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---
layout: page-header
id: research
title: CRISP Research
---
<div class="container">
<div class="row">
<div class="col s12 m9 l10">
<div class="section scrollspy">
<img class="responsive-img" style="padding: 30px 30px 30px" src="{{site.diagrams}}/project-goal.png">
</div>
<div id="goal" class="section scrollspy">
<h5 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="100ms">
Goal of the Project
</h5>
<p class="text-justify light">
Enhance the resiliency of interdependent critical infrastructures through the integrated
decision-making framework
</p>
</div>
<div class="divider"></div>
<div id="objectives" class="section scrollspy">
<h5 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="100ms">
Objectives of the Project
</h5>
<ul>
<li class="light"> <i class="tiny material-icons">chevron_right</i>
Examine the infrastructure resiliency associated with design configurations </li>
<li class="light"> <i class="tiny material-icons">chevron_right</i>
Examine the infrastructure resiliency associated with operational strategies
</li>
<li class="light"> <i class="tiny material-icons">chevron_right</i>
Examine organizational strategies that recognize critical infrastructure (CI)
interdependencies
</li>
<li class="light"> <i class="tiny material-icons">chevron_right</i>
Develop an adaptive simulator for high-level stakeholders to identify/quantify the failure
impacts and potential strategies
</li>
<li class="light"> <i class="tiny material-icons">chevron_right</i>
Develop the Resilient Infrastructures Learning Game (RILG) for public participation and
dissemination
</li>
</ul>
</div>
<div class="divider"></div>
<div id="t1" class="section scrollspy">
<h5 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="100ms">
Task 1: Physical-based Interdependency – Water-transportation Modeling
</h5>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Objectives
</h6>
<ul>
<li class="light"> <i class="tiny material-icons">chevron_right</i>
Investigate the impact of physical-based interdependencies
(primarily common rights-of-way) using a multi-layer network modeling approach
</li>
</ul>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Progress
</h6>
<p class="text-justify light">
Three activities are being conducted under this task.
</p>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
<i class="tiny material-icons">chevron_right</i>Activity 1: Water infrastructure simulation
</h6>
<p class="text-justify light">
The purpose of water infrastructure simulation is to evaluate the resilience of the current
City of Tampa water distribution infrastructure and to assess the interdependency between it
and other infrastructure systems (e.g. transportation, cyber, socio-economic impacts). The water
distribution network model is used within the open source hydraulic modeling software, EPANET,
to conduct simulations and analyses as described in the following. Extended period simulation
of each individual water distribution component failure has been completed for the City of Tampa
network, including both junctions and pipes. In conjunction with failure thresholds, the simulation
results were also used to construct a logical consequence network, to represent relationships of
hydraulic influence between different components within the water distribution network. The logical
consequence network allows identification of critical and vulnerable components, which is a key to
enhancing resilience. Network based topological analyses were conducted for both the
physical-spatial water distribution network and the logical consequence network. In addition,
a combination of simulation and network based approaches has been explored.
</p>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
<i class="tiny material-icons">chevron_right</i>Activity 2: Transportation system simulation
</h6>
<p class="text-justify light">
Tampa Bay Regional Planning Model (TBRPM) version 8.2 and CUBE Voyager package as simulation
software programs are used to simulate the traffic performance influenced by transportation
infrastructure. To reduce the time of simulation, a subarea model involving only the City of Tampa
transportation network has been developed. This subarea model consists of CUBE packages for
‘highway assignment’ program which assigns origin-destination traffic volume to different routes.
The ultimate goals of the simulation are to identify the most critical links in the network based
on several performance measures and determine the impacts if one or more of these critical links
become dysfunctional. This process includes removing (100% capacity reduction) one link from the
network, letting the model reassign the traffic volume on that link to other available routes,
and then determining the effect of this reassignment based on three different performance measures.
The performance measures being considered are, summation of link travel times of all links,
summation of link flow multiplied by link travel time for all links and summation of volume
to capacity ratio (v/c) for links with v/c greater than one. The decision to choose these measures
for the subarea model to evaluate the network has been taken based on the results from previously
mentioned District 7 simulation.
