Resilience of Complex Adaptive Systems: A Pedagogical Framework for Engineering Education and Research
Resilience of Complex Adaptive Systems: A Pedagogical Framework for Engineering Education and Research
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DOI：
10.1115/1.4046853
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
T. Agami Reddy
中科院分区：
文献类型：
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作者：
T. Agami Reddy
关于复原力的论述目前处于各种领域研究和实施的最前沿，由于其多学科/空间/时间性质而令人困惑。复原力分析是一门学科，它允许评估和加强系统在受到短暂的高影响外部冲击导致部分或完全失败时的应对和恢复行为。本文，意味着教学和研究制定，首先提供了一个概述的不同方面的弹性一般，然后集中在社区和地区，是复杂的适应系统（CAS）涉及多个工程基础设施提供必要的服务，当地居民和适应现有的自然资源和社会需求。其次，为了进行客观分析和评估，建议用四个不同的可量化的子属性来表征复原力。然后，本文介绍了标准的技术中心的方式，在不同的时间阶段，在灾害发生后，一般可视化和分析，并讨论了这些与可靠性和风险分析。随后，描述了两种流行的框架类型，并对代表性文献进行了综述：（i）旨在透过主观方法等软性方法，提高整体复原力的措施（访谈，叙述）和普查数据，以及（ii）旨在使用硬/客观方法（如数据驱动的分析和性能预测建模方法）在某些威胁情景下增强特定复原力的数据，类似于运筹学中的资源分配问题。最后，迫切需要研究一个综合框架;一个有可能将两种方法的长处联合收割机结合起来的框架。
The discourse on resilience, currently at the forefront of research and implementation in a wide variety of fields, is confusing because of its multi-disciplinary/spatial/temporal nature. Resilience analysis is a discipline that allows the assessment and enhancement of the coping and recovery behaviors of systems when subjected to short-lived high-impact external shocks leading to partial or complete failure. This paper, meant for pedagogical teaching and research formulation, starts by providing an overview of different aspects of resilience in general and then focuses on communities and regions that are complex adaptive systems (CAS) involving multiple engineered infrastructures providing essential services to local inhabitants and adapted to available natural resources and social requirements. Next, for objective analysis and assessment, it is proposed that resilience be characterized by four different quantifiable sub-attributes. This paper then describes the standard technocentric manner in which different temporal phases during and in the aftermath of disasters are generally visualized and analyzed, and discusses how these relate to reliability and risk analyses. Subsequently, two prevalent types of frameworks are described and representative literature reviewed: (i) those that aim at improving general resilience via soft methods such as subjective means (interviews, narratives) and census data, and (ii) those that are meant to enhance specific resilience under certain threat scenarios using hard/objective methods such as data-driven analysis and performance-predictive modeling methods, akin to resource allocation problems in operations research. Finally, the need for research into an integrated framework is urged; one that could potentially combine the strengths of both approaches.