Abstract
In this study, an artificial neural network (ANN)-based surrogate model is proposed to evaluate the system-level seismic risk of bridge transportation networks efficiently. To estimate the performance of a network, total system travel time (TSTT) was introduced as a performance index, and an ANN-based surrogate model was incorporated to evaluate a high-dimensional network with probabilistic seismic hazard analysis (PSHA) efficiently. To generate training data, the damage states of bridge components were considered as the input training data, and TSTT was selected as output data. An actual bridge transportation network in South Korea was considered as the target network, and the entire network map was reconstructed based on geographic information system data to demonstrate the proposed method. For numerical analysis, the training data were generated based on epicenter location history. By using the surrogate model, the network performance was estimated for various earthquake magnitudes at the trained epicenter with significantly-reduced computational time cost. In addition, 20 historical epicenters were adopted to confirm the robustness of the epicenter. Therefore, it was concluded that the proposed ANN-based surrogate model could be used as an alternative for efficient system-level seismic risk assessment of high-dimensional bridge transportation networks.
Highlights
Natural and man-made hazards can cause devastating damage to civil infrastructures, such as transportation, water, gas, and power networks
This study proposes an accelerated methodology of system-level seismic risk assessment with a novel system performance measure
When the intensity of the ground motion is determined at the locations of bridge structures, the probabilities of bridges according to their damage states can be the locations of bridge structures, the probabilities of bridges according to their damage states can be calculated from calculated fromseismic seismicfragility fragilitycurves
Summary
Natural and man-made hazards can cause devastating damage to civil infrastructures, such as transportation, water, gas, and power networks. Complex lifelines have been constructed densely throughout entire cities; disconnecting main components could cause massive direct damage (e.g., repair costs), and indirect damage (e.g., disruption of commercial and residential activities). Bridge transportation networks are extensively constructed to meet the needs of commercial, industrial, and residential activities by providing products or supplies from source to destination nodes through complex transportation systems. Sci. 2020, 10, 6476 necessitates seismic risk assessment for complex bridge transportation networks and a post-hazard recovery strategy. It is essential to analyze the seismic performance of bridge transportation networks, including disconnection of major components, and assess post-hazard performance under disaster conditions [1,2]
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