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Transportation Infrastructure under Hurricane Hazards
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Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY 14260, USA
Prof. Dr. Anxin Guo
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China
Dr. Shaopeng Li
Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, USA
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Transportation Infrastructure under Hurricane Hazards

Abstract submission deadline
closed (31 August 2023)
Manuscript submission deadline
closed (31 December 2023)
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4462

Topic Information

Dear Colleagues,

Transportation infrastructure (e.g., bridges, tunnels, highways and railways) is vulnerable to hurricane-related hazards (e.g., strong winds, heavy rain, high surge/waves and severe flooding), which are aggravated by climate change (e.g., increased frequency and intensity of hurricanes). Hence, it is of critical importance to understand hurricane impacts on transportation infrastructure and ensure its resilience during hurricanes. Recently, great success has been achieved in using advanced algorithms (e.g., AI and machine learning) and equipment (e.g., high performance sensors and actuators) to enhance the safety and serviceability of transportation infrastructure. The advanced technologies and methodologies in this new era present great opportunities for building transportation infrastructure that can withstand hurricane events. To explore the potential benefits of technological and methodological advancements, this topic “Transportation Infrastructure under Hurricane Hazards” welcomes the latest research on advanced tools to assess and enhance performance of transportation infrastructure at risk of hurricane hazards. The scope includes, but is not limited to, the following:

  • Modeling schemes of hurricane-induced hazards (e.g., wind, rain, surge, waves and flooding);
  • The effect of climate change on hurricane hazards;
  • Hurricane impacts on transportation infrastructure (e.g., structural vibrations under winds, damage attributed to storm surge and wave loading, impact from debris, and scour or erosion of abutments and foundations);
  • Multi-hazard analysis for transportation infrastructure;
  • Advanced tools to enhance hurricane resilience of transportation infrastructure (e.g., AI, machine learning, sensors and actuators);
  • Risk analysis of transportation infrastructure subject to hurricane hazards;
  • Hurricane mitigation methods of transportation infrastructure;
  • Emergency response of transportation infrastructure during hurricanes;
  • Post-hurricane recovery of transportation infrastructure.

Dr. Teng Wu
Prof. Dr. Anxin Guo
Dr. Shaopeng Li
Topic Editors

Keywords

  • transportation infrastructure
  • hurricane hazards
  • infrastructure resilience
  • climate change
  • artificial intelligence
  • machine learning
  • risk analysis
  • hurricane mitigation
  • emergency operation
  • hurricane recovery

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.5 5.3 2011 18.4 Days CHF 2400
Atmosphere
atmosphere 		2.5 4.6 2010 16.1 Days CHF 2400
GeoHazards
geohazards 		- 2.6 2020 19 Days CHF 1000
Mathematics
mathematics 		2.3 4.0 2013 18.3 Days CHF 2600
Water
water 		3.0 5.8 2009 17.5 Days CHF 2600
Wind
wind 		- - 2021 35.7 Days CHF 1000

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Published Papers (2 papers)

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18 pages, 6527 KiB  
Article
Fast Prediction of Solitary Wave Forces on Box-Girder Bridges Using Artificial Neural Networks
by Minglong Lu, Shaopeng Li and Teng Wu
Water 2023, 15(10), 1963; https://doi.org/10.3390/w15101963 - 22 May 2023
Cited by 1 | Viewed by 1969
Abstract
The extreme shallow-water waves during a tropical cyclone are often simplified to solitary waves. Considering the lack of simulation tools to effectively and efficiently forecast wave forces on coastal box-girder bridges during tropical cyclones, this study investigates the impacts of solitary waves on [...] Read more.
The extreme shallow-water waves during a tropical cyclone are often simplified to solitary waves. Considering the lack of simulation tools to effectively and efficiently forecast wave forces on coastal box-girder bridges during tropical cyclones, this study investigates the impacts of solitary waves on box girders and accordingly develops a fast prediction model for solitary wave forces. Computational fluid dynamics (CFD) simulations are used to simulate the hydrodynamic forces on the bridge deck. A total of 368 cases are calculated for the parametric study by varying the submergence coefficients (Cs), relative wave heights (H/h) and deck aspect ratios (W/h). With the CFD simulation results as the training datasets, an artificial neural network (ANN) is trained utilizing the back-propagation algorithm. The maximum wave forces first increase and then decrease with the Cs, while they monotonically increase with H/h. For relatively large H/h and small Cs values, the relationship between the maximum wave forces and W/h presents strong nonlinearities. The observed correlation coefficients between the ANN predictions and the CFD results for the vertical and horizontal wave forces are 98.6% and 98.1%, respectively. The trained ANN-based model shows good prediction accuracy and could be used as an efficient model for the tropical cyclone risk analysis of coastal bridges. Full article
(This article belongs to the Topic Transportation Infrastructure under Hurricane Hazards)
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14 pages, 4397 KiB  
Article
Modeling Nonlinear Aeroelastic Forces for Bridge Decks with Various Leading Edges Using LSTM Networks
by Xingyu An, Shaopeng Li and Teng Wu
Appl. Sci. 2023, 13(10), 6005; https://doi.org/10.3390/app13106005 - 13 May 2023
Cited by 3 | Viewed by 1586
Abstract
With the rapid increase in bridge spans, the mitigation of risk to flutter (aeroelastic instability) is of critical importance in the design of long-span bridges, especially considering the more frequent intense hurricanes under climate change. Although the strong nonlinearities of the aeroelastic (self-excited) [...] Read more.
With the rapid increase in bridge spans, the mitigation of risk to flutter (aeroelastic instability) is of critical importance in the design of long-span bridges, especially considering the more frequent intense hurricanes under climate change. Although the strong nonlinearities of the aeroelastic (self-excited) forces in wind–bridge interactions can be well captured through either numerical simulations or experimental tests, both are expensive and time consuming. Hence, it is important to develop an efficient reduced-order model for the simulations of nonlinear aeroelastic forces on the bridge decks. This study proposes a reduced-order model based on the long short-term memory (LSTM) network to simulate the nonlinear aeroelastic forces on bridge decks with various leading edges, and thus rapidly predict the corresponding post-flutter behaviors of long-span bridges. To generate the training datasets, computational fluid dynamics (CFD) was employed to simulate the nonlinear aeroelasticities of bridge decks with a wide range of leading-edge configurations and wind speeds. Trained on the high-fidelity CFD datasets, the LSTM network takes the motion of a bridge deck, leading-edge angles and wind speeds as inputs and outputs the nonlinear aeroelastic forces on the bridge decks. A hybrid loss function utilizing the prediction errors of both aeroelastic forces simulated by the LSTM network and the bridge deck responses calculated by the Newmark-β algorithm was introduced into the training process to improve the network performance. The prediction results of the trained LSTM model were compared with the CFD simulations, which demonstrated that the nonlinear aeroelastic forces of the bridge deck with various leading edges can be accurately and efficiently acquired by the proposed LSTM model. Full article
(This article belongs to the Topic Transportation Infrastructure under Hurricane Hazards)
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