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Disaster recovery is considered one of the less-understood phases of the disaster cycle. In particular, the literature on lifeline infrastructure restoration modeling frequently mentions th...

A major challenge in enhancing the resilience of communities stems from current approaches used to identify needs and strategies that build the capacity of jurisdictions to mitigate loss and improve recovery. A new generation of resilience-based planning processes has emerged in the last several years that integrate goals of community well-being and identity into recovery-based performance measurement frameworks. Specific tools and refined guidance are needed to facilitate evidence-based development of recovery estimates. This article presents the participatory modeling process, a planning system designed to develop recovery-based resilience measurement frameworks for community resilience planning initiatives. Stakeholder engagement is infused throughout the participatory modeling process by integrating disaster recovery simulation modeling into community resilience planning. Within the process, participants get a unique opportunity to work together to deliberate on community concerns through facilitated participatory modeling. The participatory modeling platform combines the DESaster recovery simulation model and visual analytics interfaces. DESaster is an open source Python Library for creating discrete event simulations of disaster recovery. The simulation model was developed using a human-centered design approach whose goal is to be open, modular, and extensible. The process presented in this article is the first participatory modeling approach for analyzing recovery to aid creation of community resilience measurement frameworks.

The efficacy of various types of intervention measures intended to facilitate post-earthquake housing recovery can be evaluated ahead of time by using simulation models to quantify their benefits and tradeoffs. Towards this end, this paper presents a conceptual framework comprised of three components for modeling post-earthquake housing recovery. The modeling framework starts with a probabilistic assessment of building-level damage using recovery-based limit states that characterize post-earthquake functionality, inhabitability, and repairability. These limit states are the basis for the second component, which includes two different utility-based models for representing post-earthquake household decision making. Stochastic models to probabilistically quantify building-level recovery trajectories comprise the third and final component of the framework. Collectively, these alternative models can integrate the effect of building states, available resources, household decisions, and endogenous factors such as lifeline restoration. The modeling framework can be scaled to model spatiotemporal scenarios of housing recovery to inform jurisdictional-level policies, plans, and interventions to increase residential community resilience.

Natural hazards and disaster reconnaissance investigations have provided many lessons for the research and practice communities and have greatly improved our scientific understanding of extreme events. Yet, many challenges remain for these communities, including improving our ability to model hazards, make decisions in the face of uncertainty, enhance community resilience, and mitigate risk. State-of-the-art instrumentation and mobile data collection applications have significantly advanced the ability of field investigation teams to capture quickly perishable data in post-disaster settings. The NHERI RAPID Facility convened a community workshop of experts in the professional, government, and academic sectors to determine reconnaissance data needs and opportunities, and to identify the broader challenges facing the reconnaissance community that hinder data collection and use. Participants highlighted that field teams face many practical and operational challenges before and during reconnaissance investigations, including logistics concerns, safety issues, emotional trauma, and after-returning, issues with data processing and analysis. Field teams have executed many effective missions. Among the factors contributing to successful reconnaissance are having local contacts, effective teamwork, and pre-event training. Continued progress in natural hazard reconnaissance requires adaptation of new, strategic approaches that acquire and integrate data over a range of temporal, spatial, and social scales across disciplines. U.S. National Science FoundationNational Science Foundation (NSF) [1611820] The U.S. National Science Foundation supported this work under grant number 1611820. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

In 2016, the National Science Foundation (NSF) funded a multi-institution interdisciplinary team to develop and operate the Natural Hazards Reconnaissance Facility (known as the "RAPID") as part of the Natural Hazards Engineering Research Infrastructure (NHERI) program. During the following 2 years, the RAPID facility developed its instrumentation portfolio and operational plan with input from the natural hazards community, the facility's leadership team, and an external steering committee. In September 2018, the RAPID began field operations, which continue today and include instrumentation, software, training, and support services to conduct reconnaissance research before, during, and after natural hazard and disaster events. Over the past 2 years, the RAPID has supported the data collection efforts for over 60 projects worldwide. Projects have spanned a wide range of disciplines and hazards and have also included data collection at large-scale experimental facilities in the United States and abroad. These projects have produced an unprecedented amount of high-quality field data archived on the DesignSafe cyberinfrastructure platform. This paper describes the RAPID facility's development, instrumentation portfolio (including the mobile application RApp), services and capabilities, and training activities. Additionally, overviews of three recent RAPID-supported projects are presented, including descriptions of field data collection workflows, details of the resulting data sets, and the impact of these project deployments on the natural hazard fields. NSFNational Science Foundation (NSF) [1904653, 1904327, CMMI: 1611820]; NSF through GEER [1826118]; Oregon DOT; FHWA [SPR807] The RAPID Facility operates under a cooperative agreement with the NSF under Award No. CMMI: 1611820. Research on the performance of LRLVBs in Hurricane Michael was supported by the NSF under award nos. 1904653 and 1904327. Research on the flow slide during the Palu, Indonesia earthquake was supported by the NSF through GEER under award number 1826118. Any opinions, findings, conclusions, and recommendations presented in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation. Funding for the Hooskadaden Landslide case study were provided by Oregon DOT and FHWA (SPR807).

