Infrastructure Management Decision-Making with Condition Data Generated by Remote Sensors: A Time Series Framework

Available Documents
Project Report

Primary Investigator
Pablo Durango-Cohen
Department of Civil and Environmental Engineering
Northwestern University Transportation Center
2145 Sheridan Road, A335
Evanston, IL 60208
(847) 491 4008
(847) 491 4011 fax
pdc[at]northwestern.edu

Project Objective
The objectives of this research study are to develop tools that will allow agencies to process and exploit the data to support infrastructure maintenance and rehabilitation (IM&R) decision-making, and to provide a framework to evaluate different strategies for deploying sensing technologies.

Project Abstract
The researchers propose to develop an optimization model for IM&R decision-[j1]making that can accommodate potentially large quantities of condition data generated by sensors or other non-destructive evaluation technologies. The data will be processed by an algorithm known as the Kalman Filter to obtain a representation of facility condition meaningful for IM&R decision-making. The model builds on a previously presented framework that transforms condition data generated by sensors into a representation of condition that consists of two elements: structural integrity and functional performance. The motivation for the research is to capture (i) the benefits of updating/fine-tuning the representation of condition over a facility’s life-cycle in response to new data (generated by sensors), and (ii) the benefits of using different representations for different facilities. The elements used to represent the condition of steel bridges may be different than those used for concrete bridges.

Task Descriptions

  1. Task 1- Literature Review – An extensive literature review will be conducted to identify possible approaches toaddress the research problem. The most promising is the Kalman Filter
  2. Task 2- Model Formulation and Solution – (a) model IM&R decision-making as a latent decision process model and (b)implement Kalman Filtering algorithm
  3. Task 3- Case Studies – facility TBD – compare the methodology to state-of-the-art models such as the ones used in the Pontis Bridge Management System.
  4. Task 4 – prepare reports and deliverables
  5. Task 5 – explore the feasibility of transferring the proposed methodology into an infrastructure management system

Relationship to Other Research Projects
None.

Technology Transfer Activities
Research team will explore the feasibility of transferring the proposed methodology into an infrastructure management system and choose a state DOT partner to work with.

Potential Benefits of the Project
New technologies require new methods to process their nascent output to a form that is meaningful and useful for deterioration modeling, and in turn, for infrastructure decision making. This research will maximize a system’s performance by incorporating data generated by remote sensors.

Project Information

  • Milestones Dates: Project Start Date: June 1, 2003 Project End Date: May 31, 2004
  • Budget: $77,105
  • Student Involvement: 1 Graduate student assistant
  • TRB Keywords: Remote Sensing, Maintenance and Rehabilitation, Infrastructure Management
  • Primary Subject: Infrastructure maintenance and rehabilitation (IM&R) decision-making
  • Modal Orientation: Multi-modal, primarily highway
  • Project Number: 04-03