Geology topics

Suzanna K. Long

Publications and source records attributed to Suzanna K. Long.

8 recordsLinked to original sources

USGS2019

Supply chain infrastructure restoration calculator software tool—Developer guide and user manual

This report describes a software tool that calculates costs associated with the reconstruction of supply chain interdependent critical infrastructure in the advent of a catastrophic failure by either outside forces (extreme events) or internal forces (fatigue). This tool fills a gap between search and recover strategies of the Federal Emergency Management Agency (or FEMA) and construction techniques under full recovery. In addition to overall construction costs, the tool calculates reconstruction needs in terms of personnel and their required support. From these estimates, total costs (or the cost of each element to be restored) can be calculated. Estimates are based upon historic reconstruction data, although decision managers do have the choice of entering their own input data to tailor the results to a local area.

Open-File Report↗

USGS2018

Modeling and simulation of emergent behavior in transportation infrastructure restoration

The objective of this chapter is to create a methodology to model the emergent behavior during a disruption in the transportation system and that calculates economic losses due to such a disruption, and to understand how an extreme event affects the road transportation network. The chapter discusses a system dynamics approach which is used to model the transportation road infrastructure system to evaluate the different factors that render road segments inoperable and calculate economic consequences of such inoperability. System dynamics models have been integrated with business process simulation model to evaluate, design, and optimize the business process. The chapter also explains how different factors affect the road capacity. After identifying the various factors affecting the available road capacity, a causal loop diagram (CLD) is created to visually represent the causes leading to a change in the available road capacity and the effects on travel costs when the available road capacity changes.

Book chapter↗

USGS2016

Infrastructure system restoration planning using evolutionary algorithms

This paper presents an evolutionary algorithm to address restoration issues for supply chain interdependent critical infrastructure. Rapid restoration of infrastructure after a large-scale disaster is necessary to sustaining a nation's economy and security, but such long-term restoration has not been investigated as thoroughly as initial rescue and recovery efforts. A model of the Greater Saint Louis Missouri area was created and a disaster scenario simulated. An evolutionary algorithm is used to determine the order in which the bridges should be repaired based on indirect costs. Solutions were evaluated based on the reduction of indirect costs and the restoration of transportation capacity. When compared to a greedy algorithm, the evolutionary algorithm solution reduced indirect costs by approximately 12.4% by restoring automotive travel routes for workers and re-establishing the flow of commodities across the three rivers in the Saint Louis area.

INCOSE International Symposium↗

USGS2016

Post-disaster supply chain interdependent critical infrastructure system restoration: A review of data necessary and available for modeling

The majority of restoration strategies in the wake of large-scale disasters have focused on short-term emergency response solutions. Few consider medium- to long-term restoration strategies to reconnect urban areas to national supply chain interdependent critical infrastructure systems (SCICI). These SCICI promote the effective flow of goods, services, and information vital to the economic vitality of an urban environment. To re-establish the connectivity that has been broken during a disaster between the different SCICI, relationships between these systems must be identified, formulated, and added to a common framework to form a system-level restoration plan. To accomplish this goal, a considerable collection of SCICI data is necessary. The aim of this paper is to review what data are required for model construction, the accessibility of these data, and their integration with each other. While a review of publicly available data reveals a dearth of real-time data to assist modeling long-term recovery following an extreme event, a significant amount of static data does exist and these data can be used to model the complex interdependencies needed. For the sake of illustration, a particular SCICI (transportation) is used to highlight the challenges of determining the interdependencies and creating models capable of describing the complexity of an urban environment with the data publicly available. Integration of such data as is derived from public domain sources is readily achieved in a geospatial environment, after all geospatial infrastructure data are the most abundant data source and while significant quantities of data can be acquired through public sources, a significant effort is still required to gather, develop, and integrate these data from multiple sources to build a complete model. Therefore, while continued availability of high quality, public information is essential for modeling efforts in academic as well as government communities, a more streamlined approach to a real-time acquisition and integration of these data is essential.

Data Science Journal↗

USGS2016

Defining resilience: A preliminary integrative literature review

The term “resilience” is ubiquitous in technical literature; it appears in numerous forms, such as resilience, resiliency, or resilient, and each use may have a different definition depending on the interpretation of the writer. This creates difficulties in understanding what is meant by ‘resilience’ in any given use case, especially in discussions of interdisciplinary research. To better understand this problem, this research constructs a preliminary integrative literature review to map different definitions, applications and calculation methods of resilience invoked within critical infrastructure applications. The preliminary review uses a State-of-the-Art Matrix (SAM) analysis to characterize differences in definition across disciplines and between regions. Qualifying the various usages of resilience will produce a greater precision in the literature and a deeper insight into types of data required for its evaluation, particularly with respect to critical infrastructure calculations and how such data may be analyzed. Results from this SAM analysis will create a framework of key concepts as part of the most common applications for “resilient critical infrastructure” modeling.

Conference Paper↗

USGS2015

Framework for modeling urban restoration resilience time in the aftermath of an extreme event

The impacts of extreme events continue long after the emergency response has terminated. Effective reconstruction of supply-chain strategic infrastructure (SCSI) elements is essential for postevent recovery and the reconnectivity of a region with the outside. This study uses an interdisciplinary approach to develop a comprehensive framework to model resilience time. The framework is tested by comparing resilience time results for a simulated EF-5 tornado with ground truth data from the tornado that devastated Joplin, Missouri, on May 22, 2011. Data for the simulated tornado were derived for Overland Park, Johnson County, Kansas, in the greater Kansas City, Missouri, area. Given the simulated tornado, a combinatorial graph considering the damages in terms of interconnectivity between different SCSI elements is derived. Reconstruction in the aftermath of the simulated tornado is optimized using the proposed framework to promote a rapid recovery of the SCSI. This research shows promising results when compared with the independent quantifiable data obtained from Joplin, Missouri, returning a resilience time of 22 days compared with 25 days reported by city and state officials.

Kansas↗

USGS2015

A Systems Thinking approach to post-disaster restoration of maritime transportation systems

A Systems Thinking approach is used to examine elements of a maritime transportation system that are most likely to be impacted by an extreme event. The majority of the literature uses a high-level view that can fail to capture the damage at the sub-system elements. This work uses a system dynamics simulation for a better view and understanding of the Port of San Juan, Puerto Rico, as a whole system and uses Hurricane Georges (1998), as a representative disruptive event. The model focuses on the impacts of natural disasters at the sub-system level with a final goal of determining the sequence needed to restore an ocean-going port to its pre-event state. This work in progress details model development and outlines steps for using real-world information to assist maritime port manager planning and recommendations for best practices to mitigate disaster damage.

Port of San Juan↗

USGS2013

Integrating complexity into data-driven multi-hazard supply chain network strategies

Major strategies in the wake of a large-scale disaster have focused on short-term emergency response solutions. Few consider medium-to-long-term restoration strategies that reconnect urban areas to the national supply chain networks (SCN) and their supporting infrastructure. To re-establish this connectivity, the relationships within the SCN must be defined and formulated as a model of a complex adaptive system (CAS). A CAS model is a representation of a system that consists of large numbers of inter-connections, demonstrates non-linear behaviors and emergent properties, and responds to stimulus from its environment. CAS modeling is an effective method of managing complexities associated with SCN restoration after large-scale disasters. In order to populate the data space large data sets are required. Currently access to these data is hampered by proprietary restrictions. The aim of this paper is to identify the data required to build a SCN restoration model, look at the inherent problems associated with these data, and understand the complexity that arises due to integration of these data.

Book↗