skip to main content


Title: Resilience of Complex Adaptive Systems: A Pedagogical Framework for Engineering Education and Research
Abstract 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.  more » « less
Award ID(s):
1832678
NSF-PAR ID:
10191726
Author(s) / Creator(s):
Date Published:
Journal Name:
ASME Journal of Engineering for Sustainable Buildings and Cities
Volume:
1
Issue:
2
ISSN:
2642-6641
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The 2030 Global Sustainable Development Agenda of United Nations highlighted the critical importance of understanding the integrated nature between enhancing infrastructure resilience and facilitating social equity. Social equity is defined as equal opportunities provided to different people by infrastructure. It addresses disparities and unequal distribution of goods, services, and amenities. Infrastructure resilience is defined as the ability of infrastructure to withstand, adapt, and quickly recover from disasters. Existing research shows that infrastructure resilience and social equity are closely related to each other. However, there is a lack of research that explicitly understands the complex relationships between infrastructure resilience and social equity. To address this gap, this study aims to examine such interrelationships using social media data. Social media data is increasingly being used by researchers and proven to be a reliable source of valuable information for understanding human activities and behaviors in a disaster setting. The spatiotemporal distribution of disaster-related messages helps with real-time and quick assessment of the impact of disasters on infrastructure and human society across different regions. Using social media data also offers the advantages of saving time and cost, compared to other traditional data collection methods. As a first step of this study, this paper presents our work on collecting and analyzing the Twitter activities during 2018 Hurricane Michael in disaster-affected counties of Florida Panhandle area. The collected Twitter data was organized based on the geolocations of affected counties and was compared against the infrastructure resilience and social equity data of the affected counties. The results of the analysis indicate that (1) Twitter activities can be used as an important indicator of infrastructure resilience conditions, (2) socially vulnerable populations are not as active as general populations on social media in a disaster setting, and (3) vulnerable populations require a longer time for disaster recovery. 
    more » « less
  2. Over the past two decades, researchers have searched for methods to better understand the relationship between coral hosts and their microbiomes. Data on how coral-associated bacteria are involved in their host’s responses to stressors that cause bleaching, disease, and other deleterious effects can elucidate how they may mediate, ameliorate, and exacerbate interactions between the coral and the surrounding environment. At the same time tracking coral bacteria dynamics can reveal previously undiscovered mechanisms of coral resilience, acclimatization, and evolutionary adaptation. Although modern techniques have reduced the cost of conducting high-throughput sequencing of coral microbes, to explore the composition, function, and dynamics of coral-associated bacteria, it is necessary that the entire procedure, from collection to sequencing, and subsequent analysis be carried out in an objective and effective way. Corals represent a difficult host with which to work, and unique steps in the process of microbiome assessment are necessary to avoid inaccuracies or unusable data in microbiome libraries, such as off-target amplification of host sequences. Here, we review, compare and contrast, and recommend methods for sample collection, preservation, and processing (e.g., DNA extraction) pipelines to best generate 16S amplicon libraries with the aim of tracking coral microbiome dynamics. We also discuss some basic quality assurance and general bioinformatic methods to analyze the diversity, composition, and taxonomic profiles of the microbiomes. This review aims to be a generalizable guide for researchers interested in starting and modifying the molecular biology aspects of coral microbiome research, highlighting best practices and tricks of the trade. 
    more » « less
  3. Disasters are becoming more frequent as the global climate changes, and recovery efforts require the cooperation and collaboration of experts and community members across disciplines. The DRRM program, funded through the National Science Foundation (NSF) Research Traineeship (NRT), is an interdisciplinary graduate program that brings together faculty and graduate students from across the university to develop new, transdisciplinary ways of solving disaster-related issues. The core team includes faculty from business, engineering, education, science, and urban planning fields. The overall objective of the program is to create a community of practice amongst the graduate students and faculty to improve understanding and support proactive decision-making related to disasters and disaster management. The specific educational objectives of the program are (1) context mastery and community building, (2) transdisciplinary integration and professional development, and (3) transdisciplinary research. The program’s educational research and assessment activities include program development, trainee learning and development, programmatic educational research, and institutional transformation. The program is now in its fourth year of student enrollment. Core courses on interdisciplinary research methods in disaster resilience are in place, engaging students in domain-specific research related to natural hazards, resilience, and recovery, and in methods of interdisciplinary and transdisciplinary collaboration. In addition to courses, the program offers a range of professional development opportunities through seminars and workshops. Since the program’s inception, the core team has expanded both the numbers of faculty and students and the range of academic disciplines involved in the program, including individuals from additional science and engineering fields as well as those from natural resources and the social sciences. At the same time, the breadth of disciplines and the constraints of individual academic programs have posed substantial structural challenges in engaging students in the process of building interdisciplinary research identities and in building the infrastructure needed to sustain the program past the end of the grant. Our poster and paper will identify major program accomplishments, but also draw on interviews with students to examine the structural challenges and potential solution paths associated with a program of this breadth. Critical opportunities for sustainability and engagement have emerged through integration with a larger university-level center as well as through increased flexibility in program requirements and additional mechanisms for student and faculty collaboration. 
    more » « less
  4. null (Ed.)
