Jong Sung Lee

Architectural Overview of MAEviz – HAZTURK

MAEviz is a broadly extensible, open source platform for earthquake hazard risk management. MAEviz is a model cyberenvironment that provides practical capabilities for researchers through decision-makers to model earthquake events, develop risk reduction strategies, and implement mitigation plans to minimize the impact of earthquake disasters while also providing a pathway for researchers to quickly add new algorithms and data to assure that decisions are based on state-of-the-art engineering understanding. While MAEviz is capable of interacting with remote data and computational sources, it is also fully capable of running analyses locally so research scientists and decision-makers can generate information when a catastrophic event occurs and provide first-responders result information. This article describes MAEvizs overall layered architecture, its foundation in the widely used Eclipse Rich Client Platform (RCP), and use of open-source middleware and geographic information system (GIS) components. MAEvizs data management capabilities and workflow-oriented execution model are also discussed with an emphasis on detailing MAEvizs capability to incorporate new data types and new analysis modules.

Overview and applications of Maeviz-Hazturk 2007

A new generation of tools is needed to allow researchers and practicing engineers the ability to leverage investments in new methodologies and software infrastructure while enabling customization to local conditions. MAEviz represents such a next generation of seismic risk assessment software, based on the Mid-America Earthquake (MAE) Center research in Consequence-based Risk Management (CRM) and is designed to be extended, customized, and evolved to meet the needs of specific organizations and regions. MAEviz helps bridge the gap between researchers, practitioners, and policy-makers by integrating the latest research findings and most accurate data, using state-of-the-art methodologies, in an extensible software platform.

Selectorecombinative Genetic Algorithm to Relax Computational Complexity of Discrete Network Design Problem

A new approach is proposed for relaxing the computational complexity of the user equilibrium discrete network design problem (UE-DNDP), under deterministic traffic conditions, with a solution search procedure based on the selectorecombinative genetic algorithm (SGA). The SGA uses only selection and recombination operators rather than mutation. The UE-DNDP approach proposed in this study has a bilevel structure: the upper-level problem relates to the strategy of the network design authority, and the lower-level problem deals with network user behaviors. To solve the upper-level problem, the SGA is first used to create feasible network design solutions to the UE-DNDP by accounting for a budget constraint. Then the design authority selects the best network design strategy with minimum total system travel time (TSTT). Extensive experimental design and testing on GA operators and parameters are conducted to select the useful GA operators and parameters. For the lower-level problem, the best K-value concept is ...

Analysis of Economic Impacts of an Earthquake on Transportation Network

Prior to the 1990s, natural disasters and their economic impacts were not a major field of study for regional economic analysts even though there was a sizeable literature based on structural engineering and geotechnical approaches. The latter approaches attempted to understand the behavior of earthquakes and to explore ways to prevent or minimize damage from the disaster should it occur. However, when decisions needs to be made on the retrofit of existing facilities as a prevention or the restoration of disrupted facilities after damages, economic considerations related to budgeting priorities have not been prominently featured. As a consequence, decisions about retrofit strategies tend to focus on engineering-based criteria (for example, bridge 21 on route 50 should be retrofitted because it presents the greatest probability of collapsing given an earthquake of magnitude x) rather than on economic criteria (for example, a 10% loss of capacity on bridge 10 on route 60 would create the greatest economic disruption under a similar earthquake scenario and hence would have the highest priority for retrofit). Hence, there is a clear need to provide some interface to explore the ways in which engineering-based assessments can be compared with those based on economic analysis tools. The current research described in this chapter provides such an interdisciplinary research effort.1

Integrated Decision Support for Roadway Safety Analysis

This paper presents an integrated decision-support framework that helps public agencies identify high-crash locations and develop cost-effective safety improvement projects. State-of-the-art safety concepts, such as safety performance functions, empirical Bayesian method, and potential for safety improvements, are incorporated to identify hazardous highway locations. Cost-effective safety projects are prioritized based on life-cycle safety benefit evaluation and optimal resource allocation models. An efficient solution algorithm to the optimization model is also proposed. The models are implemented in an internet-based, geographic information system (GIS) software tool that builds on state-of-the-art information technologies, graphical interfaces, and customized GIS algorithms such as spatial analysis with buffering operations.

