More Like this

1. Local floods induce large-scale abrupt failures of road networks

   https://doi.org/10.1038/s41467-019-10063-w  

   Wang, Weiping ; Yang, Saini ; Stanley, H. Eugene ; Gao, Jianxi ( May 2019 , Nature Communications)

   The adverse effect of climate change continues to expand, and the risks of flooding are increasing. Despite advances in network science and risk analysis, we lack a systematic mathematical framework for road network percolation under the disturbance of flooding. The difficulty is rooted in the unique three-dimensional nature of a flood, where altitude plays a critical role as the third dimension, and the current network-based framework is unsuitable for it. Here we develop a failure model to study the effect of floods on road networks; the result covers 90.6% of road closures and 94.1% of flooded streets resulting from Hurricane Harvey. We study the effects of floods on road networks in China and the United States, showing a discontinuous phase transition, indicating that a small local disturbance may lead to a large-scale systematic malfunction of the entire road network at a critical point. Our integrated approach opens avenues for understanding the resilience of critical infrastructure networks against floods.

    

   more » « less
2. Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

   https://doi.org/10.1111/mice.12527  

   Dong, Shangjia ; Yu, Tianbo ; Farahmand, Hamed ; Mostafavi, Ali ( December 2019 , Computer-Aided Civil and Infrastructure Engineering)

   This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision‐makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near‐real‐time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas.

    

   more » « less
3. Modest flooding can trigger catastrophic road network collapse due to compound failure

   https://doi.org/10.1038/s43247-022-00366-0  

   Dong, Shangjia ; Gao, Xinyu ; Mostafavi, Ali ; Gao, Jianxi ( February 2022 , Communications Earth & Environment)

   Compound failures occur when urban flooding coincides with traffic congestion, and their impact on network connectivity is poorly understood. Firstly, either three-dimensional road networks or the traffic on the roads has been considered, but not both. Secondly, we lack network science frameworks to consider compound failures in infrastructure networks. Here we present a network-theory-based framework that bridges this gap by considering compound structural, functional, and topological failures. We analyze high-resolution traffic data using network percolation theory to study the response of the transportation network in Harris County, Texas, US to Hurricane Harvey in 2017. We find that 2.2% of flood-induced compound failure may lead to a reduction in the size of the largest cluster where network connectivity exists, the giant component, 17.7%. We conclude that indirect effects, such as changes in traffic patterns, must be accounted for when assessing the impacts of flooding on transportation network connectivity and functioning.

    

   more » « less
4. Robust component: a robustness measure that incorporates access to critical facilities under disruptions

   https://doi.org/10.1098/rsif.2019.0149  

   Dong, Shangjia ; Wang, Haizhong ; Mostafavi, Ali ; Gao, Jianxi ( August 2019 , Journal of The Royal Society Interface)

   The objective of this paper is to integrate the post-disaster network access to critical facilities into the network robustness assessment, considering the geographical exposure of infrastructure to natural hazards. Conventional percolation modelling that uses generating function to measure network robustness fails to characterize spatial networks due to the degree correlation. In addition, the giant component alone is not sufficient to represent the performance of transportation networks in the post-disaster setting, especially in terms of the access to critical facilities (i.e. emergency services). Furthermore, the failure probability of various links in the face of different hazards needs to be encapsulated in simulation. To bridge this gap, this paper proposed the metric robust component and a probabilistic link-removal strategy to assess network robustness through a percolation-based simulation framework. A case study has been conducted on the Portland Metro road network during an M9.0 earthquake scenario. The results revealed how the number of critical facilities severely impacts network robustness. Besides, earthquake-induced failures led to a two-phase percolation transition in robustness performance. The proposed robust component metric and simulation scheme can be generalized into a wide range of scenarios, thus enabling engineers to pinpoint the impact of disastrous disruption on network robustness. This research can also be generalized to identify critical facilities and sites for future development. 

   more » « less
5. Anatomy of perturbed traffic networks during urban flooding

   https://doi.org/10.1016/j.scs.2023.104693  

   Rajput, Akhil Anil ; Nayak, Sanjay ; Dong, Shangjia ; Mostafavi, Ali ( October 2023 , Sustainable Cities and Society)

   Urban flooding disrupts traffic networks, affecting mobility and disrupting residents’ access. Flooding events are predicted to increase due to climate change; therefore, understanding traffic network’s flood-caused disruption is critical to improving emergency planning and city resilience. This study reveals the anatomy of perturbed traffic networks by leveraging high-resolution traffic network data from a major flood event and advanced high-order network analysis. We evaluate travel times between every pairwise junction in the city and assess higher-order network geometry changes in the network to determine flood impacts. The findings show network-wide persistent increased travel times could last for weeks after the flood water has receded, even after modest flood failure. A modest flooding of 1.3% road segments caused 8% temporal expansion of the entire traffic network. The results also show that distant trips would experience a greater percentage increase in travel time. Also, the extent of the increase in travel time does not decay with distance from inundated areas, suggesting that the spatial reach of flood impacts extends beyond flooded areas. The findings of this study provide an important novel understanding of floods’ impacts on the functioning of traffic networks in terms of travel time and traffic network geometry. 

   more » « less