The sameness of the process of maintenance and rebuilding might not be worrisome were it not for the immense backlog of deferred maintenance that has left big questions about the safety of bridges and the process for prioritizing repairs and replacements.
Absent adequate funding, risk assessment becomes central to prioritizing projects. The next few decades will require countless choices that are only now beginning to be understood. It is in this arena that technology is offering promising new ways to better understand our geriatric bridge inventory.
The collapse in August 2007 of the I-35W bridge in Minneapolis was a dramatic reminder of the deteriorating condition of the nation's transportation infrastructure. Following the collapse, state departments of transportation, local agencies and the U.S. Department of Transportation intensified the inspection of bridges and upped the estimates of the costly backlog that had been accumulating over decades.
The cost estimates were daunting: According to a 2009 estimate by the American Society of Civil Engineers, it would cost $17 billion a year, for 50 years, to eliminate all bridge deficiencies.
While many states rapidly reprioritized funding toward high-risk bridges, and others appropriated significant down payments toward their backlog, no public outcry propelled a major long-term commitment to address the problem. And transportation funding in the aftermath was not tightly targeted: the U.S. Public Interest Research Group reported that only 11 percent of the 2008 transportation funding went toward bridge repair and maintenance.
How well we perform risk assessments will determine how well we deal with the infrastructure crisis.
The conventional bridge safety assessment involves inspections, load ratings and overall safety assessments that must err on the side of safety. It is here that technology is beginning to offer supports to civil engineering that may help public officials make more safety-informed decisions that stretch public dollars across multiple demands.
A recent case study about a bridge in Monmouth County, N.J., illustrates how evolving bridge sensor technology now offers tools to far more accurately assess the structural health of existing bridges, and enables public officials to make more informed safety decisions about bridge capacity.
In effect, bridge sensor technology is a multistep process that begins with the creation of a 3-D structural model which takes into account the original design, and the accumulated modifications, repairs and upgrades made to the bridge over time, together with the areas of deterioration. Then strain sensors installed on critical structural members further refine the model. When the calibration is complete and the model confirmed as "true" to the real bridge, sequential tests with different types of loads provide data to tell engineers what the prognosis is with far greater accuracy.
In the Monmouth County example, the extra engineering costs to assess risk enabled the county to avoid closure or weight limitations that would have prohibited its use by trucks and emergency vehicles during the five- to seven-year period a replacement bridge was being constructed.
In this instance, relatively minor rehabilitation identified through the model analytics bought Monmouth County time. They could allow continued use of the bridge, confident in its safety.
In other applications, this new complex technology has pinpointed unexpected weaknesses as well as determined that far less work was necessary to make a bridge safe.
How might this new technology-based engineering tool inform the decisions to be made over the next decades? One answer lies in a $900,000 National Science Foundation grant to Drexel University to work with the Burlington County Bridge Commission in New Jersey. Under the grant, the Tacony-Palmyra Bridge has been equipped with monitoring instrumentation and other smart technology, and has become a live laboratory for engineering students from colleges and universities across the nation.
Ultimately, greater knowledge of how bridges are aging will not lower by much the final cost of restoring our infrastructure. The more interesting question will be whether better information will be productively used to drive decision-making in a highly-politicized resource allocation environment.