The U.S. National Institute of Standards and Technology (NIST)’s Community Resilience Planning Guide uses recovery times of infrastructure functions as key metrics for disaster resilience. The existing literature also widely uses the recovery curve and the area under it to measure infrastructure resilience. Therefore, infrastructure recovery curve estimation is critical to understanding and improving disaster resilience. Unfortunately, this process is challenging in the pre-event planning context due to lack of historical data. To bridge this gap, we consider a situation where infrastructure experts are asked to estimate the time for different infrastructure systems to recover to certain functionality levels after a scenario hazard event. We propose a methodological framework to use expert-elicited data to estimate the expected recovery time curve of a particular infrastructure system. This framework uses the Gaussian process regression (GPR) to capture the experts’ estimation-uncertainty and satisfy known physical constraints of recovery processes. The framework is designed to find a balance between the data collection cost of expert elicitation and the prediction accuracy of GPR. We evaluate the framework on simulated expert-elicited data concerning two case study events, the 1995 Great Hanshin-Awaji Earthquake and the 2011 Great East Japan Earthquake. It is shown that the framework is robust against different configurations such as the number of experts, how the quantities of interest are elicited, and uncertainty in the experts’ estimates.

Changes in levels and patterns of physical activity might be a mechanism to assess and inform disaster recovery through the lens of wellbeing. However, few studies have examined disaster impacts on physical activity or the potential for physical activity to serve as an indicator of disaster recovery. In this exploratory study, we examined daily bicycle and pedestrian counts from four public bicycle/pedestrian trails in Houston, before and after Hurricane Harvey landfall, to assess if physical activity returned to pre-Harvey levels. An interrupted time series analysis was conducted to examine the immediate impact of Harvey landfall on physical activity, t-tests were performed to assess if trail usage returned to pre-Harvey levels. Hurricane Harvey was found to have a significant negative impact on daily pedestrian and bicycle counts for three of the four trails. Daily pedestrian and bicycle counts were found to return to pre-Harvey or higher levels at 6 weeks post-landfall at all locations studied. We discuss the potential for further research to examine the trends, feasibility, validity, and limitations of using bicycle and pedestrian use levels as a proxy for disaster recovery and wellbeing among affected populations.

The goal of this paper is to facilitate a community of practice for disaster recovery modeling. This community should include hazard and disaster researchers without modeling experience and...

A growing body of literature suggests that restrictive public health measures implemented to control COVID-19 have had negative impacts on physical activity. We examined how Stay Home orders in Houston, New York City, and Seattle impacted outdoor physical activity patterns, measured by daily bicycle and pedestrian count data. We assessed changes in activity levels between the period before and during Stay Home orders. Across all three cities, we found significant changes in bicycle and pedestrian counts from the period before to the period during Stay Home orders. The direction of change varied by location, likely due to differing local contexts and outbreak progression. These results can inform policy around the use of outdoor public infrastructure as the COVID-19 pandemic continues.

The goal of this study was to first develop a systematic data collection strategy to create and maintain a database of policies in the United States that promote seismic mitigation. With this database, indices were developed to facilitate understanding of the relationship between policy activity, policy coverage, and seismic risk, as well as to perform cross-state comparison. The most basic index is a count of the number of policies each state has adopted. An index was developed that directly accounts for the seismic risk of each state. A Guttman scale was developed to characterize states’ policy coverage, as well as the level of intensity represented by states’ policy activity. The relationship between policy coverage and seismic risk was also explored. For the most part, this study found a moderate relationship between seismic risk and policy activity, as well as a moderately strong relationship between risk and policy coverage.

We are developing a new natural language processing (NLP) method to facilitate analysis of text corpora that describe long-term recovery. The aim of the method is to allow users to measure the degree that user-specified propositions about potential issues are embodied within the corpora, serving as a proxy for the disaster recovery process. The presented method employs a statistical syntax-based semantic matching model and was trained on a standard, publicly available training dataset. We applied the NLP method to a news story corpus that describes the recovery of Christchurch, New Zealand after the 2010–2011 Canterbury earthquake sequence. We used the model to compute semantic measurements of multiple potential recovery issues as expressed in the Christchurch news corpus that span 2011 to 2016. We evaluated method outputs through a user study involving twenty professional emergency managers. User study results show that the model can be effective when applied to a disaster-related news corpus. 85% of study participants were interested in a way to measure recovery issue propositions in news or other corpora. We are encouraged by the potential for future applications of our NLP method for after-action learning, recovery decision making, and disaster research.

