Engineering Term of the Month “ Infrastructure Asset Management ”
“ Infrastructure asset management is the science and prac ce of driving the maximum value and levels of service from physical infrastructure systems ( roads , bridges , dams , etc .). This is not limited to regular maintenance and expands to assessing actual condi ons , predic ng future deteriora on , es ma ng the risks of failure or reduced levels of service , considering the impacts on sustainability , and using financial / life-cycle analysis tools to allocate budgets 1 .” ( Piryonesi , 2019 ).
According to the 2021 ASCE Infrastructure Report Card , there are more than 617,000 bridges across the Unites States , with 42 % being at least 50 years old and 7.5 % considered to be structurally deficient or in “ poor ” condi on . There is increasing demand within the industry for the na on to implement a systema c program for bridge preserva on that priori zes exis ng deteriora on and preven ve maintenance .
In addi on to deteriora ng bridge infrastructure , there are currently 30,000 miles of inventoried levees located in the United States , with an addi onal 10,000 miles of levees with unknown loca ons or condi ons . There are roughly 91,000 dams opera ng in the country , with about half being privately owned as of 2019 . Over the last 20 years the number of high-hazard-poten al dams has more than doubled due to new project developments advancing upon these once-rural dams and reservoirs 2 .
While these figures may seem shocking , there are immediate ac ons that can be taken with regard to infrastructure asset management in order to gain more visibility into asset performance while also mi ga ng risks to the surrounding communi es .
Historically , asset managers have manually inspected and monitored assets when and where there is par cular concern , but the established tools available were labor-intensive and o en ineffec ve in providing useful informa on regarding early warning trends within the data . Technological developments over the last decade have now provided asset managers with the ability to install and remotely monitor various types of instrumenta on , such as piezometers , strain gauges , crackmeters , ltmeters , accelerometers , inclinometers , extensometers , and load cells , via cloud-hosted so ware . This enables the automa c tracking of key asset performance parameters and provides the informa on needed to respond to issues accordingly when compared with infrequent manual measurements .
Following the ini al installa on , the data from these various sensors ( paired with wireless datalogging technology ) are able to provide near-real- me informa on to monitoring engineers , all without them stepping foot on-site . This func onality dras cally reduces the me commitment and costs associated with sending a field engineer on-site to take manual measurements , especially in high-hazard and remote loca ons .
The more visibility and granular suppor ng data that is available to engineers , the be er they are able to priori ze and manage asset maintenance ac vi es and performance , while also gaining insight into assetrelated risk to surrounding communi es .
Piryonesi , S . M . ( 2019 ). The Applica on of Data Analy cs to Asset Management : Deteriora on and Climate Change Adapta on in Ontario Roads ( Doctoral disserta on )
ASCE ’ s 2021 American Infrastructure Report Card : GPA : C- . ASCE ’ s 2021 Infrastructure Report Card . ( 2022 , August 22 ). Retrieved September 2022 , from h ps :// infrastructurereportcard . org /