McC BarsMcCormick School of Engineering HomeMcCormick School of Engineering HomeNorthwestern University Homepage

Infrastructure Technology Institute

Data for Infrastructure Management


The Transportation Research Board just released “How We Travel: A Sustainable National Program for Travel Data1,” a policy study that assesses the state of Americans’ travel data and recommends an integrated national program designed to meet the evolving needs of transportation decision making. This report responds to the erosion of resource support for, and extent of, national travel data – measures of flows of freight and passengers, as well as transportation system performance and condition across the nation and for all modes. The recommendations call for increased investment in data programs – only a modest amount is needed – to assure that comprehensive, good quality data are available to support critical decisions about transportation infrastructure investments, policies, and system management.

Data are an often-invisible resource that (should) underlie major transportation infrastructure choices. While decision makers may use analyses, reports and advice to guide their choices, few recognize the source of the underlying data, or who paid for them. As a consequence, it is often difficult to secure the resources necessary to underwrite critical data programs. This is particular true when resources are tight and decision makers at all levels are trying to do more – or the same – with less. Arguably, when times are tough, the need for good data increases, because the investment choices become more difficult and more important.

We have argued that data are an asset of a transportation system, just as bridges, pavements, and rolling stock are assets2. Data are part of the value of a transportation system because they guide management and decision making. In the absence of data, a good transportation system can wander off track: its performance can deteriorate, and its ability to fulfill its mission can decline.

The value of data is determined by its use – and sometimes its non-use. That value comes from better decisions, choices of more cost-effective pavement, or safer bridge designs, and from avoiding poor decisions – infrastructure investments that do not return enough to society because they are under- (or over) utilized, their durability is less than required, or because their lack of resilience leaves us by the side of the road when floods or snowstorms hit.

Data that track the performance of transportation infrastructure over time have special value. They reveal trends in performance, telling us the direction we’re headed and sometimes signaling – warning us – what the future may be. Long-term data streams allow us to assess the effectiveness of programs, facilities, or technologies, e.g., ridership effects of transit service improvements or the corrosion resistance of new steels. Tracking multiple measures, particularly inputs and outputs or stimuli and responses – such as loads on bridges and strains or deflections, allows us to build analytic and predictive relationships, or to verify theories used in design.

The work of the Infrastructure Technology Institute is substantially built on collecting and analyzing data to learn how specific infrastructure systems are performing, and to build or extend our capabilities to predict that performance over time.

Some important examples illustrate the roles of data in infrastructure evaluation, learning and management.

• Prof. Zdenek Bazant studies long-term deflection in large concrete structures, looking for evidence of the size effect which can lead to very large deflections that threaten and even destroy the integrity of these structures, particularly large, post-tensioned concrete box girder bridges, a design that has become increasingly common. These deflections arise after many (but not that many) years - 20-30 years, a period that is well within the design life of these structures. This risk may be a serious threat to highway infrastructure, one that calls for important changes in design codes. Beyond theory and laboratory experiments, which are integral parts of Bazant’s research program, the institutional process of effecting changes in design codes requires convincing field - evidence of the need for those changes.

Such research requires large data sets that cover many bridges over extended time periods. Prof. Bazant has assembled deflection data on over 60 bridges from around the world to support his research. This meticulous task relied on personal and professional contacts and persistence, and it was facilitated by the fact that some agencies and individuals archived their deflection data – saved it for future use and made it available for research. This does not always happen, particularly in cases of structural failure, where litigation and settlement often lead to sealing records that could be invaluable for research that may someday protect public safety.

• Capturing and integrating multiple data streams can provide a basis for understanding the complex responses of infrastructure to loadings, weather, and aging. Working with the Wisconsin Department of Transportation (WisDOT), the ITI Research Engineering Group (REG) has been measuring strains, displacements, accelerations, and temperatures on a bridge near Hurley, Wisconsin, that carries heavy logging trucks. WisDOT installed a weigh-in-motion system next to this bridge to measure vehicle loadings. The result is a multidimensional data set that captures inputs (loads and temperature variations) and structural responses (strains and displacements). Clinical Associate Professor David Corr is using these data to analyze and model the effects of these inputs on bridge condition. Associate Professor Pablo Durango-Cohen is using long-term data from the Hurley bridge to build statistical process control models that may lead to algorithms that autonomously detect significant changes in bridge performance and provide early warning of potential failures.

• The ITI REG – Dan Marron, Dave Kosnik, Mat Kotowsky, and Brian Quezada - is working with the California Department of Transportation (Caltrans) to build and analyze a large data set containing tilt sensor records from scour-critical bridges across California. Scour, the process by which bridge piers and abutments are undermined by flowing water, is the leading cause of bridge failures in the United States.

More than a decade ago, ITI partnered with Caltrans to develop a simple and reliable method to measure the response of bridge structures to scour. This method uses precision tilt sensors to measure small changes in the orientation of bridge piers as scour holes develop around and under them. Caltrans subsequently adopted this simple empirical method to monitor scour-critical bridges statewide.

Caltrans has collected a large data set, but to make those data useful for research and program management requires organizing and converting them into systematic information about past and current structural response during periods of high water. ITI is now collaborating with Caltrans to build an Internet-enabled data management system that will support automated analysis of the long-term performance of selected bridges under various stream flow conditions, comparative assessment of the scour responses of multiple California bridges, and innovative ways of visualizing bridge scour performance to support informed strategic management. The data warehouse will support research at both Northwestern and Caltrans, and at the same time it will become a management tool for Caltrans.

In this project, ITI is working with the owner agency to make effective use of an existing database, taking it the next steps to archive (preserve), organize, analyze, and interpret the data, thus converting them into information that is useful for both long-term learning (research) and asset management.

Long-term, multi-dimensional data sets describing infrastructure condition and performance can be key assets for managing transportation infrastructure. They can support research to understand performance trends and the factors affecting them; they can track transportation assets to support strategic management; and they provide early warnings of emergent problems needing attention and remediation. These three applications illustrate an important principle of data program management – collect it once and use it many times – a strategy that maximizes the cost-effectiveness of data collection efforts.

To support informed decision making about transportation infrastructure, it is important that we assure that we have the right data in hand when we need them – and that we don’t spend money needlessly on data. These steps are important for assuring that we meet the data needs for infrastructure evaluation, management, and research:

• Structure the data program to support the goals of the transportation system or agency; collect the data that we need to protect our investments and to make wise decisions about them.

• Organize and archive the data to preserve long term data streams essential for research and understanding.

• Collect data on all of the factors of importance: the inputs and impacts on facilities; the response of the infrastructure itself; and the consequences of that response, including the costs of disruptions when they occur.

• Organize data to facilitate easy access and retrieval to encourage convenient and appropriate use.
• Use the data for learning as well as tracking – before-after studies, long-term monitoring, and analysis to understand and model relationships and to track changes in them;

• Review data programs periodically to be sure that what is needed is gathered and archived, and what is gathered is used.
• Share data appropriately. Providing liberal access to data encourages research and learning, grows new ideas, and ultimately benefits transportation in cost-effective ways.

Data are the fuel that supports transportation management, decision making, and policy setting. They energize research that identifies and solves problems and leads to better ways to manage transportation systems. For these reasons it is essential to ensure the quality and sustainability of transportation data programs.