Critical infrastructure is vulnerable to a broad range of hazards. Timely and effective recovery of critical infrastructure after extreme events is crucial. However, critical infrastructure disaster recovery planning is complicated and involves both domain-and user-centered characteristics and complexities. Recovery planning currently uses few quantitative computer-based tools and instead largely relies on expert judgment. Simulation modeling can simplify domain-centered complexities but not the human factors. Conversely, human-centered design places end-users at the center of design. We discuss the benefits of combining simulation modeling with human-centered design and refer it as human-centered simulation modeling. Human-centered simulation modeling has the capability to make recovery planning simpler and more understandable for critical infrastructure and emergency management experts and other recovery planning decision-makers. We qualitatively analyzed several resilience planning initiatives, post-disaster recovery assessments, and relevant journal articles to understand experts and decision-makers' perspectives. We propose a conceptual design framework for creating human-centered simulation models for critical infrastructure disaster recovery planning. This framework consists of three constructs: 1) user interaction with design features that end-users interact with, including model parameters assignment, decision-making support, task queries, and usability; 2) system representation that refers to system components, system interactions, and system state variables; and 3) computation core that represents computational methods required to perform processes.

The devastating 2017 Puebla quake provides an opportunity to assess how citizens perceive and use the Mexico City earthquake early warning system.

If community disaster resilience is to mature into a robust and lasting area of research, methodologically facilitated dialogue between empirical observations and theory is necessary. However, methodological and empirical research has outpaced community disaster resilience theory. To address this gap, a theoretical framework called WISC is presented. WISC is named after four constructs of the framework: well-being, identity, services, and capitals. WISC relates the two concepts of community and infrastructure, broadly defined, to the four constructs it is named after. The 4 constructs are respectively defined by 29 variables. The broadest interpretation of WISC is that infrastructure supports and facilitates components of community within human settlements. Infrastructure is represented as combinations of capitals and services; community is represented by connections of identity and well-being. Ultimately, well-being of a community is dependent on that community's collective capital. But these two constru...

Prior empirical research has demonstrated that the decisions of affected populations can significantly influence housing recovery outcomes following a natural hazard event. The current study seeks to develop an integrated post-earthquake recovery model that explicitly accounts for household decision-making. An empirical probabilistic utility-based decision model is developed using data from a survey of Los Angeles households. The results from a multinomial logistic regression showed that the time in residence, neighborhood evacuation level, physical damage to residence, duration of utility disruption and loss of access to the building, household income and earthquake insurance coverage had a statistically significant association with homeowners’ decisions. For renter decision-making, only physical damage to the residence and duration of utility disruption are found to be statistically significant. In addition to household decision-making, the integrated model incorporates probabilistic building performance assessment and a discrete-state stochastic process representation of post-earthquake housing recovery. The results from a case study incorporating three Los Angeles neighborhoods (Koreatown, East Hollywood and Lomita) show that the influence of household decision-making on occupancy-based recovery trajectories is amplified as the scale of damage increases.

Models of hazards and disasters are increasingly used to help to develop disaster-related policies and plans. Unfortunately, readily available modelling tools, such as the U.S. Federal Emergency Management Agency's Hazus model provide inadequate representation of housing recovery. Most disaster-related housing models are focused on the immediate impacts of earthquakes in terms of physical damage and economic losses. This paper introduces a new simulation modelling programming library called DESaster (discrete event simulation of disaster recovery) for building disaster recovery simulation models, which currently focuses exclusively on housing recovery. A brief overview of DESaster is provided prior to describing its application to model housing recovery after the 2015 Nepal earthquake. The DESaster library was found to be flexible enough to construct a specific model of housing recovery in Nepal. More specifically, it was found to be particularly useful for better understanding resource needs, such as construction materials and skilled labourers, through comparison of different allocation scenarios.

An integrated community resilience index (CRI) quantifies the status, exposure, and recovery of the physical, economic, and socio-cultural capital for a specific target community. However, most CRIs do not account for the recovery of ecosystem functioning after extreme events, even though many aspects of a community depend on the services provided by the natural environment. The primary goal of this study was to monitor the recovery of ecosystem functionality (ecological capital) using remote sensing-derived gross primary production (GPP) as an indicator of ‘ecosystem-wellness’ and assess the effect of resilience of ecological capital on the recovery of a community via an integrated CRI. We developed a measure of ecosystem resilience using remotely sensed GPP data and applied the modeling prototype ResilUS in a pilot study for a four-parish coastal community in southwestern Louisiana, USA that was impacted by Hurricane Rita in 2005. The results illustrate that after such an extreme event, the recovery of ecological capital varies according to land use type and may take many months to return to full functionality. This variable recovery can potentially impact the recovery of certain businesses that rely heavily on ecosystem services such as agriculture, forestry, fisheries, and tourism.