Semantic middleware for e-Science knowledge spaces

The Tupelo semantic content management middleware implements Knowledge Spaces that enable scientists to integrate information into a comprehensive research record as they work with existing tools and domain‐specific applications. Knowledge Spaces combine approaches that have demonstrated success in automating parts of this integration activity, including content management systems for domain‐neutral management of data, workflow technologies for management of computation and analysis, and semantic web technologies for extensible, portable, citable management of descriptive information and other metadata. Tupelos ‘Context’ facility and its associated semantic operations both allow existing data representations and tools to be plugged in, and also provide a semantic ‘glue’ of important associative relationships that span the research record, such as provenance, social networks, and annotation. Tupelo has enabled the recent work creating e‐Science cyberenvironments to serve distributed, active scientific communities, allowing researchers to develop, coordinate and share datasets, documents, and computational models, while preserving process documentation and other contextual information needed to produce an integrated research record suitable for distribution and archiving. Copyright

Implementation of Spatiotemporal Model for Infrastructure Reconstruction Strategy Under Large-Scale Disaster

The reconstruction strategy chosen to repair damaged transportation network infrastructure after an unscheduled event is important in returning the disrupted economy to the status preceding the event. Furthermore, the strategy determines how fast the national economy will recover. Previous research has shown how damage to the transportation network has significant direct and indirect impacts on the national economy. The optimal sequence or priority for reconstruction of damaged links must be developed to restore the economy quickly. Common issues from the previous research on finding the optimal reconstruction strategy are (a) lack of consideration of the national economic impact, (b) lack of integration of the traffic flow and commodity flow models, and (c) lack of consideration of the spatiotemporal characteristics of the economy. To overcome these issues, this paper suggests a spatiotemporal model for finding the optimal reconstruction strategy after an unscheduled event based on an earlier model. The paper discusses the framework for finding the optimal reconstruction strategy by using the spatiotemporal analysis model for a posteriori unscheduled event (STAM-2).

Spatio-Temporal Models for Network Economic Loss Analysis Under Unscheduled Events: A Conceptual Design

The damages and losses caused by unscheduled events such as earthquakes, floods, and other major natural disasters have sudden and significant impacts on the economies of regions where these events occur. The impacts of damage on production facilities and lifelines (e.g. utility lines and transportation networks) may spread across several regions via importexport relationships and have serious economic impacts on even distant regions far from the location of the event.

MAEviz: Bridging the Time-from-Discovery Gap between Seismic Research and Decision Making

MAEviz is an open-source project that helps reduce the time from discovery gap that exists between researchers, practitioners, and decision makers by integrating the latest research findings, most accurate data, and new methodologies into a single software product. It was developed as a platform to perform seismic risk assessment based on the mid-america earthquake (MAE) center research in the consequence-based risk management (CRM) framework. MAEviz is built upon an open source, extensible software platform developed at NCSA using the eclipse rich client platform (RCP). The example shown in the poster is network-based seismic retrofit (NBSR) analysis. The analysis solves a typical problem faced by decision makers that, given a fixed budget, which combination of bridges and retrofit methods would minimize the societal cost of an earthquake. It clearly shows how new science can be put quickly into the hands of the decision makers, thus bridging the gap between research and practical application. MAEviz is shown to be a powerful tool that can currently be used to assist decision makers in preparing for and mitigating the consequences of seismic hazards. Moreover, the extensible architecture of MAEviz allows it to be easily adapted to integrate newly discovered science and data, both in the area of seismic risk assessment, as well as other future research areas.

Hindcasting community-level building damage for the 2011 Joplin EF5 tornado

Resiliency of communities prone to natural hazards can be enhanced through the use of risk-informed decision-making tools. These tools can provide community decision makers key information, thereby providing them the ability to consider an array of mitigation and/or recovery strategies. The Center for Risk-Based Community Resilience Planning, headquartered at Colorado State University in Fort Collins, Colorado, developed an Interdependent Networked Community Resilience (IN-CORE) computational environment. The purpose of developing this computational environment is to build a decision-support system, for professional risk planners and emergency responders, but even more focused on allowing researchers to explore community resilience science. The eventual goal was being to integrate a broad range of scientific, engineering and observational data to produce a detailed assessment of the potential impact of natural and man-made hazards for risk mitigation, planning and recovery purposes. The developing computational environment will be capable of simulating the effects from different natural hazards on the physical and socioeconomic sectors of a community, accounting for interdependencies between the sectors. However, in order to validate this computational tool, hindcasting of a real event was deemed necessary. Therefore, in this study, the community of Joplin, Missouri in the USA, which was hit by an EF-5 tornado on May 22, 2011, is modeled in the IN-CORE v1.0 computational environment. An explanation of the algorithm used within IN-CORE is also provided. This tornado was the costliest and deadliest single tornado in the USA in the last half century. Using IN-CORE, by uploading a detailed topological dataset of the community and the estimated tornado path combined with recently developed physics-based tornado fragilities, the damage caused by the tornado to all buildings in the city of Joplin was estimated. The results were compared with the damage reported from field studies following the event. This damage assessment was done using three hypothetical idealized tornado scenarios, and results show very good correlation with observed damage which will provide useful information to decision makers for community resilience planning.