Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System

Multiple hazard resilience is of significant practical value because most regions of the world are subject to multiple natural and technological hazards. An analysis and assessment approach for multiple hazard spatiotemporal resilience of interdependent infrastructure systems is developed using network theory and a numerical analysis. First, we define multiple hazard resilience and present a quantitative probabilistic metric based on the expansion of a single hazard deterministic resilience model. Second, we define a multiple hazard relationship analysis model with a focus on the impact of hazards on an infrastructure. Subsequently, a relationship matrix is constructed with temporal and spatial dimensions. Further, a general method for the evaluation of direct impacts on an individual infrastructure under multiple hazards is proposed. Third, we present an analysis of indirect multiple hazard impacts on interdependent infrastructures and a joint restoration model of an infrastructure system. Finally, a simplified two‐layer interdependent infrastructure network is used as a case study for illustrating the proposed methodology. The results show that temporal and spatial relationships of multiple hazards significantly influence system resilience. Moreover, the interdependence among infrastructures further magnifies the impact on resilience value. The main contribution of the article is a new multiple hazard resilience evaluation approach that is capable of integrating the impacts of multiple hazard interactions, interdependence of network components (layers), and restoration strategy.     

Inherent Costs and Interdependent Impacts of Infrastructure Network Resilience

Recent studies in system resilience have proposed metrics to understand the ability of systems to recover from a disruptive event, often offering a qualitative treatment of resilience. This work provides a quantitative treatment of resilience and focuses specifically on measuring resilience in infrastructure networks. Inherent cost metrics are introduced: loss of service cost and total network restoration cost. Further, “costs” of network resilience are often shared across multiple infrastructures and industries that rely upon those networks, particularly when such networks become inoperable in the face of disruptive events. As such, this work integrates the quantitative resilience approach with a model describing the regional, multi‐industry impacts of a disruptive event to measure the interdependent impacts of network resilience. The approaches discussed in this article are deployed in a case study of an inland waterway transportation network, the Mississippi River Navigation System.     

Operational Models of Infrastructure Resilience

We propose a definition of infrastructure resilience that is tied to the operation (or function) of an infrastructure as a system of interacting components and that can be objectively evaluated using quantitative models. Specifically, for any particular system, we use quantitative models of system operation to represent the decisions of an infrastructure operator who guides the behavior of the system as a whole, even in the presence of disruptions. Modeling infrastructure operation in this way makes it possible to systematically evaluate the consequences associated with the loss of infrastructure components, and leads to a precise notion of “operational resilience” that facilitates model verification, validation, and reproducible results. Using a simple example of a notional infrastructure, we demonstrate how to use these models for (1) assessing the operational resilience of an infrastructure system, (2) identifying critical vulnerabilities that threaten its continued function, and (3) advising policymakers on investments to improve resilience.     

Toward Disaster‐Resilient Cities: Characterizing Resilience of Infrastructure Systems with Expert Judgments

Resilient infrastructure systems are essential for cities to withstand and rapidly recover from natural and human‐induced disasters, yet electric power, transportation, and other infrastructures are highly vulnerable and interdependent. New approaches for characterizing the resilience of sets of infrastructure systems are urgently needed, at community and regional scales. This article develops a practical approach for analysts to characterize a community's infrastructure vulnerability and resilience in disasters. It addresses key challenges of incomplete incentives, partial information, and few opportunities for learning. The approach is demonstrated for Metro Vancouver, Canada, in the context of earthquake and flood risk. The methodological approach is practical and focuses on potential disruptions to infrastructure services. In spirit, it resembles probability elicitation with multiple experts; however, it elicits disruption and recovery over time, rather than uncertainties regarding system function at a given point in time. It develops information on regional infrastructure risk and engages infrastructure organizations in the process. Information sharing, iteration, and learning among the participants provide the basis for more informed estimates of infrastructure system robustness and recovery that incorporate the potential for interdependent failures after an extreme event. Results demonstrate the vital importance of cross‐sectoral communication to develop shared understanding of regional infrastructure disruption in disasters. For Vancouver, specific results indicate that in a hypothetical M7.3 earthquake, virtually all infrastructures would suffer severe disruption of service in the immediate aftermath, with many experiencing moderate disruption two weeks afterward. Electric power, land transportation, and telecommunications are identified as core infrastructure sectors.     

Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages

The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross‐validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log‐likelihood and root mean squared error. The HBKM yields on average the highest out‐of‐sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.     

Implications of Applying the Green Infrastructure Concept in Landscape Planning for Ecosystem Services in Peri-Urban Areas: An Expert Survey and Case Study

This paper investigates how planning experts understand the potential of integrating the concept of green infrastructure in planning practice in Germany. It develops a systematic approach for green infrastructure planning at the landscape scale. Research methods include a web-based survey among German planning experts and geographic information system analysis in the case study region of Hannover. Survey results suggest that the green infrastructure concept is not yet well known in planning practice, and that the potential benefits for planning lie primarily in communication purposes. An approach is developed here that sets priorities for green infrastructure development based on its potentials for creating synergies in the provision of ecosystem services.     

