Efficient Optimization of Post-Disaster Reconstruction of Transportation Networks

El-Anwar, Omar; Jin, Youngeup; Orabi, Wallied

doi:10.1061/(asce)cp.1943-5487.0000503

Cited by 27 publications

(11 citation statements)

References 27 publications

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“…Later, El-Anwar et al [6] addressed an optimization-based solution of reconstruction plans for damaged transportation networks in the post-disaster period. Last, El-Anwar et al [7] proposed a model to optimize plans of retrofitting damaged transportation networks after a disaster using Mixed Integer Linear Programming. Prioritization models are also applied for decision-making of road or infrastructure maintenance.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Conceptual Model for Accelerated Project Prioritization after Disaster Event

et al. 2019

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There is a need for rapid and responsive infrastructure repair and construction after natural disaster events such as hurricanes, wildfires, and tornadoes. These natural disasters often shut down basic infrastructure systems, including roads, bridges, water supply, and power supply, as experienced recently in several Region 6 states as well as in other states around the country. Accelerated construction practices are often used in these situations to speed up the traditional, and often slow, project delivery process. However, after a natural disaster, several and different types of transportation infrastructure components are in need of inspection, rehabilitation or reconstruction, and transportation agencies are challenged with the task of prioritizing these accelerated projects. This paper studied the current practices and institutional barriers to identify the critical decision criteria and to develop a conceptual model for prioritizing needs for accelerated construction after disaster events, specifically hurricanes and flooding which commonly affect Region 6.

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Section: Literature Reviewmentioning

confidence: 99%

Development of a Conceptual Model for Accelerated Project Prioritization after Disaster Event

et al. 2019

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“…where (●)′ = ∂(●)/∂ x , (●) = ∂(●)/∂ t , u z,j ( x, t ) = vertical deflection of the j th span, u y,j ( x, t ) = lateral deflection of the j th span, L = span length, K = number of magnets attached to the rigid levitation frame, δ (●) = Dirac’s delta function, H ( t ) = unit step function, k = 1, 2, 3, …, K th moving magnetic wheel on the beam, tk = ( k− 1) d/v = arrival time of the k th magnetic wheel into the beam, xk = position of the k th magnetic wheel on the guideway, and ( G y,k , G z,k ) = lateral guidance and uplift levitation forces of the k th lumped magnet in the vertical and lateral directions [ 17 , 18 ].…”

Section: Guideway Modelmentioning

confidence: 99%

Invisible transportation infrastructure technology to mitigate energy and environment

Hossain

2017

Energ Sustain Soc

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BackgroundTraditional transportation infrastructure built by heat trapping products and the transportation vehiles run by fossil fuel, both causing deadly climate change. Thus, a new technology of invisible Flying Transportation system has been proposed to mitigate energy and environmental crisis caused by traditional infrastructure system.MethodsUnderground Maglev system has been modeled to be constructed for all transportation systems to run the vehicle smoothly just over two feet over the earth surface by propulsive and impulsive force at flying stage. A wind energy modeling has also been added to meet the vehicle’s energy demand when it runs on a non-maglev area. Naturally, all maglev infrastructures network to be covered by evergreen herb except pedestrian walkways to absorb CO2, ambient heat, and moisture (vapor) from the surrounding environment to make it cool.ResultsThe research revealed that the vehicle will not require any energy since it will run by superconducting electromagnetic force while it runs on a maglev infrastructure area and directed by wind energy while it runs on non-maglev area.ConclusionsThe proposed maglev transportation infrastructure technology will indeed be an innovative discovery in modern engineering science which will reduce fossil fuel energy consumption and climate change dramatically.

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“…Recently, several disaster recovery models were developed to better understand, guide, and optimize the host communities' recovery process. These models utilized mixed integer linear programming for postdisaster recovery for transportation projects (El-Anwar et al 2015), genetic algorithms for housing recovery (El-Anwar et al 2010), evolutionary algorithms for transportation recovery fund allocation (Karlafits et al 2007), geographic information system (GIS) to guide and manage the disaster management issues (Pradhan et al 2007), numerical models in the earthquake recovery process (Miles and Chang 2006), and operation research in support of disaster recovery planning (Bryson et al 2002). However, the aforementioned models focus on the optimization and reconstruction of isolated projects rather than taking into account the host community's overall welfare and vulnerability.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Disaster Recovery: Multiagent-Based Model for Integrating Environmental Vulnerability into Decision-Making Processes of the Associated Stakeholders

Eid

El-adaway

2017

J. Urban Plann. Dev.

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The goal of sustainable disaster recovery is to regain the built environment's functionality while decreasing the vulnerability of the society to future perturbations. This requires a new generation of decision support tools that integrate the host community's vulnerability assessment while taking into account the stakeholders' interactions, needs, and preferences. The available disaster recovery research focuses on the optimization and reconstruction of isolated projects rather than taking into account the host community's overall vulnerability and welfare. Moreover, the available research did not simultaneously take into account the stakeholders' preferences and needs. To this effect, this paper presents an agent-based model that integrates an environmental vulnerability indicator to better guide the decision-making process of the associated stakeholders. Such an approach will aid urban planners to redevelop societies into a more resilient status. This paper implements a five-step research methodology that comprises: (1) utilizing a comprehensive assessment tool to measure community's environmental vulnerability; (2) developing the objective functions and learning algorithms of the different associated stakeholders; (3) modeling the different attributes and potential strategies interrelated with the different stakeholders; (4) creating an interdependent multiagent-based model that concurrently simulates the aforementioned information; and finally, (5) interpreting and analyzing the results generated from the developed model. The proposed model adopts post-Katrina recovery as the application domain, and thus was tested using the housing and infrastructure recovery projects in three coastal counties in Mississippi. To this end, the model was able to optimize and adapt to the changing vulnerability conditions of the host community. The model also provided an optimal utilization of the infrastructure to decrease the built environment vulnerability to future natural hazards. This provided better outcomes in relation to environmental vulnerability and stakeholders' individual utility functions when compared to the actual implemented disaster recovery plans. For future work, this research will target the integration of other vulnerability indicators. This will lead to more effective representation of the host communities' complex systems, and ultimately achieving a holistic sustainable disaster recovery.

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Efficient Optimization of Post-Disaster Reconstruction of Transportation Networks

Cited by 27 publications

References 27 publications

Development of a Conceptual Model for Accelerated Project Prioritization after Disaster Event

Development of a Conceptual Model for Accelerated Project Prioritization after Disaster Event

Invisible transportation infrastructure technology to mitigate energy and environment

Sustainable Disaster Recovery: Multiagent-Based Model for Integrating Environmental Vulnerability into Decision-Making Processes of the Associated Stakeholders

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