In August 2012, Hurricane Isaac made landfall twice in Louisiana. Cumulatively, over 1 million customers lost electricity as a result of the hurricane, some for more than 10 days. As a disaster, Hurricane Isaac is relatively unique because of the opportunity to largely isolate impacts and decisions directly associated with the electricity outage and restoration from cascading impacts related to direct flood or wind damage. Louisiana emergency managers, business representatives, and public officials were interviewed to develop and analyze a case study of the outage and restoration event. Interviewees were asked about (1) the significant impacts from the outage, (2) what (if any) lasting effects would result from the outage, (3) the relative performance of power restoration, (4) the effectiveness of communication between stakeholders, and (5) factors influencing the public and political perception of restoration performance. Other collected data included content from news media, government documents...

Exposure to galactic cosmic radiation is a potential health risk in long-term space travel and represents a significant risk to the central nervous system. The most harmful component of galactic cosmic radiation is the HZE [high mass, highly charged (Z), high energy] particles, e.g., (56)Fe particle. In previous ground-based experiments, exposure to doses of HZE-particle radiation that an astronaut will receive on a deep space mission (i.e., ∼20 cGy) resulted in pronounced deficits in hippocampus-dependent learning and memory in rodents. Neurocognitive tasks that are dependent upon other regions of the brain, such as the striatum, are also impaired after exposure to low HZE-particle doses. These data raise the possibility that neurocognitive tasks regulated by the prefrontal cortex could also be impaired after exposure to mission relevant HZE-particle doses, which may prevent astronauts from performing complex executive functions. To assess the effects of mission relevant (20 cGy) doses of 1 GeV/u (56)Fe particles on executive function, male Wistar rats received either sham treatment or were irradiated and tested 3 months later for their ability to perform attentional set shifting. Compared to the controls, rats that received 20 cGy of 1 GeV/u (56)Fe particles showed significant impairments in their ability to complete the attentional set-shifting test, with only 17% of irradiated rats completing all stages as opposed to 78% of the control rats. The majority of failures (60%) occurred at the first reversal stage, and half of the remaining animals failed at the extra-dimensional shift phase of the studies. The irradiated rats that managed to complete the tasks did so with approximately the same ease as did the control rats. These observations suggest that exposure to mission relevant doses of 1 GeV/u (56)Fe particles results in the loss of functionality in several regions of the cortex: medical prefrontal cortex, anterior cingulated cortex, posterior cingulated cortex and the basal forebrain. Our observation that 20 cGy of 1 GeV/u (56)Fe particles is sufficient to impair the ability of rats to conduct attentional set-shifting raises the possibility that astronauts on prolonged deep space exploratory missions could subsequently develop deficits in executive function.

The 12 January 2010 Haiti earthquake resulted in some of the most significant social impacts from an earthquake in recent decades. In early March, an Earthquake Engineering Research Institute (EERI) social impacts reconnaissance team documented broad impacts on shelter, livelihood opportunities, and service provision through direct observation, photo documentation, interviews, and a review of other sources. The earthquake left over half of the residential housing in Port-au-Prince heavily damaged; widespread fear that even undamaged structures were unsafe swelled the number of homeless to 1.3 million. The infrastructure damage and significant loss of life within the Haitian government and international aid agencies slowed the early response and reduced the already low levels of basic service provision. Continued research is needed on media portrayal, outcomes of injuries, sheltering decisions, social networks, and learning trajectories for Haiti's youth, among other areas.

A resilient community is one that does not experience serious degradation in critical services when a hazard occurs and, in the event of degradation or failure, recovers to a similar or better level of service in a reasonable amount of time. The most efficient means of making a community resilient is to make its critical services and capital robust – minimize damage/loss probability or the consequences from damage/loss through mitigation. If a community's critical services and capital are not robust, efforts must be put into recovery. Based on the measurable aspects of community capital, we have developed a simulation model called ResilUS that operationalizes community resilience across multiple, hierarchical scales in relation to a range of policy and decision variables associated with each scale. ResilUS is implemented using fragility curves to model loss and Markov chains to model recovery with respect to time. ResilUS was applied to the 1994 Northridge earthquake disaster in order to calibrate several...

