Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model

Yoon, Sung-Sik; Kim, Jeongseob; Kim, Min-Sun; Tak, Hye-Young; Lee, Young‐Joo

doi:10.3390/app10186476

Cited by 24 publications

(8 citation statements)

References 38 publications

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“…Total system travel time was analyzed as a performance index to evaluate the performance of the bridge network. It was stated that total system travel time calculation was to be adopted as a performance index due to high calculation costs [8]. Yeom performed a one-to-one algorithm for dynamic evacuation routes searching under events of natural disasters.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes

Cho

Lee

et al. 2021

Applied Sciences

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Highway systems play a key role in providing mobility to society, especially during emergency situations, including earthquakes. Bridges in highway systems are susceptible to damage from earthquakes, causing traffic capacity loss leading to a serious impact on surrounding areas. To better prepare for such scenarios, it is important to estimate capacity loss and traffic disruptions from earthquakes. For this purpose, a traffic-capacity-analysisbased methodology was developed to model the performance of a transportation network immediately following an earthquake using a macroscopic multi-level urban traffic planning simulation model EMME4. This method employs the second order linear approximation (SOLA) traffic assignment and calculates total system travel time for various capacity loss scenarios due to bridge damage from earthquakes. It has been applied to Pohang City in Korea to evaluate the performance of traffic networks in various situations. The results indicate a significant increase in travel time and a decrease in travel speed as the intensity of an earthquake increases. However, the impact on traffic volume varies depending on the bridges. It is assumed that the location of the bridges and traffic routing patterns might be the main reason. The results are expected to help estimate the impact on transportation networks when earthquakes cause traffic capacity loss on bridges.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes

Cho

Lee

et al. 2021

Applied Sciences

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“…If serious damage to bridge structures occurs due to natural or man‐made hazards, it may cause social and economic harm to local residents and businesses, as well as lead to indirect damage to other critical infrastructure. Therefore, maintenance through regular inspections of bridge structures plays an important role in preventing serious damage 1,2 …”

Section: Introductionmentioning

confidence: 99%

“…Therefore, maintenance through regular inspections of bridge structures plays an important role in preventing serious damage. 1,2 Often, the majority of bridge structures in a country were constructed during the development of its national economy and industry. The proportion of deteriorated bridges that need to be inspected worldwide has increased rapidly in recent years.…”

Section: Introductionmentioning

confidence: 99%

A novel approach to assess the seismic performance of deteriorated bridge structures by employing UAV‐based damage detection

Yoon

Spencer

Lee

et al. 2022

Structural Contr & Hlth

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Summary All civil infrastructure, including bridges, deteriorates over time. Unmanned aerial vehicle (UAV) based visual inspection of bridges has been proposed to assess the condition of bridges. However, existing methods cannot determine the seismic performance of bridges based on the results of UAV‐based visual inspection. In this study, a novel approach is proposed to assess the seismic performance of deteriorated bridges with the results of UAV‐based damage detection. The proposed approach consists of two phases: (i) the damage detection phase using a UAV and (ii) the seismic performance assessment phase. The images obtained from UAV survey are used to conduct condition assessment for the bridge, based on a previously developed region‐based convolutional neural network (R‐CNN), and the damage grade is assigned. Note that here damage includes both seismic damage and deterioration. Subsequently, the finite element (FE) model of the intact bridge is updated to correspond to the assigned damage index. To demonstrate the proposed approach, an in‐service prestressed concrete box‐girder bridge is investigated. In particular, the seismic response of the deteriorated bridge is assessed based on a comparison with the intact bridge responses; focus is placed on the maximum moment and maximum displacement at the pier and the girder. Predictions indicate that the seismic responses of the deteriorated in‐service bridge are 10% poorer than those of the intact bridge. These results demonstrate the potential for the UAV‐based approach for evaluating the seismic performance of deteriorated bridges.

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“…With the continuous acceleration of infrastructure construction, the transportation industry has developed by leaps and bounds. However, with the continuous growth of transportation capacity, the traffic density and vehicle load on highways at all levels are also increasing, which makes a considerable number of existing highway bridges unable to meet the needs of transportation [ 1 , 2 ]. In particular, the deterioration of a large number of small- and medium-span bridges is particularly prominent.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based and Data-Driven Superstructure State Prediction Method for Highway Bridges in Service

Duan

Zhong

et al. 2022

Sensors

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Survival analysis is a data-driven approach that is widely used in various fields of biomedical prognostic research, and it is highly reliable in the processing of time-event data. This study developed a method for evaluating the service performance of bridge superstructures using the built-in acceleration sensor of smartphones and the prediction of survival analysis theory. It will be used to assist in the daily maintenance and repair of small and medium bridges. The effects of the upper load-bearing structure, upper general structure, bearings, deck paving, expansion joints, and frequency ratio on the deterioration of the bridge superstructure were investigated. The results show that the first-order vibration frequency of the bridge can be effectively detected by the built-in acceleration sensor of the mobile phone, but its low sensitivity and high output noise make it impossible to accurately detect the higher-order frequencies of the bridge. The upper load-bearing members, the upper general structure, the bearing, the deck pavement, and the frequency ratio are all related to the changing trend of the technical condition level of the bridge superstructure.

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Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model

Cited by 24 publications

References 38 publications

Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes

Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes

A novel approach to assess the seismic performance of deteriorated bridge structures by employing UAV‐based damage detection

Smartphone-Based and Data-Driven Superstructure State Prediction Method for Highway Bridges in Service

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