</p>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
<i class="tiny material-icons">chevron_right</i>Activity 3: Water-transportation interdependency
</h6>
<p class="text-justify light">
Performance measures and failure thresholds have been adopted for both water and transportation
infrastructures. The adopted measure and thresholds will be used to quantify vulnerability and
resilience to account for physical/co-location interdependencies. An interface network was created
for the City of Tampa by: overlaying both water distribution and transportation roads, accounting
for road lane width, and extracting their intersection within ArcGIS. The interface network topology
was analyzed from a network analysis perspective as a directed network, while it also served as a
logical network representing failure propagation from the water distribution network to the
transportation roads. Using the logical consequence networks from water and transportation,
a mathematical model for identification of critical water distribution components while accounting
for interdependency with the transportation road network was created. A multi-objective optimization
approach was used to solve the problem. Results from solving the multi-objective optimization
problem were the critical components and were then integrated into the ArcGIS environment for
further geo-spatial analyses, making the model results useful for utilities.
</p>
</div>
<div class="divider"></div>
<div id="t2" class="section scrollspy">
<!-- Task 2-->
<div class="row">
<h5 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="100ms">
Task 2: Virtual Interdependency – Transportation-cyber Modeling
</h5>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Objectives
</h6>
<ul>
<li class="light"> <i class="tiny material-icons">chevron_right</i>
Investigate the impact of virtual-based dependencies on the operational resiliency using
transportation and cyber as infrastructure examples (such as cyber attack on TMC center,
signals or detectors, or cyber failure caused by natural disaster, cyber attack, out of
power)
</li>
</ul>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Progress
</h6>
<p class="text-justify light"> The activity with a focus of problem formulation on system
resilience optimization is being conducted under this task.
</p>
<!--<h4 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">-->
<!--Activity 1: Water infrastructure simulation-->
<!--</h4>-->
<p class="text-justify light">
This activity focuses on the formulation of optimization problem to involve transportation
system resilience measures, alternative countermeasures and proper constrains. A resilience
measure involving travel time and unmet demand in a transportation system is proposed as the
objective function to solve the problem for the infrastructure restoration planning after
disruptive events. To integrate the quantification of resilience measures with restoration
decision-making, a bi-level optimization model is proposed. The upper- and lower-level
problems were solved by a modified active set algorithm and a network representation method
derived from Network Design Problems, respectively. Furthermore, a simulation based optimization
framework was proposed for traffic signal restoration sequence optimization after signal
failure. Two resilience measures, the accumulated total delay and the maximum total delay
during restoration are proposed to quantify the system performance resulting from different
signal restoration strategies. System performance evaluation under different restoration
strategies are evaluated through traffic simulation in Vissim.
</p>
</div>
</div>
<div class="divider"></div>
<div id="t3" class="section scrollspy">
<!-- Task 3-->
<div class="row">
<h5 class="section-title wow fadeInRight text-darken-1"
data-wow-duration="1000ms" data-wow-delay="100ms">
Task 3: Integrated critical infrastructure management
</h5>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Objectives
</h6>
<ul>
<li class="light"> <i class="tiny material-icons">chevron_right</i> Propose an innovative
prognostic and health management (PHM)
framework to maintain and improve the resiliency of critical infrastructures</li>
</ul>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Progress
</h6>
<p class="text-justify light">
Two activities with one focusing on proactive maintenance
and one on restoration planning are being conducted under this task.
</p>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
<i class="tiny material-icons">chevron_right</i>Activity 1: Modeling the performance of the
deteriorating infrastructures (proactive maintenance before failures/disasters)
</h6>
<p class="text-justify light">
This activity focused on prognostic health management framework for deteriorating
infrastructures. A mathematical framework for real-time prognostic health management of the
deteriorating physical infrastructure (e.g., transportation/water infrastructure) was developed
to utilize the field monitoring/inspection data in improving the failure prediction of physical
infrastructure. Both novel Bayesian model and computational algorithm have been developed to
analyze the bi-level heterogeneous degradation data and realize both off-line model learning
of multiple deteriorating infrastructure components (e.g., roads, pipes) and on-line sequential
updating of an individual deteriorating component. Both the numerical case study and real
case study were further performed to demonstrate the effectiveness of the proposed
methodological framework.