The study described in this paper investigates the relative merits of two peer-reviewed earthquake-induced landslide models using participatory model assessment. The earthquake-induced landslide hazard models assessed are a simplified Newmark’s displacement model and a recently developed knowledge-based model. Participatory model assessment involves conducting facilitated participatory processes where the model(s) are used for aiding decisions within a socio-behavioral experiment designed for collecting data to evaluate formal hypotheses about the model(s). The paper sets out the design of the participatory model assessment—a series of workshops involving experts and potential model end-users that incorporated a roleplay site selection task. Quantitative data elicited using a set of entrance and exit questionnaires were analyzed to investigate hypotheses about the models. Participants found the knowledge-based model to be significantly more complete and more informative for their roleplay task. Overall, the two models did not yield significant differences with respect to issues such as task efficiency or task outcome satisfaction. Lastly, it was found that education level and disciplinary perspectives (of those analyzed) did not significantly affect outcomes, suggesting that a wide demographic of participants can be used for participatory model assessments. Additional research is needed to assess the models in different contexts, as well as more broadly developing a set of best practices for conducting participatory model assessments of other natural hazard and risk models intended to support decision-making.

This paper provides a review of regional-scale modeling of earthquake-induced landslide hazard with respect to the needs for disaster risk reduction and sustainable development. Based on this review, it sets out important research themes and suggests computing with words (CW), a methodology that includes fuzzy logic systems, as a fruitful modeling methodology for addressing many of these research themes. A range of research, reviewed here, has been conducted applying CW to various aspects of earthquake-induced landslide hazard zonation, but none facilitate comprehensive modeling of all types of earthquake-induced landslides. A new comprehensive areal model of earthquake-induced landslides (CAMEL) is introduced here that was developed using fuzzy logic systems. CAMEL provides an integrated framework for modeling all types of earthquake-induced landslides using geographic information systems. CAMEL is designed to facilitate quantitative and qualitative representation of terrain conditions and knowledge about these conditions on the likely areal concentration of each landslide type. CAMEL is highly modifiable and adaptable; new knowledge can be easily added, while existing knowledge can be changed to better match local knowledge and conditions. As such, CAMEL should not be viewed as a complete alternative to other earthquake-induced landslide models. CAMEL provides an open framework for incorporating other models, such as Newmark's displacement method, together with previously incompatible empirical and local knowledge.

A new comprehensive areal model of earthquake-induced landslides (CAMEL) has been developed to assist in planning decisions related to disaster risk reduction. CAMEL provides an integrated framework for modeling all types of earthquake-induced landslides using fuzzy logic systems and geographic information systems. CAMEL is designed to facilitate quantitative and qualitative representation of terrain conditions and knowledge about these conditions on the likely areal concentration of each landslide type. CAMEL has been empirically evaluated with respect to disrupted landslides (Category I) using a case study of the 1989 M = 6.9 Loma Prieta, CA earthquake. In this case, CAMEL performs best in comparison to disrupted slides and falls in soil. For disrupted rock fall and slides, CAMEL's performance was slightly poorer. The model predicted a low occurrence of rock avalanches, when none in fact occurred. A similar comparison with the Loma Prieta case study was also conducted using a simplified Newmark displacement model. The area under the curve method of evaluation was used in order to draw comparisons between both models, revealing improved performance with CAMEL. CAMEL should not however be viewed as a strict alternative to Newmark displacement models. CAMEL can be used to integrate Newmark displacements with other, previously incompatible, types of knowledge.

Community disaster resilience is commonly conceptualized as the capacity to reduce post-event loss and facilitate effective recovery. Technologies, such as data systems, computer models, and visualization tools, are more common and well developed for understanding immediate (and static) loss than for understanding dynamic processes of recovery. Most available technology for understanding post-disaster dynamics is specific to short-term emergency or crisis processes. As a result, development of simulation models of recovery is necessary to enable technology-supported decision making for realizing community disaster resilience. We present a proof of concept design for a home reconstruction discrete-event simulation (DES) to evaluate its potential for simulating disaster recovery in general. The design is implemented as a prototype using the SimPy discrete-event simulation Python library. Preliminary outputs from the prototype simulation suggest that DES is appropriate and promising for modeling home reconstruction. The ability to alter the quantities of shared resource stocks, event durations, and access qualifications can likely facilitate modeling of other types of recovery processes, as well as a variety of post-disaster scenarios. As such, DES appears to be a novel technological approach that can be developed to support pre-and post-disaster decision making for improved community disaster resilience.

