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Smart Structures and Systems Volume 32, Number 1, July 2023 , pages 49-59 DOI: https://doi.org/10.12989/sss.2023.32.1.049 |
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A surrogate model-based framework for seismic resilience estimation of bridge transportation networks |
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Sungsik Yoon and Young-Joo Lee
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| Abstract | ||
| A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate modelbased comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate modelbased framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage. | ||
| Key Words | ||
| artificial neural network; bridge transportation network; seismic resilience; surrogate model; total system travel time | ||
| Address | ||
| "(1) Sungsik Yoon: Department of Artificial Intelligence, Hannam University, 70 Hannam-ro, Daedeok-Gu, Daejeon 34430, Republic of Korea; (2) Young-Joo Lee: Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea." | ||