</p>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
<i class="tiny material-icons">chevron_right</i>Activity 2: Mathematical models for restoration
activities (during and post-failures/disaster)
</h6>
<p class="text-justify light">
This activity focused on formulating and solving stochastic and deterministic optimization
models to account for failures and their propagations across infrastructures, while two levels
of interdependencies (physical and socioeconomic) were taken into accounts. The mathematical
model was solved for the simplified interdependent water-transportation networks in the
City of Tampa.
</p>
</div>
</div>
<div class="divider"></div>
<div id="t4" class="section scrollspy">
<!-- Task 4-->
<div class="row">
<h5 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="100ms">
Task 4: Parameters of Organizational Resiliency
</h5>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Objectives
</h6>
<ul>
<li class="light"> <i class="tiny material-icons">chevron_right</i> Identify influential
factors in managing CIs and the strategies creating consensus among high-level stakeholders.
We will also apply conceptual validation for the descriptive variables. </li>
</ul>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Progress
</h6>
<p class="text-justify light"> Stakeholder interviews and analysis are being conducted
under this task.
</p>
<!--<h5 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">-->
<!--Activity 1: Modeling the performance of the deteriorating infrastructures (proactive maintenance before failures/disasters)-->
<!--</h5>-->
<p class="text-justify light">
The social science research team conducted semi-structured interviews with administrative
officials and engineers from the City of Tampa departments of Water, Transportation/Stormwater,
and Technology and Innovation in 2017 and 2018. The team transcribed the interviews and
conducted text link analysis of the transcriptions.
</p>
</div>
</div>
<div class="divider"></div>
<div id="t5" class="section scrollspy">
<!-- Task 5-->
<div class="row">
<h5 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="100ms">
Task 5: Multi-method Adaptive Simulator
</h5>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Objectives
</h6>
<p class="text-justify light">
<ul>
<li class="light"> <i class="tiny material-icons">chevron_right</i> Develop a multi-paradigm
adaptive simulator, including system dynamics that models socioeconomic systems via a
top-down approach, and agent-based models that model a network of stakeholders associated
with the decision making and management process of CIs via a bottom-up approach.
</li>
</ul>
</p>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Progress
</h6>
<p class="text-justify light">The prototype agent-based simulation engine was designed for the
interdependent water-transportation infrastructures in the City of Tampa. Cascading algorithm
and decision-making behaviors for maintenance crew were designed and implanted in Java and
AnyLogic software.
</p>
</div>
</div>
<div class="divider"></div>
<div id="t6" class="section scrollspy">
<!-- Task 6-->
<div class="row">
<h5 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="100ms">
Task 6: Parameters of Organizational Resiliency
</h5>
<h6 class="section-title wow fadeInRight" data-wow-duration="1000ms" data-wow-delay="200ms">
Objectives
</h6>
<p class="text-justify light">
<ul>
<li class="light"> <i class="tiny material-icons">chevron_right</i> Design a simulation
game platform such that different teams from academia and practitioners can evaluate their
developed strategies through a feedback learning environment
</li>
</ul>
</p>
</div>
</div>
</div>
<div class="col hide-on-small-only m3 l2">
<div class="toc-wrapper-pushpin">
<ul class="section table-of-contents">
<li><a href="#t1">Task 1</a></li>
<li><a href="#t2">Task 2</a></li>
<li><a href="#t3">Task 3</a></li>
<li><a href="#t4">Task 4</a></li>
<li><a href="#t5">Task 5</a></li>
<li><a href="#t6">Task 6</a></li>
</ul>
</div>
</div>
</div>
</div>