This paper sets out the foundations for developing robust models of community recovery from earthquake disasters. Models that anticipate post-disaster trajectories are complementary to loss estimation models that predict damage and loss. Such models can serve as important decision support tools for increasing community resilience and reducing disaster vulnerability. The paper first presents a comprehensive conceptual model of recovery. The conceptual model enumerates important relationships between a community's households, businesses, lifeline networks, and neighborhoods. The conceptual model can be operationalized to create a numerical model of recovery. To demonstrate this, we present a prototype computer simulation model and graphical user interface. As the model is intended for decision support, it is important to involve potential users in model development. We conducted a focus group involving Puget Sound, Washington, area disaster management practitioners to elicit local insight about community recovery and model development needs, using the prototype as stimulus. Important focus group issues included potential model inputs, useful recovery indicators, potential uses of recovery models, and suitable types of software systems.

This paper describes the socio-technical restoration process and impacts associated with Hurricane Isaac (August, 2012) in Louisiana. Hurricane Isaac had few other impacts besides the power outage—there was extremely limited wind or flood damage. This isolation provides a relatively rare opportunity of seeing the linkages between electrical network disruption dependent infrastructure, and impacts and responses that might be obscured or overlooked in larger disaster. Thirty-three participants were asked five open ended questions about 1) the significant impacts from the outage, 2) what (if any) lasting effects would result from the outage, 3) the relative performance of Entergy’s restoration, 4) the effectiveness of Entergy’s communication, and 5) factors influencing the public and political dissatisfaction with the restoration performance. Other collected data included content from the news and social media, government documents, press releases, situation reports, and publicly available data. In sum, these data were synthesized to develop a broad understanding of the case study and identify significant themes with respect to impacts, as well as public and political reactions.

This case study discusses post-event reconnaissance research of the September 8, 2011, power outage that left San Diego County, California, without electricity for up to 12 h. The objective of this case study is to synthesize and analyze the impacts of the outage and responses to the event. Understanding the outage’s impacts and responses helps to reveal restoration practices and contexts that promote meeting both technical and nontechnical goals. This study reveals several issues related to restoration decision-making and communication related to critical customers, particularly those responsible for health care, wastewater and potable water management, fuel provision, and food service. Restoration did not occur and was not communicated in such a way to avoid impacts to dependent critical infrastructure, reflect state restoration criteria, or meet expectations of a variety of power customers. Insight from this case study suggests three themes to guide research and development of best practices fo...

Understanding and improving the resilience of infrastructure is critical to the broader goal of community disaster resilience - minimization of loss and facilitation of recovery. Empirical research shows that infrastructure damage or disruption from hazard events and ensuing reduced service levels has a strong, negative impact on business and household performance. Conceptual and computer models relating the socio-technical facets of infrastructure to community disaster resilience are underdeveloped. Community disaster resilience does not make theoretical or practical sense without viewing infrastructure as the combination of capitals and services. Specifically, infrastructure is characterized by its relationship to other infrastructure, agents, possible disruptions, possible interventions, jurisdictions and markets. This paper presents a subset of evolving conceptual, algorithmic and geo-visual representations of infrastructure-dependent community disaster resilience. This builds upon existing work on ResilUS - a prototype simulation model of community disaster resilience - and other contributions from the literature. The ultimate goal of this research is to provide the means for making lifeline infrastructure mitigation planning and restoration decisions that account for socio-technical and recovery-based goals.

A 1999 study published by the American Society of Civil Engineers Technical Council on Lifeline Earthquake Engineering entitled “Overcoming Barriers: Lifeline Seismic Improvement Programs” developed and analyzed six utility service providers and one major freeway management agency to understand their approach to seismic improvement. The study described seismic improvement policies and techniques, as well as the legal, historical, and organizational context that either facilitated or hindered seismic improvement. Since the original study, there have been significant changes in the scale and nature of these risk reduction efforts. This paper describes preliminary results of a follow-up study using the same data collection strategy to explore longitudinal trends of lifeline seismic improvement. The goal of the current study is to get an idea of the relative success of previously identified seismic improvement programs and study recommendations. More importantly, this study seeks to gain insight regarding whether these service providers have increased or decreased their seismic improvement efforts, how this has occurred and why.

This paper compares four permanent displacement models based on Newmark's sliding-block analogy for assessing regional seismic slope-performance. The models vary primarily by the ground motion descriptor used to correlate with Newmark displacement. The first uses peak ground-acceleration (PGA). The second uses PGA but normalizes displacements by predominant period and equivalent cycles. The third uses Arias intensity. The fourth calculates cumulative displacements from double-integrating simulated earthquake accelerograms. The models are implemented in a GIS to characterize seismic slope-performance for the Oakland East quadrangle near San Francisco, California. The resulting slope-performance maps are compared visually and through statistical analysis to expose potential differences and assess the effects of using a particular approach within a decision-making context. These maps were created for the purpose of comparison and are not suitable for use as critical decision-making tools. The models forecast notably different levels of slope-performance, with the PGA-based models predicting the greatest Newmark displacement on average. Thus, considering the variety of slope-performance models, it is suggested that practitioners avoid reliance on a single model. Instead, multiple models can be implemented in a GIS framework to gain a better perspective of the potential hazard and make a more informed decision.