    Abstract The Electron Loss and Fields Investigation with a Spatio-Temporal Ambiguity-Resolving option (ELFIN-STAR, or heretoforth simply: ELFIN) mission comprises two identical 3-Unit (3U) CubeSats on a polar (∼93 ∘ inclination), nearly circular, low-Earth (∼450 km altitude) orbit. Launched on September 15, 2018, ELFIN is expected to have a >2.5 year lifetime. Its primary science objective is to resolve the mechanism of storm-time relativistic electron precipitation, for which electromagnetic ion cyclotron (EMIC) waves are a prime candidate. From its ionospheric vantage point, ELFIN uses its unique pitch-angle-resolving capability to determine whether measured relativistic electron pitch-angle and energy spectra within the loss cone bear the characteristic signatures of scattering by EMIC waves or whether such scattering may be due to other processes. Pairing identical ELFIN satellites with slowly-variable along-track separation allows disambiguation of spatial and temporal evolution of the precipitation over minutes-to-tens-of-minutes timescales, faster than the orbit period of a single low-altitude satellite (T orbit ∼ 90 min). Each satellite carries an energetic particle detector for electrons (EPDE) that measures 50 keV to 5 MeV electrons with $\Delta $ Δ E/E < 40% and a fluxgate magnetometer (FGM) on a ∼72 cm boom that measures magnetic field waves (e.g., EMIC waves) in the range from DC to 5 Hz Nyquist (nominally) with <0.3 nT/sqrt(Hz) noise at 1 Hz. The spinning satellites (T spin $\,\sim $ ∼ 3 s) are equipped with magnetorquers (air coils) that permit spin-up or -down and reorientation maneuvers. Using those, the spin axis is placed normal to the orbit plane (nominally), allowing full pitch-angle resolution twice per spin. An energetic particle detector for ions (EPDI) measures 250 keV – 5 MeV ions, addressing secondary science. Funded initially by CalSpace and the University Nanosat Program, ELFIN was selected for flight with joint support from NSF and NASA between 2014 and 2018 and launched by the ELaNa XVIII program on a Delta II rocket (with IceSatII as the primary). Mission operations are currently funded by NASA. Working under experienced UCLA mentors, with advice from The Aerospace Corporation and NASA personnel, more than 250 undergraduates have matured the ELFIN implementation strategy; developed the instruments, satellite, and ground systems and operate the two satellites. ELFIN’s already high potential for cutting-edge science return is compounded by concurrent equatorial Heliophysics missions (THEMIS, Arase, Van Allen Probes, MMS) and ground stations. ELFIN’s integrated data analysis approach, rapid dissemination strategies via the SPace Environment Data Analysis System (SPEDAS), and data coordination with the Heliophysics/Geospace System Observatory (H/GSO) optimize science yield, enabling the widest community benefits. Several storm-time events have already been captured and are presented herein to demonstrate ELFIN’s data analysis methods and potential. These form the basis of on-going studies to resolve the primary mission science objective. Broad energy precipitation events, precipitation bands, and microbursts, clearly seen both at dawn and dusk, extend from tens of keV to >1 MeV. This broad energy range of precipitation indicates that multiple waves are providing scattering concurrently. Many observed events show significant backscattered fluxes, which in the past were hard to resolve by equatorial spacecraft or non-pitch-angle-resolving ionospheric missions. These observations suggest that the ionosphere plays a significant role in modifying magnetospheric electron fluxes and wave-particle interactions. Routine data captures starting in February 2020 and lasting for at least another year, approximately the remainder of the mission lifetime, are expected to provide a very rich dataset to address questions even beyond the primary mission science objective. 
    more » « less
  5. d. Many of the infrastructure sectors that are considered to be crucial by the Department of Homeland Security include networked systems (physical and temporal) that function to move some commodity like electricity, people, or even communication from one location of importance to another. The costs associated with these flows make up the price of the network’s normal functionality. These networks have limited capacities, which cause the marginal cost of a unit of flow across an edge to increase as congestion builds. In order to limit the expense of a network’s normal demand we aim to increase the resilience of the system and specifically the resilience of the arc capacities. Divisions of critical infrastructure have faced difficulties in recent years as inadequate resources have been available for needed upgrades and repairs. Without being able to determine future factors that cause damage both minor and extreme to the networks, officials must decide how to best allocate the limited funds now so that these essential systems can withstand the heavy weight of society’s reliance. We model these resource allocation decisions using a two-stage stochastic program (SP) for the purpose of network protection. Starting with a general form for a basic two-stage SP, we enforce assumptions that specify characteristics key to this type of decision model. The second stage objective—which represents the price of the network’s routine functionality—is nonlinear, as it reflects the increasing marginal cost per unit of additional flow across an arc. After the model has been designed properly to reflect the network protection problem, we are left with a nonconvex, nonlinear, nonseparable risk-neutral program. This research focuses on key reformulation techniques that transform the problematic model into one that is convex, separable, and much more solvable. Our approach focuses on using perspective functions to convexify the feasibility set of the second stage and second order conic constraints to represent nonlinear constraints in a form that better allows the use of computational solvers. Once these methods have been applied to the risk-neutral model we introduce a risk measure into the first stage that allows us to control the balance between an efficient, solvable model and the need to hedge against extreme events. Using Benders cuts that exploit linear separability, we give a decomposition and solution algorithm for the general network model. The innovations included in this formulation are then implemented on a transportation network with given flow demand 
    more » « less