type. To research the response of bridge under earthquake excitation, Monte Carlo simulation is performed to study nonlinear dynamic analysis. For nonlinear time history analysis a set of 150 synthetic time histories were generated. Fragility curves in this study are represented by lognormal distribution functions with two parameters and developed as a function of PGA. Five damage states were defined to express the condition of damage based on the actual experimental damage data of bridge column. As a result of this research, the value of damage probability corresponding to each damage state were determined and frame type bridge are favorable under seismic event.

• Earthquakes and Structures
• /
• v.22 no.6
• /
• pp.609-623
• /
• 2022

To study the empirical seismic fragility of a reinforced concrete girder bridge, based on the theory of numerical analysis and probability modelling, a regression fragility method of a rapid fragility prediction model (Gaussian first-order regression probability model) considering empirical seismic damage is proposed. A total of 1,069 reinforced concrete girder bridges of 22 highways were used to verify the model, and the vulnerability function, plane, surface and curve model of reinforced concrete girder bridges (simple supported girder bridges and continuous girder bridges) considering the number of samples in multiple intensity regions were established. The new empirical seismic damage probability matrix and curve models of observation frequency and damage exceeding probability are developed in multiple intensity regions. A comparative vulnerability analysis between simple supported girder bridges and continuous girder bridges is provided. Depending on the theory of the regional mean seismic damage index matrix model, the empirical seismic damage prediction probability matrix is embedded in the multidimensional mean seismic damage index matrix model, and the regional rapid prediction matrix and curve of reinforced concrete girder bridges, simple supported girder bridges and continuous girder bridges in multiple intensity regions based on mean seismic damage index parameters are developed. The established multidimensional group bridge vulnerability model can be used to quantify and predict the fragility of bridges in multiple intensity regions and the fragility assessment of regional group reinforced concrete girder bridges in the future.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2000.04b
• /
• pp.297-303
• /
• 2000

Damage estimation of a bridge structure is presented using ambient vibration data caused by the traffic loadings. The procedure consists of identification of the modal properties and assessment of the damage locations and severities. An experimental study is carried out on the bridge model subjected to vehicle loadings. Vertical accelerations of the bridge deck are measured at a limited number of locations. The modal parameters are identified from the free vibration signals extracted using the random decrement method. Then, the damage assessment is carried out based on the estimated modal parameters using the neural networks technique. The identified damage locations and severities agree reasonably well with the inflicted damages on the structure.

• Smart Structures and Systems
• /
• v.13 no.5
• /
• pp.869-890
• /
• 2014

In this study, a damage detection approach using train-induced vibration response of the bridge is proposed, utilizing only direct structural analysis by means of introducing soft computing methods. In this approach, the possible damage patterns of the bridge are assumed according to theoretical and empirical considerations at first. Then, the running train-induced dynamic response of the bridge under a certain damage pattern is calculated employing a developed train-bridge interaction analysis program. When the calculated result is most identical to the recorded response, this damage pattern will be the solution. However, owing to the huge number of possible damage patterns, it is extremely time-consuming to calculate the bridge responses of all the cases and thus difficult to identify the exact solution quickly. Therefore, the soft computing methods are introduced to quickly solve the problem in this approach. The basic concept and process of the proposed approach are presented in this paper, and its feasibility is numerically investigated using two different train models and a simple girder bridge model.

• Structural Engineering and Mechanics
• /
• v.46 no.5
• /
• pp.615-627
• /
• 2013

One of the most common defects in reinforced concrete bridge decks is corrosion of steel reinforcing bars. This invisible defect reduces the deck stiffness and affects the bridge's serviceability. Regular monitoring of the bridge is required to detect and control this type of damage and in turn, minimize repair costs. Because the corrosion is hidden within the deck, this type of damage cannot be easily detected by visual inspection and therefore, an alternative damage detection technique is required. This research develops a non-destructive method for detecting reinforcing bar corrosion. Experimental modal analysis, as a non-destructive testing technique, and finite element (FE) model updating are used in this method. The location and size of corrosion in the reinforcing bars is predicted by creating a finite element model of bridge deck and updating the model characteristics to match the experimental results. The practicality and applicability of the proposed method were evaluated by applying the new technique to a two spans bridge for monitoring steel bar corrosion. It was shown that the proposed method can predict the location and size of reinforcing bars corrosion with reasonable accuracy.