A tool that has proliferated within civil engineering in recent years is geographic information systems (GIS). The goal of a tool is to supplement ability and knowledge that already exists, not to ...

Researchers and practitioners in earthquake engineering have recognized geographic information systems (GIS) to be a significant tool in modeling spatial phenomenon related to hazard and risk. GIS, as an engineering tool, has been primarily used for its spatial data storing and presentation features. Models are often simplified to be more compatible with the light computational capabilities of many GIS. If not simplified, heavy computations are generally performed external to the GIS. A prototype vector-based GIS was developed that employs a rigorous approach to Newmark's displacement method for assessing earthquake triggered landslide hazards. The rigorous Newmark's analysis provides desirable flexibility by allowing input of actual ground motions. The prototype hazard GIS incorporates a popular shot filtered noise technique for generating artificial ground motions. The rigorous approach was compared to a popular simplified approach for computing Newmark displacements. Distribution of regional displacements was found to be similar with the simplified approach giving more and larger extreme displacements. The rigorous approach is suitable for large scales to model various seismic scenarios and their effect on seismically induced landslide potential.

This study presents findings of an institutional capacity analysis of urban disaster risk reduction for informal settlements in the Guatemala Metropolitan Region. It uses a resource access perspective of vulnerability, actor-network theory, and qualitative data collection. The analysis reveals that there is interest in disaster risk reduction for the informal settlements; however, there is little in the way of direct financial or oversight relationships between informal settlement residents and all other actors. Respondents observed that informal settlements would probably remain inhabited; thus, there is a need for disaster risk reduction within these settlements. Disaster risk reduction capacity for informal settlements exists and can be further leveraged, as long as steps are taken to ensure appropriate access to and control of resources and oversight. Further, the nascent institutional arrangements should be strengthened through increased communication and coordination between actors, a decentralization of oversight and financial relationships, and mediation of identified resource conflicts.

This paper issues a call to improve the conceptual underpinnings of community resilience to disasters as it relates to critical infrastructure. It is argued that, broadly, infrastructure is the combination of both capital and services. Loss and recovery, and thus resilience, of infrastructure do not make practical sense without consideration of both. Specifically, infrastructure is characterized by its relationship to other infrastructure, agents, possible disruptions, possible interventions, jurisdictions and markets. Based on this and previous development work related to the resilience simulation model ResilUS, the beginnings of a new conceptual model are proposed. The comprehensive nature of the model suggests it is appropriate for planning purposes. The conceptual model provides opportunities for advancing ResilUS, specifically, and infrastructure and community resilience research and planning broadly. It facilitates the creation of a database schema for infrastructure loss and restoration and its impact on indicators of community resilience. Population of this schema will push understanding of community resilience and facilitate the development of better tools for supporting resilience planning.

Each disaster reminds us that, from an economic standpoint, losses do not occur instantaneously, but are accumulated over the course of a sometimes long and complex recovery process. Moreover, disasters are spatial events that impact some places and some groups within those places more heavily than others. These effects can be observed in both natural disasters and human-induced events. Thus in the September 11th tragedy of 2001, the loss of the World Trade Center towers and the thousands of human lives do not in themselves constitute the economic impact of the disaster. Neither does the loss of gross regional product (GRP) on the 11th itself. Rather, the economic impact of the disaster is strongly influenced by the multitude of decisions made in the days and months following — decisions regarding whether to relocate an office to New Jersey and for how long, whether to lay off workers and how many, and whether to inject stimulus spending into the New York City economy and how much. Similar post-event decisions strongly influence recovery in natural disasters such as floods or earthquakes. In other words, the process of disaster recovery is critical to understanding the spatial economic impacts of disasters, yet the recovery process itself is extremely complex and uncertain.

