There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Communities have the potential to function effectively and adapt successfully in the aftermath of disasters. Drawing upon literatures in several disciplines, we present a theory of resilience that encompasses contemporary understandings of stress, adaptation, wellness, and resource dynamics. Community resilience is a process linking a network of adaptive capacities (resources with dynamic attributes) to adaptation after a disturbance or adversity. Community adaptation is manifest in population wellness, defined as high and non-disparate levels of mental and behavioral health, functioning, and quality of life. Community resilience emerges from four primary sets of adaptive capacities—Economic Development, Social Capital, Information and Communication, and Community Competence—that together provide a strategy for disaster readiness. To build collective resilience, communities must reduce risk and resource inequities, engage local people in mitigation, create organizational linkages, boost and protect social supports, and plan for not having a plan, which requires flexibility, decision-making skills, and trusted sources of information that function in the face of unknowns.

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Community Resilience as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness

There is considerable research interest on the meaning and measurement of resilience from a variety of research perspectives including those from the hazards/disasters and global change communities. The identification of standards and metrics for measuring disaster resilience is one of the challenges faced by local, state, and federal agencies, especially in the United States. This paper provides a new framework, the disaster resilience of place (DROP) model, designed to improve comparative assessments of disaster resilience at the local or community level. A candidate set of variables for implementing the model are also presented as a first step towards its implementation.

A place-based model for understanding community resilience to natural disasters

Preface Acknowledgements Introduction: Modern wind energy and its origins Modern wind turbines History of wind energy Wind characteristics and resources Introduction General characteristics of the wind resource Characteristics of the atmospheric boundary layer Wind data analysis and resource estimation Wind turbine energy production estimates using statistical techniques Overview of available resource assessment data Wind measurements and instrumentation Advanced topics Aerodynamics of wind turbines General overview One-dimensional momentum theory and the Betz limit Ideal horizontal axis wind turbing with wake rotation' Airfoils and general concepts of aerodynamics Momentum theory and blade element theory Blade shape for ideal rotor without wake rotation General rotor blade shape performance prediction Blade shape for optimum rotor with wake rotation Generalized rotor design procedure Simplified HAWT rotor performance calculation procedure Effect of drag and blade number on optimum performance Advanced aerodynamic topics Mechanics and dynamics Wind turbine rotor dynamics Detailed and specialized dynamic models Electrical aspects of wind turbines Basic concepts of electric power Power transformers Electrical machines Power converters Ancillary electrical equipment Wind turbine design Design procedure Wind turbine topologies Materials Machine elements Wind turbine loads Wind turbine subsystems and components Design evaluation Power curve prediction Wind turbine loads Wind turbine subsystems and components Design evaluation Power curve prediction Wind turbine control Overview of wind turbine control systems Typical grid-connected turbine operation Supervisory control overview and implementation Dynamic control theory and implementation Wind turbine siting, system design and integration Wind turbine siting Installation and operation issues Wind farms Wind turbines and wind farms in electric grids Offshore wind farms Operation in severe climates Hybrid electrical systems Wind energy system economics Overview of economic assessment of wind energy systems Capital costs of wind energy systems Operation and maintenance costs Value of wind energy Economic analysis methods Wind energy market considerations Wind energy systems: environmental aspects and impacts Avian interaction with wind trubines Visual impact of wind turbines Wind turbine noise Electromagnetic interference effects Land-use environmental impacts Other environmental considerations Nomenclature Problems Index

Wind energy explained : theory, design, and application

Global sensitivity analysis using polynomial chaos expansions

This paper presents a conceptual framework to define seismic resilience of communities and quantitative measures of resilience that can be useful for a coordinated research effort focusing on enhancing this resilience. This framework relies on the complementary measures of resilience: ‘‘Reduced failure probabilities,’’ ‘‘Reduced consequences from failures,’’ and ‘‘Reduced time to recovery.’’ The framework also includes quantitative measures of the ‘‘ends’’ of robustness and rapidity, and the ‘‘means’’ of resourcefulness and redundancy, and integrates those measures into the four dimensions of community resilience—technical, organizational, social, and economic—all of which can be used to quantify measures of resilience for various types of physical and organizational systems. Systems diagrams then establish the tasks required to achieve these objectives. This framework can be useful in future research to determine the resiliency of different units of analysis and systems, and to develop resiliency targets and detailed analytical procedures to generate these values. [DOI: 10.1193/1.1623497]

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A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities

Digital simulation of random processes and its applications

Simulation of Stochastic Processes by Spectral Representation

This report presents methods of bridge fragility curve development on the basis of statistical analysis. Both empirical and analytical fragility curves are considered. The empirical fragility curves are developed utilizing bridge damage data obtained from past earthquakes, particularly the 1994 Northridge and 1995 Hyogo-ken Nanbu (Kobe) earthquakes. Analytical fragility curves are constructed for typical bridges in the Memphis, Tennessee area utilizing nonlinear dynamic analysis. Two-parameter lognormal distribution functions are used to represent the fragility curves. These two parameters (referred to as fragility parameters) are estimated by two distinct methods. The first method is more traditional and uses the maximum likelihood procedure treating each event of bridge damage as a realization from a Bernoulli experiment. The second method is unique in that it permits simultaneous estimation of the fragility parameters of the family of fragility curves, each representing a particular state of damage, associated with a population of bridges. The method still utilizes the maximum likelihood procedure, however, each event of bridge damage is treated as a realization from a multi-outcome Bernoulli type experiment. These two methods of parameter estimation are used for each of the populations of bridges inspected for damage after the Northridge and Kobe earthquakes and with numerically simulated damage for the population of typical Memphis area bridges. Corresponding to these two methods of estimation, this report introduces statistical procedures for testing goodness of fit of the fragility curves and of estimating the confidence intervals of the fragility parameters. Some preliminary evaluations are made on the significance of the fragility curves developed as a function of ground intensity measures other than PGA. Furthermore, applications of fragility curves in the seismic performance estimation of expressway network systems are demonstrated. Exploratory research was performed to compare the empirical and analytical fragility curves developed in the major part of this report with those constructed utilizing the nonlinear static method currently promoted by the profession in conjunction with performance-based structural design. The conceptual and theoretical treatment discussed herein is believed to provide a theoretical basis and practical analytical tools for the development of fragility curves, and their application in the assessment of seismic performance of expressway network systems.

Statistical analysis of fragility curves

This paper presents a statistical analysis of structural fragility curves. Both empirical and analytical fragility curves are considered. The empirical fragility curves are developed utilizing bridge damage data obtained from the 1995 Hyogo-ken Nanbu (Kobe) earthquake. The analytical fragility curves are constructed on the basis of the nonlinear dynamic analysis. Two-parameter lognormal distribution functions are used to represent the fragility curves with the parameters estimated by the maximum likelihood method. This paper also presents methods of testing the goodness of fit of the fragility curves and estimating the confidence intervals of the two parameters (median and log-standard deviation) of the distribution. An analytical interpretation of randomness and uncertainty associated with the median is provided.

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Masanobu Shinozuka

Papers

A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities

Digital simulation of random processes and its applications

Simulation of Stochastic Processes by Spectral Representation

Statistical analysis of fragility curves

Statistical Analysis of Fragility Curves