Urban-Level Data Interoperability of Buildings and Civil Infrastructure Systems for Enhancing Disaster Resilience

Author(s):  
Moeid Shariatfar ◽  
Yong-Cheol Lee
2021 ◽  
Author(s):  
Nikola Blagojevic ◽  
Max Didier ◽  
Bozidar Stojadinovic

Communities and their supporting civil infrastructure systems can be viewed as an assembly of, often numerous, interacting components. Tools that can identify components relevant for community disaster resilience can help to efficiently allocate limited resources to reach community resilience goals. We use Sobol’ indices to measure the importance of vulnerability and recoverability of components for disaster resilience of communities with interdependent civil infrastructure systems. The initial component importance analysis requires no prior knowledge regarding component’s vulnerability and recoverability. We first rank components based on their importance, using their Sobol’ indices. Secondly, we illustrate how the results of the component importance analysis can be used to improve community disaster resilience. Finally, we use component importance to show how model complexity can be reduced by abstracting less important components.


2021 ◽  
Author(s):  
Nikola Blagojevic ◽  
Fiona Hefti ◽  
Jonas Henken ◽  
Max Didier ◽  
Bozidar Stojadinovic

Disaster resilient civil infrastructure systems are essential for disaster resilient communities. Measuring the resilience of these systems is the first step towards their improvement. This, however, is not easy: civil infrastructure systems are highly complex, operate in different ways, and are affected differently in different disasters. Adding to the complexity are the interdependencies among different systems. The Re-CoDeS framework for quantifying disaster resilience measures the lack of resilience of a system (e.g., a community) as the amount of the system’s unmet demand for a considered resource or service over the resilience assessment interval. This paper extends the Re-CoDeS framework by considering component interdependencies using a demand/supply approach: whenever the demand of a component is not met by the currently available supply capacity of the system, that component ceases to operate and its supply capacity decreases. Interdependency relations among components can change during the resilience assessment interval as the components’ functionality recovers following a disaster. The proposed iRe-CoDeS framework is demonstrated on a virtual community served by three interdependent civil infrastructure systems producing five types of resources and services.


Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2963
Author(s):  
Hyun Myung ◽  
Yang Wang

For several decades, various sensors and sensing systems have been developed for smart cities and civil infrastructure systems [...]


Materials ◽  
2003 ◽  
Author(s):  
Ken P. Chong

The transcendent technologies include nanotechnology, microelectronics, information technology and biotechnology as well as the enabling and supporting civil infrastructure systems and materials. These technologies are the primary drivers of the twenty first century and the new economy. Mechanics and materials are essential elements in all of the transcendent technologies. Research opportunities, education and challenges in mechanics and materials, including nanomechanics, carbon nano-tubes, bio-inspired materials, coatings, fire-resistant materials as well as improved engineering and design of materials are presented and discussed in this paper.


Author(s):  
Hyung Seok Jeong ◽  
Dolphy M. Abraham ◽  
Dulcy M. Abraham

This article reviews current research and practice of knowledge management (KM) in the management of Civil infrastructure systems. Civil infrastructure systems, such as energy systems (electric power, oil, gas), telecommunications, and water supply, are critical to our modern society. The economic prosperity and social well being of a country is jeopardized when these systems are damaged, disrupted, or unable to function at adequate capacity. The management of these infrastructure systems has to take into account critical management issues such as (Lemer, Chong & Tumay, 1995): • the need to deal with multiple, often conflicting objectives; • the need to accommodate the interests of diverse stakeholders; • the reliance of decision making on uncertain economic and social issues; • the constraints in data availability; and • the limitations posed by institutional structure.


2019 ◽  
Vol 23 (11) ◽  
pp. 4851-4867 ◽  
Author(s):  
Phuong Dong Le ◽  
Michael Leonard ◽  
Seth Westra

Abstract. Conventional flood risk methods typically focus on estimation at a single location, which can be inadequate for civil infrastructure systems such as road or railway infrastructure. This is because rainfall extremes are spatially dependent; to understand overall system risk, it is necessary to assess the interconnected elements of the system jointly. For example, when designing evacuation routes it is necessary to understand the risk of one part of the system failing given that another region is flooded or exceeds the level at which evacuation becomes necessary. Similarly, failure of any single part of a road section (e.g., a flooded river crossing) may lead to the wider system's failure (i.e., the entire road becomes inoperable). This study demonstrates a spatially dependent intensity–duration–frequency (IDF) framework that can be used to estimate flood risk across multiple catchments, accounting for dependence both in space and across different critical storm durations. The framework is demonstrated via a case study of a highway upgrade comprising five river crossings. The results show substantial differences in conditional and unconditional design flow estimates, highlighting the importance of taking an integrated approach. There is also a reduction in the estimated failure probability of the overall system compared with the case where each river crossing is treated independently. The results demonstrate the potential uses of spatially dependent intensity–duration–frequency methods and suggest the need for more conservative design estimates to take into account conditional risks.


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