"What makes for a good model?" In various forms, this question is a question that, undoubtedly, many people, businesses, and institutions ponder with regards to their particular domain of modeling. One particular domain that is wrestling with this question is the multidisciplinary field of environmental modeling. Examples of environmental models range from models of contaminated ground water flow to the economic impact of natural disasters, such as earthquakes. One of the distinguishing claims of the field is the relevancy of environmental modeling to policy and environment-related decision-making in general. A pervasive view by both scientists and decision-makers is that a "good" model is one that is an accurate predictor. Thus, determining whether a model is "accurate" or "correct" is done by comparing model output to empirical observations. The expected outcome of this process, usually referred to as "validation" or "ground truthing," is a stamp on the model in question of "valid" or "not valid" that serves to indicate whether or not the model will be reliable before it is put into service in a decision-making context. In this paper, I begin by elaborating on the prevailing view of model validation and why this view must change. Drawing from concepts coming out of the studies of science and technology, I go on to propose a contextual view of validity that can overcome the problems associated with "ground truthing" models as an indicator of model goodness. The problem of how we talk about and determine model validity has much to do about how we perceive the utility of environmental models. In the remainder of the paper, I argue that we should adopt ideas of pragmatism in judging what makes for a good model and, in turn, developing good models. From such a perspective of model goodness, good environmental models should facilitate communication, convey  not bury or "eliminate"  uncertainties, and, thus, afford the active building of consensus decisions, instead of promoting passive or self-righteous decisions.

Researchers, emergency response and lifeline managers, and municipal planners are beginning to recognize the utility of seismic landslide hazard zonation. With this recognition, the decisions made based on resulting maps could have widespread social and economic impact in the event of a large earthquake. This report compares several popular permanent displacement models for assessing seismic slope-performance. The approaches are implemented in a raster GIS to expose potential differences and assess the effects of using a particular approach within a decision-making context. It is observed that each approach forecasts notably different levels of slope-performance. Thus, considering the variety of spatial seismic landslide analysis approaches and the effect of basing a decision on a map created using a single one of them, it is suggested that less reliance be put on the traditional paper map format. Instead, multiple approaches can be used to investigate many scenario earthquakes under a variety of conditions in a computer-based spatial decision support system. INTRODUCTION Keefer (1984) observed that earthquakes of moderate to high magnitude can cause landslides over an area as large as 500,000 km2 . These landslides also have large damage potential as illustrated by the recent effects of the 1989 Loma Prieta and 1994 Northridge earthquakes (Harp and Jibson, 1995; Keefer, 1998). Accordingly, researchers, emergency response and lifeline managers, and municipal planners are beginning to recognize the utility of seismic landslide hazard and risk zonation. With this recognition, the decisions made based on resulting hazard or risk maps could have widespread social and economic impact in the event of a large earthquake. Therefore, investigating and comparing several popular techniques for seismic slopeperformance zonation is important. The state of the art in seismic landslide hazard zonation using geographic information systems (GIS) was summarized by Ho and Miles (1997), who suggested several potential approaches using dynamic permanent-displacement models. In the short time since then, considerable effort has been spent improving seismic landslide hazard zonation techniques using spatial technologies (Miles and Ho, 1999; Jibson and others, 1998; McCrink and Real, 1996). This report extends the study of Ho and Miles (1997) by implementing several seismic slope-performance models using raster GIS to expose any differences between the approaches and assess the potential effects of using a particular approach within a decision-making context. The report begins by summarizing the approaches that exist for determining seismic landslide hazard. The general procedure of a permanent-displacement analysis the class of approaches chosen in the report for investigation is then described. The report concludes by discussing the implementation of each individual approach and the differences among these approaches. PERMANENT-DISPLACEMENT ANALYSIS Three basic approaches exist for conducting seismic landslide hazard analysis. These consist of the statistical, pseudo-static, and permanent-displacement approaches. A statistical approach assesses hazard by assuming the past predicts the future. Hazard is assessed through correlation of past landslides with several influential factors. Results of a statistically based analysis can range from an estimated probability of failure to some index indicating degrees of hazard. Pseudo-static analysis employs a traditional static slope-stability analysis with the addition of a horizontal force component that models the effects of earthquake-induced ground-motions. A pseudo-static analysis yields a factor of safety against seismic slope failure. This effectively provides a simple binary index of whether a slope is expected to fail or not at a given level of seismic acceleration. Permanent-displacement techniques provide information regarding actual slope-performance through calculation of some index of relative or actual displacement based on commonly accepted characterizations of earthquake-shaking severity. Permanent-displacement analysis is chosen for investigation because of its higher information content, better modeling of ground-motion, and increasing acceptance in the earthquake engineering community. Newmark's Sliding Block Analogy In his landmark paper, Newmark (1965) noted that the transient effects of earthquake motions can cause permanent deformation of slopes prior to complete failure. Newmark proposed modeling a slope subjected to earthquake-induced accelerations as a friction block resting on an inclined plane subjected to the same accelerations as the modeled slope (Figure 1). Therefore, in each instance when the sum of the static and dynamic forces exceed the shear resistance of the sliding interface the block will displace. The interface shear resistance is commonly characterized by the critical acceleration (ac) of the modeled slope, which is the base acceleration needed to overcome the shear resistance. Newmark (1965) defined the following relationship to calculate critical acceleration in the case of planar slip