System of Systems Engineering and Risk Management of Extreme Events: Concepts and Case Study

The domain of risk analysis is expanded to consider strategic interactions among multiple participants in the management of extreme risk in a system of systems. These risks are fraught with complexity, ambiguity, and uncertainty, which pose challenges in how participants perceive, understand, and manage risk of extreme events. In the case of extreme events affecting a system of systems, cause‐and‐effect relationships among initiating events and losses may be difficult to ascertain due to interactions of multiple systems and participants (complexity). Moreover, selection of threats, hazards, and consequences on which to focus may be unclear or contentious to participants within multiple interacting systems (ambiguity). Finally, all types of risk, by definition, involve potential losses due to uncertain events (uncertainty). Therefore, risk analysis of extreme events affecting a system of systems should address complex, ambiguous, and uncertain aspects of extreme risk. To accomplish this, a system of systems engineering methodology for risk analysis is proposed as a general approach to address extreme risk in a system of systems. Our contribution is an integrative and adaptive systems methodology to analyze risk such that strategic interactions among multiple participants are considered. A practical application of the system of systems engineering methodology is demonstrated in part by a case study of a maritime infrastructure system of systems interface, namely, the Straits of Malacca and Singapore.     

A Simplified Approach to Risk Assessment Based on System Dynamics: An Industrial Case Study

Seveso plants are complex sociotechnical systems, which makes it appropriate to support any risk assessment with a model of the system. However, more often than not, this step is only partially addressed, simplified, or avoided in safety reports. At the same time, investigations have shown that the complexity of industrial systems is frequently a factor in accidents, due to interactions between their technical, human, and organizational dimensions. In order to handle both this complexity and changes in the system over time, this article proposes an original and simplified qualitative risk evaluation method based on the system dynamics theory developed by Forrester in the early 1960s. The methodology supports the development of a dynamic risk assessment framework dedicated to industrial activities. It consists of 10 complementary steps grouped into two main activities: system dynamics modeling of the sociotechnical system and risk analysis. This system dynamics risk analysis is applied to a case study of a chemical plant and provides a way to assess the technological and organizational components of safety.     

Major Wind Energy & the Interface of Policy and Regulation: A Study of Welsh NSIPs

This article examines how sub-national policy is applied in consenting decisions for major wind energy infrastructure. The study focuses on the Welsh tier of governance and the perspective of the public, building on existing work on ‘territorial politics’ and public participation. It looks explicitly at the regulatory stage of decision-making, which is critical to understanding multi-level governance contexts for energy infrastructure. Two cases of ‘Nationally Significant Infrastructure Projects’ (NSIPs) in the UK are assessed and findings show how conflict is fuelled by the ways in which different tiers of policy and regulation interact.     

Using GIS in Risk Analysis: A Case Study of Hazardous Waste Transport

This paper provides an illustration of how a geographic information system (GIS) can be used in risk analysis. It focuses on liquid hazardous waste transport and utilizes records archived by the London Waste Regulatory Authority. This data source provides information on the origin and destination of each waste stream, but not the route followed during transport. A GIS was therefore employed to predict the paths used, taking into account different routing criteria and characteristics of the available road network. Details were also assembled on population distribution and ground-water vulnerability, thus providing a basis for evaluating the potential consequences of a waste spillage during transport. Four routing scenarios were implemented to identify sections of road which consistently saw heavy traffic. These simulations also highlighted that some interventions could lead to risk tradeoffs rather than hazard mitigation. Many parts of the research would not have been possible without a GIS, and the study demonstrates the considerable potential of such software in environmental risk assessment and management.     

基于交易成本理论的IT外包风险控制策略研究综述

IT外包在过去10年中迅速发展,但潜藏着巨大的风险.为了更好地管理风险,对现有研究成果做了全面的梳理、归纳和提炼,将风险控制策略进一步分类为2个子系统:①主要策略子系统,包括合同机制、关系治理、组织整合、控制机制和动态成本监控;②辅助策略子系统,包括引入第三方专业机构、外包决策优化、不同的管理模式、标准化工具.这2个子系统为建立企业ITOR策略研究的方法论提供了良好的基础,有助于丰富企业ITOR管理理论.     

弥补创新的中间断层——以华南理工大学工研院为例

目前我国产学研合作创新面临的中间断层危机很大程度上制约着企业技术创新能力的构建与可持续性竞争力的形成。本文基于产学研合作、知识转移、区域创新系统等理论,通过剖析珠三角区域创新系统内,华南理工大学工研院在产学研合作中充当的三重角色、知识转移模式和内外合作网络的架构,提出我国区域创新系统高校产学研合作创新的对策:(1)创业型大学是三螺旋模型的助推器,应该积极推动高校作为产学研合作的组织者和连接官产学研合作的纽带与桥梁;(2)提出嵌入企业学习机理的知识转移模式;(3)产学研合作组织者的关系镶嵌与网络能力是弥补产学研创新断层的关键。     

民营企业管理机制运作实效研究--对美的集团管理机制的S调查案例

民营企业的竞争优势在于管理机制的创新与灵活性,本文在对广东美的集团2767份问卷调查基础上,从实证角度研究了民营企业中各种管理机制对员工产生的影响和作用;管理机制作为企业文化要素,被企业员工认同的程度;对比分析企业高层员工和基层员工对各种管理机制的不同感受.