NCHRP Project
Proposed Research Problem Statement
1. Title
Prediction of bridge condition rating using bridge historical inspection data
2. Background
Bridges serve as critical structures within an infrastructure system and require regular
inspections to ensure they are in safe operating condition for the public. As of 2010 there are
approximately 600,000 bridges in the United States and more than 40% of the bridges are
either structurally deficient or functionally obsolete and in need of maintenance, repair,
rehabilitation, improvement or replacement according to the Federal Highway
Administration (FHWA) report1. The National Bridge Inventory Database (NBI), compiled
by the FHWA and State Departments of Transportation (DOTs), contains detailed historical
data on over 600,000 bridges for the past twenty years. These bridge databases usually
include detail bridge condition and its state at the time of inspection. This information
presents a tremendous resource for examination and evaluation of bridges; however, its
power is not fully realized until detail data processing approaches are applied in order to
explore the relationships among these data and their inherent patterns and trends. By
discovering inherent patterns among the inspection data, future bridge condition forecasting
should not be difficult.
3. Statement of Urgency
Bridge owners need to assess their capabilities and capacity to bring their efficient bridge to
the state of good repair and they need start the planning process as soon as possible in order
to provide ensure that they can respond to upcoming requirements most effectively. The
research results will be directly applicable to bridge management system. This study shows
the potential to develop a tool to accurately predict the future condition ratings of bridges to
assist agencies in bridge program planning. It can help to identify the most efficient
maintenance activities and timing for an individual bridge or network of bridges.
4. Project Objective(s)
The objective of the project is to study the database, examine data patterns and make
predictions of future bridge condition ratings using Time Series methodology in addition to
non-linear statistical data modeling. The research will investigate the correlation between
bridge structural deterioration conditions (e.g. deck, superstructure and substructure) and
bridge & traffic characteristics (e.g. bridge age, average daily traffic volumes, number of
spans, materials, type of design, etc.) over 10 -15 year period.
1
Assessment of FHWA Oversight of the Highway Bridge Program and the National Bridge Inspection Program. FHWA Bridge Report
MH- 2010-039. 2010
5. Relationship to Existing Body of Knowledge
Artificial neural networks (ANNs), a branch of learning algorithms inspired by biological
neural networks has being successfully applied across an wide range of problem domains.
Recent research activities in forecasting suggest that ANNs can be used to model extremely
complex engineering scenarios involving a large number of variables. The national bridge
inventory (NBI) database presents a complexity to perform prediction models due to missing
historical data and subjectivity from visual inspections. Lee, Sanmugarasa, Blumenstein, and
Loo (2008) developed an ANN prediction based on backward prediction model to generate
unavailable historical bridge condition ratings. Based on this earlier research, Lee, Guan,
Loo, and Blumenstein (2014) enhanced the model by applying an Elman neural network
(ENN) for learning historical deterioration patterns and predicting long-term performance. .
Creary, Fang and Jamalipour (2012) carried out a data mining of about 10,000 inspection
records from more than 5,000 bridges in Connecticut during a 3-year time span of 2006 to
2009. Experimental results indicate that the combined model can be an effective way to
improve forecasting accuracy achieved by either of the models used separately. In a
summary, ANNs is a sophisticated data modeling technique particularly suitable for large
empirical data sets where no theoretical guidance is available to suggest an appropriate data
generating process such as in bridge condition prediction. The early research demonstrated
that ANNs has a great potential for accurately predicting the future condition ratings of
bridges in the short-term and long-term planning process. Predication of future bridge
condition can tremendously help agencies to manage their assets and their future activities
including programing their future funding needs.
6. List of Anticipated Work Tasks
 Task 1: Collect and Contextualize Data
 Task 2: Data Cleansing and Statistical Analysis
 Task 3: Artificial Neural Networks Modeling
 Task 6: Final Report
 Task 5: Practical Tool Development
 Task 4: Interim Report and Presentation
7. Estimate of Funds Needed
$70,000
8. Estimate of Time Needed to Complete the Research
12 months
9. Name, Affiliation and Contact Information of Submitter(s)
Alireza jamalipour, former senior Research Scientist and current senior Bridge Consultant
Design, Connecticut Department of Transportation, 2800 Berlin Turnpike, Newington,
Connecticut 06111, e-mail: alireza.jamalipour@ct.gov; submitted as a member of state
department of transportation.
10. Date of Submittal
April 1, 2015